Every NBA official since 2003-04 and how the game goes when they're on the floor. Click a column heading to sort.
Pick an official. Every team they've worked, how that team did with them, and how the whistle ran against that team's usual.
Pick a team. Every official who has worked 30+ of their games, and how the team did with each one.Pick a coach. Every official who has worked 20+ of their games, across every team they've coached, and how those teams did with each one.
Next game. Put in who you are, who you're playing, where, and the crew. What comes back is what the ledger says to prepare for, in order of how sure the numbers are.
The portal opens on your roster every time after this. You can switch programs any time from the Program box.
Every number in the portal rests on the choices below. This is the one place they're all written down; the tooltips and the Explain sheet say the same things in context. Where something is my judgment rather than something the data decided, I say so. Figures as of September 26, 2026.
SHA-256 of the files the ratings and 2026-27 projections come from, recorded before the 2026-27 season. The files themselves are on this site (links below, and a SHA256SUMS.txt beside them), so anyone can download one and recompute its fingerprint. Moving or renaming a file leaves the fingerprint alone; changing one character changes it completely.
Changed on September 25, 2026, evening, before any 2026-27 game: the rating no longer has a role adjustment (section 3), and all five files were rebuilt and fingerprinted again. This is a real change to the ratings and projections, not a note. Earlier the same day only two descriptive notes changed (fingerprints 05738f12…, 34eefe5a…, e16cdcbb…, with the CSVs untouched); the version before that was 4283f972…, 853c8fb3…, 7609d1e1…, c7aa5dbc…, c72e4136…. The look log of every scoring of 2025-26 is inside the settings files. These fingerprints are the ones the 2026-27 season will be scored against. Later that evening I added a sixth file, the scoring protocol, and fingerprinted it too; the five model files didn't change.
869058ea54d81f0162674f5c910ac7039dad3b30056324348e3025212d6a753cf6ca9523bbe8a7ee2192f10bf66deb8d0322a334fdaade689c43317ddb23c0eff2293df1bf4ec88ec70fe1f1b8f6872ac806dc461d23f562387052df7e726ab88240099908c2dc79ca26a03275ae2a279e19227f2f396a78372b3e406294dbd76318bb7901b334560815c8118d7822200c01a3deced55e42e4abbf9bb3e05ac94bae20835f187b1787c49dfe12bbb81d9bc0c9622ed6c2acf447ef156ec4f528A roster and money planner for college basketball. It rates every Division I player from the box score, turns that rating into dollars four ways (What I can afford, Typical-budget price, Conference rate, Market value), and tracks what you have committed, pledged and still need to raise.
Market value: what the player would go for in a draft of every player in Division I. Every school starts with its budget (counted at 1.1x for money that goes unreported) and 15 roster spots. Players go in order, best first. Each school bids what the player is worth to it: its own budget, its own rate per point, and the minutes it would give his rating. A school can only bid what it has left, so a rich school's money is spent on the players it takes, not quoted as everyone's price. At a player's turn, the schools that still have the money and a roster spot make their offers, and his market value is the fifth-highest of those offers, so no single school sets it. Taking the top offer instead would raise prices only about 4% on average, because schools near the top of a player's list offer close to the same amount; the fifth also leaves more money in the market for the players picked later (82 rotation players find no school with money left, against 366 if every player took his top offer). The very best players also collect a premium up to their single best bid, fading by rank: #1 gets the whole gap, #10 keeps 40% of it, #30 about 5%, and the top ten can push a school past its money, as stars do. The five schools named in the panel are the five highest offers at his turn: where he could go, not who is bidding against me. Men add up to about $1.08B against $1.18B of budgets, and the 17 reported star deals come out at about 0.85 of the deal (0.81 without Boogie Fland's $2M, which is a 2025-26 floor). It doesn't depend on my budget or his current pay.
What I can afford: what he's worth to me at my budget, at the minutes a program like mine gives a player of his rating. It's the most I'd pay, not what I expect to pay.
Target pay: what I've decided to pay him (I type it in).
Their assumed pay: what his 2026-27 school can afford for him, worked out the same way as What I can afford: that school's budget split across its listed roster by value, so a stacked roster gives each star a smaller share. A reported deal shows beside it. It is not market value; that has its own column.
Conference rate: what his own conference typically pays for that production.
Raise room: what I can afford minus what his school can afford for him. Positive means I could offer more than his school's money gives him and still pay no more than he's worth to me. It says nothing about whether he'd move.
Worth vs pay: market value divided by what he's paid now. Above 1x the market would pay him more than he gets: underpaid, and a player who may be looking.
Savings vs market value (Cap sheet): market value minus my target pay. Positive means I'm paying him less than the open market would.
Typical-budget price (Cap sheet, under Columns): the same production priced on a middle-of-the-pack high-major budget. It's a reference point, not what he'd get, because nobody sells to the middle bidder.
Because that's how a player's market works. A player at Iowa State doesn't care what I would pay; she's going to use the offers from the schools that can pay her the most. So her market value is what the market clears at: every program bids on its own budget, the best players are placed first, and each budget is spent once, so the same rich school can't be the top bidder for two hundred players.
It isn't a blank check. A school gives a player the minutes her rating earns there, so a role player's market value stays a role player's number, and production from a smaller conference is discounted a level up. When no program has money left by a player's turn, the panel says so and no underpaid or overpaid call is made.
It's one chain, and each step uses the one before it.
1. Box score. Every Division I game from last season: points, shots, free throws, turnovers, rebounds, assists, steals and blocks.
2. Rating (RVBR). The box score turned into points per 100 possessions better or worse than an average rotation player, with every game adjusted for the opponent. 0 is average. On top of that I add the part of the team's margin the box scores don't explain that follows a player when he moves: about 0.88 of it for men, about a quarter for women.
3. Next season's rating. Last season's rating pulled toward the average for the player's tier (harder when he played few minutes), with the season before counted at three-quarters weight, a step for his class, and the team context at his 2026-27 school. Freshmen have no line, so they're priced by recruiting stars instead.
4. Value. Next season's rating minus replacement level (the rating a program can find for free), times his share of the game's minutes, over five spots. Typical-budget price and Market value use the high-major replacement level. What I can afford, Their assumed pay and Conference rate use the level for the tier of the program doing the paying. Production from a lower tier is marked down when it's priced a tier up: mid-major to high-major 0.75, low to mid 0.6, low to high 0.45.
5. Dollars, one curve. Every price is a budget times a rate for the tier times his value to the power 1.5 for men (1 for women). It's a straight line up to 20% of the budget, then it bends and only creeps toward 35%, so the best players still separate. A rated player never goes below half a percent of the budget, and a man is never priced above $7M.
6. Four prices off that curve. What I can afford: your budget, at your own program's rate. Typical-budget price: the standard high-major budget ($11.41M men, $900k women), so it's the same at every school. Conference rate: the median budget in his conference. Market value: what the market clears at, explained under "What do the money columns mean" (every program bids on its own budget, best players go first, each budget is spent once).
7. On Scouting, two more. Their assumed pay: the curve at his 2026-27 school's budget, at that school's own rate. Raise room: What I can afford minus Their assumed pay.
8. Savings vs market value, on the Cap sheet only: market value minus the target pay you've entered, blank until you enter one. In Scouting, use Worth vs pay instead: market value over what he's paid now.
Years left doesn't change any price. A senior and a sophomore with the same projection are priced the same.
An example, with you at Michigan (reported budget $12.5M). Delrecco Gillespie played for Kent State last season and rated 8.2 over 1,094 minutes, after 21.8 over 791 minutes the season before. He's on Houston's 2026-27 roster. His projection there is 25.0 at 69% of the game, about 28 minutes: the two seasons blended and pulled toward the average (+3.2), a class step (+1.2), Houston's team context (+5.2), Houston's schedule against Kent State's (+3.8), and the high-major level term (+3.4). The line under his projection shows that build-up.
His value on the high-major basis is 25.0 plus 0.3 (high-major replacement is -0.3), times 69%, over five: 3.49 points. Kent State is a mid-major, so that's marked down to 0.75 of it: 2.62 points.
Typical-budget price is the curve at the standard $11.41M: $2.48M, about 22% of the standard budget. That's past the 20% knee, so the curve has started to bend. That 22% is his Roster share.
Their assumed pay is the curve at Houston's reported $11.5M, at Houston's own rate. Houston's listed roster is strong, so each point is worth less there than at a typical high-major, and he comes out at $1.75M. The Big 12's median budget is $11.5M, a little above the standard, so his conference rate is a little above Typical-budget price: $2.50M.
What I can afford at Michigan is $1.63M: his projection moved to Michigan (Michigan's team context and schedule in place of Houston's), at the minutes a program like mine gives a player of his rating (60% of the game, not 69%). Michigan's listed roster is strong too, so its rate per point is under the typical high-major's. Raise room is $1.63M minus $1.75M, about -$120k: Houston likely pays him more than he's worth at Michigan's budget.
Market value is what the market clears at. Every program priced him on its own budget and rate; the market was cleared best player first, each budget spent once; by his turn the five highest offers were SMU ($2.13M), Virginia Tech ($2.13M), Clemson ($2.07M), Nebraska ($2.06M) and Villanova ($2.05M). He goes well past the 30th player taken, so there's no star premium, and market value is the fifth of those: $2.05M. The small print says usually $1.16M to $2.70M (the middle half of outcomes, scaled on Villanova's curve, the bid that set the price); at his rating minus and plus one standard error it runs from $802k to $2.88M, in the panel. His Worth vs pay is $2.05M over $1.75M: 1.2x, about right (the low end of the range isn't above his pay, so no underpaid call).
An example, with you at Michigan (estimated budget $900k). Aaliyah Crump played for Texas last season as a freshman and rated 29.4 over 419 minutes. She's on Duke's 2026-27 roster. Her projection there is 36.7 at 36% of the game, about 15 minutes: last season's line pulled toward the average for a small sample, a class step (+1.9), Duke's team context (+0.7) and the high-major level term (+5.8). The line under her projection shows that build-up.
Her value on the high-major basis is 36.7 minus 2.7 (high-major replacement is +2.7), times 36%, over five: 2.48 points. Texas is a high-major, so no markdown.
Typical-budget price is the curve at $900k, the standard women's high-major budget: $134k, about 15% of the standard budget, on the straight part of the curve. That 15% is her Roster share.
Their assumed pay is the curve at Duke's estimated $660k, at Duke's own rate. Duke's listed roster is strong, so each point is worth less there, and she comes out at $51.7k. The ACC's median budget is $900k, the standard, so her conference rate is the same as Typical-budget price, $134k.
What I can afford at Michigan is $68.8k, on her projection moved to Michigan. Raise room is $68.8k minus $51.7k, about +$17k: room to bid.
Market value is what the market clears at. By her turn the five highest offers were Kansas State ($105k), Villanova ($104k), Tennessee ($103k), Cal ($103k) and George Washington ($102k), so market value is the fifth of those, $102k; the small print says usually $83k to $111k. Her Worth vs pay is $102k over $51.7k: 2.0x, underpaid, and a player other schools will call.
Where the bend shows: Tyler Tanner (Vanderbilt) has 7.35 points of value. On a straight line that would be past the ceiling, but the typical-budget price is $3.84M, 34% of the standard budget.
Where the bend shows: Sarah Strong (UConn) has 14.36 points of value. On a straight line that would be past the ceiling, but the typical-budget price is $289k, 32% of the standard budget.
Points per 100 possessions better or worse than an average rotation player, so 0 is average. A point scored counts as a point. Every shot and turnover costs what a possession was worth that season, so an average shot adds nothing and a good one adds. Rebounds, steals, blocks and assists are weighted by how much they moved team results across all of Division I. Every game is adjusted for the opponent, so a big night against a bad defense counts less than the same night against a good one.
Because 20 points on 22 shots against a weak schedule isn't the same as 20 points on 14 shots in the SEC. The rating charges for the shots and credits the schedule.
Some, and more for men than for women. The box scores never explain all of a team's margin. I measured how much of that leftover follows a player when he transfers: for men about 0.88 of it, for women about a quarter (0.23). That's the share I add.
It shows as the "Team context" row in the math, shared across the roster. It can be negative on a team that did worse than its box scores say. On a strong men's team it can be a big part of a player's rating.
What that production is worth at a standard high-major program: the pricing curve at the standard high-major budget ($11.41M men, $900k women), on the high-major basis. It's the same at any school for the same projected minutes.
A rated player gets at least half a percent of that budget ($57k men, $4.5k women). The top bends toward 35% (just under $4M for men, $315k for women) without quite reaching it.
Jeremy Fears Jr. is at $3.88M and Tyler Tanner at $3.84M.
Sarah Strong is at $289k.
A player whose 2025-26 team was a mid- or low-major has his production marked down before it's priced (0.75 from a mid-major, 0.45 from a low-major).
It's a price for production, not a contract. A top program pays more, which is what "Market value" is for. In the room, quote What I can afford, not Typical-budget price.
What I'd pay for that production at my budget: the pricing curve at your budget, at your own program's rate per point.
That rate is set once, from your program's listed 2026-27 roster, so the listed roster starts at 100% of your budget. It's never more than 1.6 times the typical rate for your tier, so a thin roster can't make its role players look like stars.
After that, adding or cutting a player moves the over or under line in the money table, never another player's price.
Every rated player gets at least half a percent of the budget. A walk-on or a returner who barely played is $0 unless you type a share for him. Freshmen are priced by their recruiting stars.
The money table also shows Projected payroll: your target pay where you've entered it, else a firm reported deal for that player, else What I can afford, plus the open spots. A reported figure that's only a floor ("or more") isn't used there.
The pay boxes hint at what I can afford for each player, split into revenue share and NIL. Scouting shows the same number for anyone on your sheet.
The same production priced on the curve at the median budget in the player's own conference, at that conference's tier, so a Missouri Valley starter is priced against Missouri Valley money.
It's a typical-budget price for that league, not the player's actual pay.
In this order. A reported payroll, when the press or a public filing has one (31 men's programs, Duke, Kentucky, VCU, TCU (an approximate figure) and the Louisiana public schools among them, and 9 women's, North Carolina, Georgia, LSU and Kansas State among them). The line under the program name names the source.
If the program hasn't reported a payroll, a reported deal on its roster sets a floor under the estimate below: 2.86 times the deal, so that deal is 35% of the budget. A disputed deal doesn't set a floor.
Otherwise it starts from The Sideline's estimated roster value. For a men's high-major I run it through a line fitted to the 19 high-majors with both a reported payroll and a Sideline figure, which scales small figures up more than big ones. It's never put above a conference rival's reported payroll when that rival has an equal or bigger Sideline figure, and never above $12.5M, the median reported high-major payroll.
Men's mid- and low-majors start from The Sideline's figure and are scaled by what the public filings say school pay is against it. Louisiana's state system, Kennesaw State, Wyoming, UTEP, Hawaii and NC Central all put school pay at about half the Sideline figure for programs the Sideline lists at $1M to $2M; the two smallest Louisiana programs paid more than their figure; and the programs that spend above their figure (VCU, Memphis, Utah State) have reported payrolls, which are used as reported. So the figure is used as given at $400k and below, halved at $1M and above, with a straight line between. Collective money on top of school pay isn't public at this level, so treat any one mid-major's budget as rough.
A program that opted out of revenue sharing for 2026-27 (the Patriot League, Central Connecticut, Stonehill, Wagner, Charleston Southern, Gardner-Webb, Presbyterian and Houston Christian, per the College Sports Commission list) pays nothing, so it gets a placeholder and a note, whatever The Sideline says its roster is worth.
Women's budgets are school pay estimated from The Sideline, which counts brand money too, so I scale it down. Every disclosed power-conference figure sits between $410k (Texas Tech) and about $1.07M (LSU), with Georgia, Kansas State and Utah near $1M, so a high-major at a $1.5M Sideline figure gets $900k, bigger or smaller figures scale less than one for one, and nothing goes above $1.07M, the highest disclosed figure. A mid-major gets half its figure, up to $500k. A low-major gets half, up to $150k.
A program with no Sideline figure is placed among its conference's figures by last season's adjusted margin, so a strong team gets a figure near the top of its league's range. It then goes through the same rules.
The line under the program name says which one you're looking at. Type your real budget over it, in the box at the top or in the Money to work with tile. Every rule is on the Assumptions and data sources sheet (link at the bottom of the portal).
Not rated: under 200 minutes and no earlier season to lean on. I price those players as placeholders until they play. A returner who played under 50 minutes for the same program is priced at $0 unless you type a share for him.
Under 400 minutes the rating leans on the average for that tier until the minutes build up, and the card says so. One season of shooting is noisy even at 1,000 minutes, so every player is pulled toward the average a little, the shooting part harder than the rest.
Yes, when they're ranked. A freshman in the top 100 of the 247Sports Composite (men) or the ESPN 100 (women) gets his level from the rank: 1 to 10 a top-10 price, 11 to 30 five stars, 31 to 100 four stars. Pro international signees get five stars. Everyone else is 3 stars. You can change any of them on the row.
The stars start from what last season's freshmen in that tier actually produced: 3 stars is the average, 4 stars the 75th percentile, 5 stars the 90th, and a top-10 price the 97th.
Then I cut that value, because agents and GMs see first-year players paid far less than that: to 0.35 of it for 1 to 3 stars and unstarred, 0.55 for four stars, 0.53 for five stars and 0.5 for a top-10 recruit.
Inside each band the rank matters too. The best-ranked recruit in the 1 to 10 or 11 to 30 band gets 1.15 times the band's value and the last one 0.85. In the 31 to 100 band it runs from 1.3 down to 0.7.
No. The eligibility table shows years left, but no price changes for it: a senior and a sophomore with the same projection are priced the same.
It's checked on how well it predicts a player's rating next season. It was fit on the 2014-15 to 2021-22 seasons, tuned on 2022-23 and 2023-24, and checked on 2024-25 and 2025-26. There it predicted next season's rating better than "same as last year" for men and women, transfers included, though not by a lot (men r 0.62 against 0.57, women 0.71 against 0.68).
That tells you the rating holds up from one season to the next. It says nothing about the dollars. The harder test is whether it predicts how good a team will be, which is the Torvik comparison below.
Those two seasons have been scored several times while the rating was being revised, so they're used validation, not a clean test. The clean test is 2026-27, and the files are fingerprinted on the Assumptions sheet so nothing can change before it's scored.
I tested that head to head. I took Bart Torvik's player BPM and PRPG! from barttorvik.com, built each team's next-season lineup from last season's numbers the same way I do with RVBR, and checked which one better predicted the team's adjusted efficiency margin. Men, 2023-24 through 2025-26, 1,090 team seasons: RVBR r 0.834, Torvik BPM 0.835, PRPG! 0.806, and "same as last year" 0.811.
That's a tie, and it's on my home field: 2023-24 was one of the seasons I tuned the rating on. So it isn't proof mine is better. The two ratings agree with each other at r 0.96, and a blend of the two gets 0.843.
I kept RVBR because it does as well on this test, I own it, it runs the same way for the women, and the site doesn't break if another site changes its data.
Defense beyond steals, blocks and boards, because no public college number has it. Injuries. Brand money (a player's outside NIL deals). Draft stock. Recruits outside the top-100 lists. What a rival is actually offering.
"Every point counts the same and every possession used costs what a possession was worth that season. Each game is adjusted for the opponent. Then I add only the part of the team's margin that travels with a player. So this is what the production is worth against every player in Division I, and here is what my budget can pay for it."
Two different questions. The rating predicts next season's rating better than "same as last year", by a modest margin, and ties Torvik's BPM on predicting team margin (both above).
The dollars can only be checked against the 17 rated men with a reported 2026-27 deal. Market value comes out at 0.85 times the deal on average (geometric mean; a bootstrap puts the 95% range at about 0.74 to 1.00). One of the 17, Boogie Fland's $2M, is a 2025-26 floor; without it the mean is 0.81, so the tool runs under the biggest deals. It's under the deal for 11 of them, over for 6, and within 25% for 8. Their assumed pay, the estimate at the player's own school, runs lower: 0.76 of the deal (all 17).
Those 17 are the biggest deals the press covers, and nine of them are floors (eight The Athletic's "$3.9M or more" and Juke Harris's "upward of $4M"), so I treat them as a check, not a target: every knob was set before them or on other evidence, and they are what a new deal gets compared with.
The ones far under are Milan Momcilovic (0.55: Kentucky pays for a proven scorer at the top of the market, more than the rating sees), Jackson Shelstad (0.62; The Athletic's "$3.9M or more", and he projects to about 10 minutes a game at Louisville), Moustapha Thiam, Samet Yigitoglu and Tounde Yessoufou (0.63 to 0.64; draft stock and brand value the rating can't see).
The ones over are Boogie Fland (1.88: his $2M is a 2025-26 floor, and no 2026-27 figure has been reported), Thomas Haugh (1.24), Flory Bidunga (1.19; reported at $5M to $6M, I use the $5M end) and JT Toppin (1.16).
Women have no reported deal that counts (both are disputed), so there's nothing to check the women's dollars against yet.
Where the press has reported a deal or a payroll, it's on the sheet next to the name or under the program name, so you can see where my number lands. A reported payroll replaces my budget estimate, and you can anchor a real deal to scale your prices to it.
Because the rating only sees production. A reported deal also pays for things it can't see: draft stock, a brand, a bidding war, or the player a coach needed that week.
The figures themselves are rough too. Eight of the 17 men's deals are The Athletic's "$3.9M or more", which is a floor, and others are "up to" or a range.
So a gap between What I can afford and a reported deal tells you what the market paid on top of the production. If you know the real number, type it as target pay, or anchor it.
A standard high-major budget: $11.41M for men and $900k for women. For men it's the median of the 79 high-major budgets as the page carries them, reported payrolls where there are some and my fitted estimate otherwise. For women it's the median of the high-major budgets after scaling The Sideline down to school pay.
Typical-budget price is the pricing curve at that budget.
Market value doesn't use a baseline budget: every program bids on its own, and the market clears (see "What do the money columns mean"). A men's price tops out at $7M, the most one player can take.
A player coming from a mid- or low-major gets no more minutes in any bid than that program gives players of his rating, and his production is cut to 0.75 (from a mid-major) or 0.45 (from a low-major) when a higher level prices it.
The small print under a Market value is the middle half of outcomes: the same price at his rating minus and plus two-thirds of a standard error (0.674, the quartiles), moved along the pricing curve of the program whose bid set his market value. The player panel also shows the wider band at one full standard error.
I don't, exactly, and neither does anyone outside the building. Where a payroll or a deal has been reported I use it and name the source.
Otherwise The Sideline publishes an estimated roster value per program (a market value, not a payroll). For men's high-majors I scale it with a line fitted to the 19 reported high-majors, which lifts small figures more than big ones, and I never put an unreported program above $12.5M, the median reported payroll.
Men's mid- and low-majors start from The Sideline's figure, scaled by the public filings (see "Where does the budget come from?"). Those single figures are noisy: some strong mid-majors land above what reported mid-major payrolls suggest, and some land below.
For women, The Sideline's figure counts brand money, so I scale it down to school pay. Nine women's payrolls have been reported, all between $86k and about $1.07M: Georgia, LSU, Kansas State, Utah, North Carolina and NC State near $1M, Texas Tech $410k, Hawaii $450k and Louisiana Tech $86k (all women's sports together).
When a program has no figure, it's placed among its conference's figures by last season's adjusted margin. The line under the budget says where each number came from.
Your own program: type the budget and every price uses it. Anchored deals then scale the curve on top of it, and the sheet warns if they price the roster above your budget.
It's his market value (what the schools that can pay the most would offer) divided by what he's paid now. 1.5x means the market would pay him half again what he gets: underpaid, and a player who may be looking. Below 1x means he's paid more than the market would pay. I call 1.15x or more underpaid and 0.87x or less overpaid (overpaid only against a reported deal or for a good starter or better), but only when the gap clears the pricing noise: even the low end of his wider market band (one full standard error either way, shown in the player panel, not the narrower 'usually' range in the table) has to be 1.15x his pay to call him underpaid (the high end 0.87x or less for overpaid), and the dollars have to differ by at least 1% of the standard high-major budget ($114k men, $9k women). A gap under that reads as about right, whatever the ratio, because a $75k market value over a $2k pay is 37x and means nothing. When the ratio is low but the overpaid call is held back (his pay is my estimate, not a reported deal, and he projects under a good starter), the column says so: pay is my estimate, no call. Above 3x the column just says 3x+. "Low-budget school" marks a school whose whole player budget is under 10% of a standard high-major's: the player isn't being shorted, the school simply can't pay. Those are the players to watch in the next portal window.
What he costs now is a firm reported deal where there is one. A deal reported as "or more" counts as at least that figure, "up to" as at most that, and a disputed figure is ignored. For everyone else it's my estimate of his pay at his 2026-27 school. Market value can come out below it, and then he shows as overpaid. Pick "Reported deals only" in the Worth vs pay filter to see the players whose real pay is public.
Players priced at my minimum show no Worth vs pay: a cheap player at the floor isn't a bargain.
Yes. School level in Scouting filters by the player's 2026-27 school (or last season's, if he isn't on a roster): high-major, mid-major, low-major, or mid- and low-major together. Pair it with Worth vs pay set to underpaid and the sort on Worth vs pay to find players at smaller schools the market would pay more than they get now.
On the Cap sheet: What I can afford divided by the target pay you typed. 1.5x means the plan pays two thirds of what the production is worth here; under 1.0x you are paying above it. It's blank until you enter a target pay.
On Scouting, Their assumed pay is what the player's 2026-27 school most likely pays him, priced at that school's budget and its own rate per point, so each school's listed roster adds up to its budget. A reported deal is shown beside it for reference, never used as the pay.
Raise room is What I can afford minus Their assumed pay, or minus the reported deal when one is reported, bigger, and not disputed. Above zero, I could give the player a raise and still pay no more than he's worth to me. Sort by Raise room for the most room first, or tick Only players I could give a raise.
Their assumed pay is an estimate from the school's budget, not his contract. Player by player it can miss by a lot (see "How do I know these valuations are right?"). It uses the minutes he projects to get at his current school.
The Cap sheet prices my listed roster at the minutes its depth chart gives each player: the projected minutes fill five spots, best players first. Scouting prices a player from elsewhere at the minutes a program like mine typically gives that rating, blended with his own, because I haven't decided his role yet. When I add him, the row keeps those minutes and his projection takes my program's level, team context and schedule terms in place of his school's, so the Add form and the row agree. Hover a rating to see last season's number and minutes.
Meeting view hides the money, for a screen you'd put in front of someone who shouldn't see your numbers. One click turns it on and one click turns it off.
It opens on the player with the biggest target pay. If you haven't planned any pay yet, it opens on the whole roster. The Show picker at the top switches to another player or to every player; every player puts everyone's target pay on screen, so use that only with your own staff.
What shows for each player on the Cap sheet: Player, Role, Class, Years left, Target pay (revenue share, NIL and total), and one line under the name with the projected rating, its tier and where it ranks in Division I and the conference. Scouting stays open with every dollar column hidden. The Dates calendar stays too.
Everything that's a dollar figure of mine is hidden: What I can afford, Typical-budget price, Conference rate, Market value, Roster value, Pledged, the budget and its source, the money tiles and table, the hints in empty pay boxes, portal risk, the player panels, and the Depth chart and Books screens. You can't switch programs while it's on.
A banner at the top says it's on, with a Turn off button. Turn it on before the agent sits down and check the screen once yourself.
A press figure for that player's 2026-27 deal, with the source on mouse-over. It's shown so you can see where my number lands against it. It's never used as the player's price. A firm figure does count in Projected payroll when you haven't entered target pay.
It floors the program's budget at 2.86 times the deal (so the deal is 35% of it), unless the program has a reported payroll or the deal is disputed.
Open the player and read the line under the projection. It's built from last season's line at its minutes, the season before (at three-quarters weight), a pull toward what players with those minutes average, a class step, the team context, a schedule step for a transfer (a quarter of the gap between the two programs' schedule credit), and a level term by the 2026-27 program's tier (high-major players outplay the class step by about 3.4 points for men and 5.8 for women, mid-majors by 1.3 and 2.4; fitted on 2015-22 and tested on 2023-26). A player in a fifth season or later takes the fourth-season class step; the fitted fifth-season step came from 17 old pairs and scored worse than no step. A player who played 300 minutes last season and 900 the season before is projected mostly from the bigger sample, so a small-sample dip doesn't sink them, and a small-sample spike doesn't launch them.
Where your plan's roster ranks in Division I next season. I add up every player's projected value above replacement and compare it with every other program's listed 2026-27 roster.
It uses your plan, so adding or cutting a player moves it, and it says how far that's moved you from your listed roster.
No. An anchored deal scales what I can afford curve: its pay over what the curve says for that player, blended with the plain curve by how much of a typical roster's value you've anchored, fully at a quarter. So What I can afford for every other player moves with it.
The budget stays what you typed. The money table shows what the roster prices out at, next to the budget, so you can see the gap.
There are two kinds of money, and different rules cover each.
Revenue share is the school's own money, paid under the House settlement at schools that opted into it. The cap covers every sport at the school combined: about $20.5 million in 2025-26 and about $21.6 million in 2026-27, and it goes up each year. There's no separate basketball cap, so how much of it goes to basketball is your athletic department's call.
NIL from outside companies, boosters and collectives isn't school money. The College Sports Commission enforces the settlement rules, and a player has to report any third-party NIL deal of $600 or more through NIL Go, the commission's clearinghouse. A deal from a booster, a collective or another group tied to the school gets a closer look: it has to have a valid business purpose, meaning he's paid for real promotional work at a normal rate for that work, not for playing or for picking the school.
That's why Books keeps the two apart. Money given to the NIL pool stays in the pool and isn't split among players. Money for a player is logged as an NIL deal between him and a payer, with its business purpose and its NIL Go status.
Rosters at those schools are limited to 15 in men's and women's basketball. Players a school designated when the limits came in (July 2025) don't count against it for the rest of their eligibility, wherever they play.
The revenue share and NIL split on this site is my default, not a rule: 60% revenue share at high-majors, 40% at mid-majors and 25% at low-majors, with revenue share topped at $4.2M for men and $1.4M for women. Type your own. I don't file anything with NIL Go or anyone else. Check every deal with your compliance office.
No. Under NCAA rules you can't contact a player at another Division I school until he's entered the transfer portal. In basketball the portal window is 15 days starting the day after the national championship game, plus a 15-day window after a head coaching change. Scouting is for planning and knowing the market, not for outreach.
Ratings and Typical-budget price: built from every box score of the 2025-26 season. That season is finished, so those numbers don't move until I rebuild after this season.
This season so far: the "Update all stats" button reads the games from ESPN when you press it (final games are kept in your browser), rates every player on the games played, and Reminders fills in from the same read during the season.
Rosters for 2026-27: each school's own roster page, read September 27, 2026 (336 men's and 343 women's programs); a program whose school hadn't posted one yet keeps its Wikipedia page or last season's list moved up a class, and the Cap sheet says which. The pull date is on the Data line below. Recruiting ranks: the 247Sports Composite (men) and the ESPN 100 (women), read September 25, 2026.
Budgets: The Sideline's roster values, read Sep 16 2026 (men) and re-read program by program Sep 24 2026 (women), plus a hand-kept list of reported payrolls and deals with their sources. Transfers: worked out from the rosters (who is on a different program than last season, who is on none); the portal itself isn't watched live yet.
Your budget (type it in the box at the top or in the Money to work with tile). Anyone missing from your roster: Add a player pulls them from the player pool with their whole line; a freshman who isn't listed yet is a placeholder with recruit stars.
Target pay, NIL pool money, NIL deals, flags, notes and deal terms are yours. They save as you type in this browser, and to your program's shared plan when you're signed in. If a roster looks stale, tell me: rosters are re-pulled by hand, next in October and again after the portal windows.
The Data line at the bottom of this sheet shows the season the ratings are built on, the roster pull date, and the budget pull date. Anything you typed shows the time it was saved in Your data.
Scouting shows the 2026-27 projection, because that's the number every price uses: last season's rating shrunk toward average for a small sample, blended with the season before, plus a step for class and one for the team. Hover a rating to see last season's number and minutes.
Set Scouting's Value from to This season and every player is re-rated on this season's games, blended with the preseason projection (the projection counts as 700 minutes of evidence for men and 450 for women, so the games take over as the minutes pile up). Market value and What I can afford move with that. His pay doesn't: it's what he signed for. So a player who slumps drops below his pay and shows as overpaid, and one who breaks out shows as underpaid.
I tested the rule on 2024-25 and 2025-26, splitting each season by date: pay locked at the preseason price, market value re-rated on the first half, then scored against the second half only. Of the players flagged overpaid at the midpoint, 55 to 58% were still overpaid over the rest of the season, against base rates of 34 to 44% if you guessed, and the midseason number was 20 to 26% closer than the preseason projection. Players flagged overpaid by season's end were more likely to drop a level for the next season (odds about 3.1 times for men and 4.8 for women, after allowing for class, school level and value). The projection counts as 700 minutes of evidence for men and 450 for women, the weights that predicted the rest of the season best.
Manual entries. Nothing here is pulled from a bank, a collective or NIL Go, and nothing here files anything for you. Money raised for the NIL pool stays in the pool: it isn't split among named players. A payment to a player is an NIL deal between the player and the payer, so log what he does for it and where it stands in NIL Go. Check every deal with your compliance office.
Every player across the years, grouped by role. Click a chip to cycle its status (none, red, orange, green). Right-click, or press and hold on a phone, to write a note. G, F and C mark the role.
Players you are watching this season, from any program. “Update stats: watch list” reads their games from ESPN and rates each one on the games played so far.
Players down the side, seasons across. A bar runs through the seasons a player is on the plan: green pay entered or under contract, orange on the roster with no pay, ember the last eligible season, grey played. Click a season to the right of the bar to extend the deal through it (or drag the bar’s end); click the last season of the bar to shorten it. That sets the length of the deal only; the money is entered in the Roster view, and a deal cannot run past the player’s eligibility. A status set in the roster map overrides the color.
Every Division I player with a 2025-26 line, with my rating (RVBR), its tier, roster share and market. Program is where the player was last season, with an arrow to the 2026-27 program when it changed; players on no roster I have pulled (graduated, drafted, or still in the portal) are hidden unless you switch Status; click a name to see the build-up, and star a player to watch them.
: every budget rule, pricing choice, rating setting and data source in one place.
Every NBA game since the 2003-04 season, every official on the floor, every box score. Referees, the teams and players in front of them, and where the numbers are flat.
Officials should be invisible. The best crew is the one nobody talks about after the game. The grade rewards sitting near the middle of the pack in every category at once, not a tight whistle or a loose one, a home lean or a road lean. I take every active official with 200+ games, find the median in each category, and measure how far each one sits from it in typical spreads (the median absolute deviation, scaled to match a standard deviation). Before measuring, each number is pulled part of the way toward the middle, by an amount set for that category: the more a category moves by luck from game to game, and the fewer games an official has, the further the pull. The distance is then measured in the spread of the raw numbers, so a category that is mostly luck leaves nearly everyone at an A there, and a noisy category can't hand out the same F as a steady one. One side effect is deliberate: an official with few games can't be shown yet to stand out, so newer officials sit closer to an A until their games say otherwise. Within half a spread of the median in a category is an A there; within one, a B; within one and a half, a C; within two, a D; further, an F. Consistency is the one category where only the wide side counts, and it has two parts: from one night to the next, how much the foul total bounces around after allowing for who was playing (overtime scaled back to a regulation game), and how often the free throws came out one-sided. Each part is measured in the same spreads as every other category, steadier than the median counts as zero, the category takes the worse of the two. All three officials on the floor share the numbers of a game, so read it as a crew habit until the crews are split. The overall grade is the average distance across all 6 categories. An A official is one whose numbers you couldn't tell from the pack. An F is one whose numbers you could.
Categories: Whistle (fouls per game vs the league in the same seasons), Home lean (home-minus-away fouls vs the league lean), Home results (home wins vs expected), Free-throw lean (home-minus-away free throws vs the league lean), Technicals (technicals per game vs the league in the same seasons), Consistency (night-to-night spread of the whistle around what the two teams usually commit, and lopsided free-throw nights; only the wide side counts, and the worse part sets the letter). 61 active officials graded; median values: Whistle +0.04, Home lean +0.04, Home results -0.39, Free-throw lean +0.08, Technicals +0.02, Consistency 0.00.
For every official with 300+ games I split the games into odd and even (by date), work out each category on both halves, and check how well one half predicts the other across officials. The first number is that correlation; the second projects it to a full career. Near 1 means the category describes the official; near 0 means it is luck. The rule: a category counts toward the grade only if the full-career number is at least 0.2 in the NBA or college game. Close games (home wins in games decided by three or fewer) and the half-to-half foul gap fail it and are shown here but left out of the grade. All three officials on the floor share each game, so even the categories that repeat describe a crew habit as much as a person.
| Category | Odd vs even | Full career | Officials | In the grade |
|---|---|---|---|---|
| Whistle | +0.79 | +0.88 | 94 | yes |
| Home lean | +0.30 | +0.46 | 94 | yes |
| Home results | +0.22 | +0.36 | 94 | yes |
| Free-throw lean | +0.23 | +0.38 | 94 | yes |
| Technicals | +0.52 | +0.69 | 94 | yes |
| Close games | +0.02 | +0.03 | 94 | no |
| Consistency: night spread | +0.22 | +0.35 | 94 | yes |
| Consistency: lopsided nights | +0.13 | +0.23 | 94 | yes |
| Consistency: half-to-half gap | -0.05 | -0.10 | 94 | no |
A 46 B 15 C 0 D 0 F 0 of 61 active officials
| Official | Games | Whistlerepeats 0.88 | Home leanrepeats 0.46 | Home resultsrepeats 0.36 | Free-throw leanrepeats 0.38 | Technicalsrepeats 0.69 | Consistencyrepeats -0.10 | Distance | Grade |
|---|---|---|---|---|---|---|---|---|---|
| Josh Tiven | 1081 | A | A | A | A | A | A | 0.09 | A |
| Nate Green | 261 | A | A | A | A | A | A | 0.13 | A |
| John Conley | 210 | B | A | A | A | A | A | 0.13 | A |
| Natalie Sago | 426 | A | A | A | A | A | A | 0.15 | A |
| Gediminas Petraitis | 695 | A | A | A | A | A | A | 0.16 | A |
| Scott Twardoski | 815 | A | A | A | A | A | A | 0.17 | A |
| Ray Acosta | 527 | A | A | A | A | A | A | 0.18 | A |
| Suyash Mehta | 257 | A | A | A | A | A | A | 0.19 | A |
| Tony Brothers | 1591 | A | A | A | B | A | A | 0.21 | A |
| Evan Scott | 294 | A | A | A | A | B | A | 0.21 | A |
| Andy Nagy | 340 | A | A | A | A | B | A | 0.22 | A |
| Matt Myers | 344 | B | A | A | A | A | A | 0.23 | A |
| Official | Games | Whistlerepeats 0.88 | Home leanrepeats 0.46 | Home resultsrepeats 0.36 | Free-throw leanrepeats 0.38 | Technicalsrepeats 0.69 | Consistencyrepeats -0.10 | Distance | Grade |
|---|---|---|---|---|---|---|---|---|---|
| James Capers | 1471 | A | C | B | B | B | A | 0.77 | B |
| Sean Corbin | 1266 | A | C | B | C | A | A | 0.71 | B |
| Mousa Dagher | 356 | B | B | A | B | B | A | 0.57 | B |
| J.T. Orr | 757 | A | B | A | C | C | A | 0.57 | B |
| James Williams | 919 | B | A | A | A | C | A | 0.56 | B |
| Brandon Adair | 361 | C | A | A | A | B | A | 0.55 | B |
| Kevin Cutler | 913 | A | D | A | B | B | A | 0.54 | B |
| Eric Dalen | 943 | D | B | A | A | B | A | 0.53 | B |
| Brent Barnaky | 973 | C | A | A | A | B | A | 0.53 | B |
| Brett Nansel | 444 | A | B | A | C | B | A | 0.51 | B |
| Michael Smith | 1360 | C | A | A | A | C | A | 0.51 | B |
| Courtney Kirkland | 1453 | A | B | A | B | C | A | 0.51 | B |
Every graded official: sortable in the Referees tab (Grade column). Distance is in median absolute deviations; 0 is dead center.
Total fouls called per game with the official on the floor, against the league average in the same seasons. This is the strongest signal in the data. It’s stable and it’s large, and it says nothing about fairness, only about how much contact gets called.
| Official | Games | Fouls/game | vs league | z |
|---|---|---|---|---|
| Mitchell Ervin | 664 | 41.40 | +1.76 | +6.4 |
| Eric Lewis | 1236 | 42.50 | +1.12 | +5.4 |
| Scott Foster | 1619 | 42.40 | +1.07 | +6.1 |
| CJ Washington | 454 | 40.30 | +0.99 | +3.1 |
| Tyler Ford | 709 | 40.70 | +0.97 | +3.8 |
| Mousa Dagher | 356 | 40.20 | +0.97 | +2.7 |
| Ben Taylor | 747 | 40.80 | +0.94 | +3.8 |
| Michael Smith | 1360 | 42.20 | +0.93 | +4.8 |
| Official | Games | Fouls/game | vs league | z |
|---|---|---|---|---|
| Brandon Adair | 361 | 38.10 | -1.28 | -3.9 |
| Eric Dalen | 943 | 38.90 | -1.25 | -5.4 |
| Marc Davis | 1623 | 40.00 | -1.20 | -6.7 |
| Aaron Smith | 498 | 38.60 | -1.05 | -3.4 |
| Brent Barnaky | 973 | 39.10 | -0.84 | -3.6 |
| Jenna Schroeder | 277 | 38.00 | -0.74 | -1.8 |
| John Butler | 355 | 38.30 | -0.68 | -1.8 |
| Ed Malloy | 1503 | 40.60 | -0.63 | -3.6 |
Home fouls minus away fouls, against the league's own lean in the same seasons (home teams get called for slightly fewer fouls everywhere). Negative means the official calls even fewer on the home team than the league does.
| Official | Games | Home fouls − away | vs league | z | Home FTA edge vs league |
|---|---|---|---|---|---|
| Kevin Cutler | 913 | -1.15 | -0.67 | -3.8 | +0.74 |
| Sean Corbin | 1266 | -1.15 | -0.53 | -3.8 | +1.15 |
| James Capers | 1471 | -1.13 | -0.49 | -3.6 | +0.74 |
| Jenna Schroeder | 277 | -1.13 | -0.90 | -3.1 | +0.86 |
| Dedric Taylor | 663 | -0.98 | -0.56 | -2.8 | +0.86 |
| Rodney Mott | 1319 | -0.89 | -0.24 | -1.7 | +0.22 |
| Ben Taylor | 747 | +0.07 | +0.50 | +2.8 | -0.54 |
| J.T. Orr | 757 | -0.00 | +0.46 | +2.5 | -0.92 |
| Brett Nansel | 444 | +0.19 | +0.52 | +2.3 | -1.53 |
| Mousa Dagher | 356 | +0.26 | +0.51 | +2.1 | -1.19 |
| Mark Lindsay | 1135 | -0.26 | +0.28 | +1.9 | -0.67 |
| Bill Kennedy | 1428 | -0.42 | +0.23 | +1.7 | -0.37 |
Technical fouls per game as ESPN logs them for both teams combined (this count includes defensive three-second calls, so it runs higher than the tally you see in league discipline reports). The veterans top the list.
| Official | Games | Techs/game |
|---|---|---|
| Michael Smith | 1360 | 1.59 |
| David Guthrie | 1217 | 1.56 |
| Bill Kennedy | 1428 | 1.53 |
| Rodney Mott | 1319 | 1.51 |
| Ed Malloy | 1503 | 1.50 |
| Eric Lewis | 1236 | 1.50 |
| Brian Forte | 1175 | 1.48 |
| James Williams | 919 | 1.47 |
Home-team win rate with the official on the floor, against the league's home-win rate in the same seasons and game type. Across 61 officials with 200+ games, chance alone would put about five beyond ±2 and almost none beyond ±3.
| Official | Games | Home win % | Expected | z |
|---|---|---|---|---|
| Phenizee Ransom | 375 | 49.3% | 55.5% | -2.4 |
| Zach Zarba | 1579 | 55.5% | 58.3% | -2.3 |
| CJ Washington | 454 | 51.8% | 56.1% | -1.9 |
| Mousa Dagher | 356 | 50.8% | 55.5% | -1.8 |
| Brent Barnaky | 973 | 54.6% | 57.2% | -1.7 |
| Marat Kogut | 941 | 54.7% | 57.3% | -1.6 |
| James Capers | 1471 | 62.7% | 58.7% | +3.2 |
| Sean Corbin | 1266 | 61.9% | 58.5% | +2.5 |
| Aaron Smith | 498 | 60.6% | 56.4% | +1.9 |
| Jonathan Sterling | 400 | 60.5% | 55.8% | +1.9 |
| Rodney Mott | 1319 | 61.0% | 58.7% | +1.7 |
| Jason Goldenberg | 418 | 60.0% | 55.9% | +1.7 |
Games where the matchup made one team at least a 70% favorite, judged from both teams' strength that season (scoring margins against who they played) and where the game was played. League-wide, underdogs won 19.9% of 12,818 such games, against 19.9% expected. Each official is measured against the upsets expected in that official's own games, not a league average. Officials with 150+ of them, most underdog wins first. 55 officials: 4 beyond ±2 (chance: about 2.5), 0 beyond ±3 (chance: about 0.1).
| Official | Games | Underdog wins | Expected | z |
|---|---|---|---|---|
| Phenizee Ransom | 175 | 26.9% | 19.2% | +2.6 |
| John Butler | 166 | 26.5% | 20.1% | +2.1 |
| J.B. DeRosa | 179 | 26.3% | 20.2% | +2.1 |
| Mousa Dagher | 158 | 25.9% | 20.0% | +1.9 |
| Derrick Collins | 618 | 22.5% | 19.9% | +1.6 |
| Tre Maddox | 368 | 22.3% | 19.2% | +1.5 |
| Marat Kogut | 406 | 22.7% | 19.7% | +1.5 |
| Mitchell Ervin | 283 | 23.0% | 19.8% | +1.4 |
| James Capers | 642 | 15.4% | 19.8% | -2.8 |
| Tony Brothers | 673 | 17.1% | 20.1% | -2.0 |
| Ed Malloy | 643 | 16.8% | 19.7% | -1.9 |
| Rodney Mott | 584 | 17.0% | 19.7% | -1.7 |
| Curtis Blair | 476 | 17.6% | 20.0% | -1.3 |
| J.T. Orr | 336 | 17.0% | 19.6% | -1.2 |
| Brent Barnaky | 449 | 17.6% | 19.7% | -1.1 |
| Matt Myers | 178 | 16.3% | 19.1% | -1.0 |
Home-team win rate in games decided by three points or fewer, officials with 400+ games. League-wide home teams win a little over half of these.
| Official | Games | Close games | Home wins in them |
|---|---|---|---|
| Kevin Cutler | 913 | 13.8% | 40.5% |
| Mitchell Ervin | 664 | 17.6% | 42.7% |
| Tyler Ford | 709 | 12.8% | 45.1% |
| Brian Forte | 1175 | 15.7% | 45.1% |
| Tre Maddox | 805 | 12.8% | 46.6% |
| Ben Taylor | 747 | 13.3% | 62.6% |
| Aaron Smith | 498 | 17.3% | 61.6% |
| Jonathan Sterling | 400 | 16.2% | 61.5% |
| Eric Dalen | 943 | 13.6% | 58.6% |
| J.B. DeRosa | 406 | 13.5% | 58.2% |
Playoff games where one team could close out the series. League-wide the trailing team survived 38.6% of 352 such games. Officials with 15+ clinch games, most extensions first.
| Official | Clinch games | Series extended | Rate | z |
|---|---|---|---|---|
| David Guthrie | 27 | 15 | 55.6% | +1.8 |
| Zach Zarba | 45 | 22 | 48.9% | +1.4 |
| Pat Fraher | 21 | 11 | 52.4% | +1.3 |
| James Williams | 23 | 11 | 47.8% | +0.9 |
| Ed Malloy | 31 | 14 | 45.2% | +0.8 |
| Sean Corbin | 22 | 9 | 40.9% | +0.2 |
| Brian Forte | 19 | 3 | 15.8% | -2.0 |
| James Capers | 39 | 13 | 33.3% | -0.7 |
| Marc Davis | 49 | 17 | 34.7% | -0.6 |
Every pairing with 30+ games. Expected wins come from the team's own record that season at that venue (home or away), so a good team with a good record is expected to win, and the question is only whether it won more or less than that with this official. This table does not account for the opponent; the Matchups tab does (both teams' records and the venue, game by game). 1,358 pairings; 49 beyond ±2 (chance: about 62), 4 beyond ±3 (chance: about 3.7). That’s the spread luck produces.
| Official | Team | Games | Wins | Win % | Expected | z | Foul diff vs team norm |
|---|---|---|---|---|---|---|---|
| Natalie Sago | NYK | 30 | 6 | 20.0% | 54.4% | -4.0 | -0.11 |
| Marat Kogut | TOR | 76 | 26 | 34.2% | 51.4% | -3.2 | +0.78 |
| Brian Forte | LAC | 77 | 29 | 37.7% | 54.8% | -3.2 | -0.10 |
| Eric Dalen | SAS | 68 | 30 | 44.1% | 61.1% | -3.1 | +0.06 |
| Marc Davis | BOS | 123 | 59 | 48.0% | 59.5% | -2.8 | +0.59 |
| Tony Brothers | LAL | 103 | 40 | 38.8% | 51.0% | -2.7 | +0.33 |
| Tyler Ford | BKN | 50 | 12 | 24.0% | 40.3% | -2.5 | -0.39 |
| Pat Fraher | LAL | 85 | 35 | 41.2% | 53.7% | -2.5 | +0.57 |
| Leon Wood | SEA | 31 | 8 | 25.8% | 47.3% | -2.5 | +1.57 |
| Rodney Mott | CHI | 84 | 28 | 33.3% | 46.1% | -2.5 | +0.29 |
| James Williams | MIN | 59 | 17 | 28.8% | 43.8% | -2.5 | +1.12 |
| Brent Barnaky | SAS | 64 | 30 | 46.9% | 60.0% | -2.4 | +0.55 |
| Official | Team | Games | Wins | Win % | Expected | z | Foul diff vs team norm |
|---|---|---|---|---|---|---|---|
| Brent Barnaky | GSW | 59 | 45 | 76.3% | 60.5% | +2.8 | +0.27 |
| Leon Wood | MIL | 74 | 46 | 62.2% | 48.0% | +2.6 | +0.19 |
| Kevin Cutler | LAC | 81 | 61 | 75.3% | 61.5% | +2.6 | -0.38 |
| Jacyn Goble | CHI | 45 | 28 | 62.2% | 44.0% | +2.5 | -0.25 |
| Marc Davis | CHI | 117 | 72 | 61.5% | 50.4% | +2.5 | +0.35 |
| Karl Lane | MEM | 57 | 40 | 70.2% | 54.6% | +2.5 | +0.16 |
| J.B. DeRosa | DET | 31 | 18 | 58.1% | 39.4% | +2.4 | -0.67 |
| Bill Kennedy | PHI | 89 | 50 | 56.2% | 44.6% | +2.3 | -0.07 |
| Ben Taylor | PHI | 56 | 36 | 64.3% | 49.9% | +2.3 | +0.63 |
| Sean Wright | HOU | 96 | 64 | 66.7% | 56.2% | +2.2 | -0.07 |
| Sean Corbin | HOU | 88 | 61 | 69.3% | 58.4% | +2.2 | -0.09 |
| Scott Twardoski | SAC | 60 | 32 | 53.3% | 39.9% | +2.2 | +0.31 |
Pairings with 50+ games where the team's foul differential (own fouls minus opponent's) with this official differs most from the team's normal differential that season. Negative means the team got called for fewer fouls than usual with this official on the floor.
| Official | Team | Games | Foul diff | vs team norm | FTA diff |
|---|---|---|---|---|---|
| Ben Taylor | BKN | 52 | -1.54 | -2.05 | +2.64 |
| Josh Tiven | IND | 81 | -1.53 | -1.84 | +1.64 |
| Curtis Blair | PHX | 66 | -1.08 | -1.73 | -0.85 |
| Curtis Blair | OKC | 69 | -1.70 | -1.70 | +3.78 |
| Tre Maddox | DAL | 54 | -2.74 | -1.56 | +1.74 |
| Tony Brothers | ATL | 100 | -1.47 | -1.33 | +1.42 |
| Karl Lane | WAS | 62 | -0.42 | -1.32 | -0.19 |
| James Williams | HOU | 57 | -1.93 | -1.29 | +5.62 |
| Pat Fraher | CHA | 93 | +0.82 | +1.94 | -1.45 |
| Eric Lewis | MIA | 95 | +1.22 | +1.61 | -2.50 |
| David Guthrie | IND | 80 | +2.13 | +1.58 | -3.86 |
| Josh Tiven | DAL | 75 | +0.44 | +1.51 | -2.69 |
| Marat Kogut | ATL | 59 | +1.53 | +1.51 | -3.20 |
| Kevin Cutler | BKN | 59 | +1.90 | +1.49 | -1.43 |
| Curtis Blair | HOU | 64 | +0.97 | +1.48 | +0.30 |
| Leon Wood | MIN | 70 | +2.10 | +1.48 | -2.51 |
Every player-official pairing with 25+ games of 8+ minutes. Free throws, personal fouls and points are per 36 minutes and measured against the player's own rates in the same seasons. 11,007 pairings. Beyond ±3: free throws 28, fouls 39, points 21; chance alone would give about 29.7 on each. Fouls run a touch high; free throws and points land right on chance. No player-official pairing in 23 seasons looks like more than variance, the famous ones included.
| Official | Player | Games | FTA/36 | Own rate | z | PF/36 | Own rate | z |
|---|---|---|---|---|---|---|---|---|
| James Williams | Jimmy Butler III | 36 | 5.75 | 8.15 | -3.5 | 1.65 | 1.40 | +1.3 |
| Pat Fraher | Robin Lopez | 40 | 1.66 | 3.47 | -3.4 | 4.25 | 3.91 | +0.7 |
| Brian Forte | Gerald Green | 29 | 0.73 | 2.46 | -3.2 | 3.15 | 3.57 | -0.9 |
| John Goble | Luka Doncic | 38 | 6.63 | 8.40 | -3.0 | 2.33 | 2.38 | -0.2 |
| John Goble | DeMar DeRozan | 59 | 5.18 | 6.71 | -3.0 | 2.51 | 2.40 | +0.7 |
| Marc Davis | Paul Pierce | 49 | 4.55 | 6.08 | -3.0 | 2.99 | 3.07 | -0.3 |
| James Williams | Giannis Antetokounmpo | 47 | 7.73 | 9.66 | -3.0 | 3.31 | 3.30 | +0.0 |
| Marc Davis | Jusuf Nurkic | 30 | 3.02 | 5.07 | -3.0 | 4.30 | 4.67 | -0.9 |
| Eric Dalen | Dwight Powell | 34 | 2.46 | 4.59 | -2.9 | 5.02 | 4.51 | +1.0 |
| Marc Davis | Chris Paul | 103 | 3.35 | 4.21 | -2.9 | 2.65 | 2.60 | +0.3 |
| Official | Player | Games | FTA/36 | Own rate | z | PF/36 | Own rate | z |
|---|---|---|---|---|---|---|---|---|
| Josh Tiven | Thabo Sefolosha | 25 | 3.89 | 1.60 | +5.3 | 2.77 | 2.73 | +0.1 |
| Aaron Smith | Aaron Gordon | 31 | 5.90 | 3.96 | +3.6 | 2.27 | 2.41 | -0.4 |
| Tyler Ford | Bogdan Bogdanovic | 32 | 3.47 | 2.02 | +3.5 | 3.16 | 2.93 | +0.7 |
| Scott Foster | Joe Smith | 27 | 5.63 | 3.46 | +3.4 | 4.21 | 4.08 | +0.3 |
| Courtney Kirkland | OG Anunoby | 33 | 3.84 | 2.48 | +3.3 | 3.40 | 2.92 | +1.5 |
| Eric Lewis | Kevin Garnett | 46 | 5.32 | 3.92 | +3.3 | 2.59 | 2.91 | -1.2 |
| Eric Dalen | Taurean Prince | 29 | 3.20 | 1.81 | +3.3 | 3.06 | 3.06 | -0.0 |
| James Capers | Mike Scott | 27 | 3.82 | 1.91 | +3.3 | 3.65 | 3.41 | +0.4 |
| Curtis Blair | Thomas Bryant | 25 | 5.52 | 3.29 | +3.2 | 2.73 | 3.39 | -1.3 |
| James Williams | Daniel Gafford | 26 | 6.91 | 4.42 | +3.2 | 3.56 | 4.70 | -1.8 |
| Official | Player | Games | FTA/36 | Own rate | z | PF/36 | Own rate | z |
|---|---|---|---|---|---|---|---|---|
| Scott Foster | Rudy Gobert | 58 | 6.36 | 5.75 | +1.3 | 4.44 | 3.28 | +4.9 |
| Eric Lewis | Anderson Varejao | 28 | 4.99 | 3.81 | +1.9 | 6.04 | 4.19 | +4.0 |
| Zach Zarba | Enes Freedom | 31 | 4.00 | 4.16 | -0.2 | 5.48 | 3.77 | +4.0 |
| Courtney Kirkland | Pascal Siakam | 42 | 4.08 | 4.44 | -0.8 | 4.37 | 3.21 | +3.9 |
| Scott Foster | Jusuf Nurkic | 31 | 4.80 | 5.10 | -0.4 | 6.34 | 4.87 | +3.7 |
| Sean Wright | Jerami Grant | 36 | 5.17 | 4.93 | +0.4 | 4.31 | 3.11 | +3.5 |
| Tyler Ford | Kristaps Porzingis | 27 | 6.08 | 5.57 | +0.7 | 5.00 | 3.67 | +3.4 |
| Bill Kennedy | Gerald Green | 27 | 2.40 | 2.85 | -0.8 | 5.32 | 3.55 | +3.4 |
| Sean Wright | Jeff Teague | 45 | 4.36 | 4.20 | +0.3 | 3.68 | 2.70 | +3.3 |
| James Capers | Marreese Speights | 31 | 4.09 | 4.48 | -0.5 | 7.60 | 5.56 | +3.3 |
| Official | Player | Games | FTA/36 | Own rate | z | PF/36 | Own rate | z |
|---|---|---|---|---|---|---|---|---|
| Zach Zarba | Ramon Sessions | 36 | 5.71 | 5.43 | +0.4 | 1.09 | 2.26 | -3.6 |
| Eric Dalen | Kyle Lowry | 50 | 3.58 | 3.90 | -0.8 | 2.41 | 3.26 | -3.5 |
| Eric Lewis | Ben Gordon | 47 | 4.20 | 3.89 | +0.6 | 2.21 | 3.13 | -3.2 |
| Rodney Mott | Andre Miller | 61 | 3.87 | 4.20 | -0.8 | 1.91 | 2.58 | -3.1 |
| Eric Dalen | P.J. Tucker | 27 | 1.37 | 1.56 | -0.5 | 2.02 | 3.13 | -3.1 |
| Derrick Collins | Marcus Morris Sr. | 40 | 4.15 | 2.89 | +2.7 | 2.11 | 3.11 | -3.1 |
| Marc Davis | Derrick Favors | 41 | 4.39 | 4.00 | +0.7 | 2.79 | 3.83 | -3.0 |
| Curtis Blair | Dario Saric | 28 | 3.33 | 3.22 | +0.2 | 2.11 | 3.47 | -3.0 |
| Tony Brothers | Nenad Krstic | 26 | 3.76 | 3.37 | +0.6 | 3.16 | 4.54 | -2.9 |
| Josh Tiven | Kyle Korver | 40 | 1.56 | 1.48 | +0.3 | 1.84 | 2.81 | -2.9 |
269 games since 2003 had a free-throw gap of 25 or more, 0.93 per 100 games. Officials with 300+ games, most frequent first, with the count you'd expect from their share of games.
| Official | Games | Lopsided games | Expected | Per 100 |
|---|---|---|---|---|
| CJ Washington | 454 | 7 | 4.20 | 1.54 |
| Courtney Kirkland | 1453 | 20 | 13.50 | 1.38 |
| Dedric Taylor | 663 | 9 | 6.10 | 1.36 |
| Karl Lane | 908 | 12 | 8.40 | 1.32 |
| Leon Wood | 1246 | 16 | 11.50 | 1.28 |
| Tony Brothers | 1591 | 20 | 14.70 | 1.26 |
| Michael Smith | 1360 | 17 | 12.60 | 1.25 |
| Sean Wright | 1331 | 16 | 12.30 | 1.20 |
| Date | Away | Home | Away pts | Home pts | Home FTA − away | Home fouls − away | Official | Official | Official |
|---|---|---|---|---|---|---|---|---|---|
| 2003-11-19 | LAL | NYK | 104 | 83 | -41 | +15 | nan | nan | |
| 2016-01-20 | DET | HOU | 123 | 114 | -41 | +24 | nan | Kevin Cutler | |
| 2010-01-29 | CLE | IND | 94 | 73 | -39 | +16 | nan | Marc Davis | |
| 2014-03-03 | MIN | DEN | 132 | 128 | -39 | +15 | nan | Kevin Cutler | Marc Davis |
| 2006-01-02 | PHX | NYK | 133 | 140 | +38 | -14 | nan | nan | |
| 2013-11-07 | LAL | HOU | 99 | 98 | +37 | -19 | nan | John Goble | |
| 2006-02-25 | CHI | PHI | 102 | 108 | +37 | -19 | nan | nan | |
| 2010-03-31 | MIL | CLE | 98 | 101 | +36 | -17 | Tony Brothers | nan | |
| 2012-01-12 | ORL | GSW | 117 | 109 | -36 | +20 | nan | Sean Wright | |
| 2019-11-05 | IND | CHA | 120 | 122 | +35 | -17 | Mark Lindsay | Scott Foster | Justin Van Duyne |
Fouls and free throws fell for a decade, jumped with the 2018-19 freedom-of-movement emphasis, then settled lower again. Home court has faded from 62% to about 55%.
| Season | Games | Fouls/game | FTA/game | Home win | Techs/game | OT |
|---|---|---|---|---|---|---|
| 2003-04 | 1272 | 43.1 | 48.5 | 62.1% | 1.67 | 5.9% |
| 2004-05 | 1314 | 45.4 | 52.2 | 60.2% | 1.70 | 6.5% |
| 2005-06 | 1319 | 45.7 | 52.7 | 60.8% | 1.69 | 7.3% |
| 2006-07 | 1309 | 44.4 | 52.2 | 59.5% | 1.85 | 6.9% |
| 2007-08 | 1316 | 42.2 | 50.0 | 61.0% | 1.53 | 5.0% |
| 2008-09 | 1223 | 42.1 | 49.5 | 61.0% | 1.57 | 5.6% |
| 2009-10 | 1231 | 41.7 | 49.1 | 59.4% | 1.51 | 5.8% |
| 2010-11 | 1137 | 41.3 | 48.5 | 60.1% | 1.72 | 6.9% |
| 2011-12 | 1075 | 39.4 | 45.1 | 59.3% | 1.54 | 6.4% |
| 2012-13 | 1315 | 40.0 | 44.9 | 61.4% | 1.35 | 6.4% |
| 2013-14 | 1315 | 41.6 | 47.3 | 58.0% | 1.32 | 6.7% |
| 2014-15 | 1310 | 40.9 | 46.1 | 57.6% | 1.15 | 6.4% |
| 2015-16 | 1317 | 40.8 | 47.0 | 59.4% | 1.29 | 6.2% |
| 2016-17 | 1311 | 40.3 | 46.9 | 58.3% | 1.30 | 5.6% |
| 2017-18 | 1314 | 40.1 | 43.9 | 58.6% | 4.9% | |
| 2018-19 | 1312 | 42.0 | 46.3 | 59.1% | 1.27 | 5.4% |
| 2019-20 | 986 | 41.2 | 45.9 | 55.0% | 1.26 | 6.4% |
| 2020-21 | 1130 | 38.7 | 43.5 | 54.3% | 1.08 | 5.0% |
| 2021-22 | 1304 | 39.5 | 43.9 | 54.7% | 1.30 | 4.4% |
| 2022-23 | 1299 | 39.9 | 46.8 | 57.9% | 1.24 | 6.3% |
| 2023-24 | 1295 | 37.5 | 43.3 | 54.5% | 1.32 | 4.8% |
| 2024-25 | 1318 | 37.4 | 43.5 | 54.6% | 1.04 | 4.9% |
| 2025-26 | 1321 | 39.9 | 47.0 | 55.5% | 1.06 | 4.5% |
Every foul in the play-by-play since 2003-04: 939,437 of them across 28,028 games, typed, clocked and scored. This is where the box score stops and the whistle itself starts: what kind of foul, when in the game, who was leading, and whether the next call went the other way. All 105 officials with 200+ games, active and retired, because the questions here are about habits and a habit needs a career to show. Retired officials are marked. Nothing here says whether a call was right.
Per 48 minutes with the official on the floor, league-wide: 15.6 shooting fouls, 10.8 other personal fouls, 2.3 offensive, 2.0 loose-ball, 0.58 technicals. The share that is shooting fouls is the tell for how an official sees contact.
| Official | Games | Shooting share | Shooting/48 | z |
|---|---|---|---|---|
| Brent Barnaky | 973 | 50.8% | 15.7 | +5.4 |
| Pat Fraher | 1457 | 49.7% | 15.8 | +5.1 |
| Brandon Schwab | 239 | 54.1% | 16.2 | +4.8 |
| Joe DeRosa (retired) | 484 | 48.8% | 17.3 | +3.8 |
| Eric Dalen | 942 | 50.4% | 15.3 | +3.7 |
| Brett Nansel | 441 | 51.2% | 16.3 | +3.5 |
| Ed Malloy | 1503 | 49.3% | 15.4 | +3.5 |
| Leroy Richardson (retired) | 1034 | 48.6% | 15.6 | +3.4 |
| Official | Games | Shooting share | Shooting/48 | z |
|---|---|---|---|---|
| Monty McCutchen (retired) | 1031 | 45.6% | 15.0 | -7.8 |
| Leon Wood | 1245 | 46.3% | 14.9 | -7.3 |
| Rodney Mott | 1314 | 47.0% | 15.4 | -5.7 |
| Tony Brown (retired) | 1075 | 46.3% | 15.1 | -5.4 |
| Joe Crawford (retired) | 839 | 46.0% | 15.2 | -5.1 |
| John Goble | 1255 | 47.9% | 15.3 | -5.1 |
| Scott Foster | 1607 | 47.6% | 15.6 | -4.2 |
| Derrick Stafford (retired) | 996 | 46.6% | 15.1 | -3.9 |
League-wide, 20.2% of fouls come in the first quarter and 28.6% in the fourth; 2.1 per game land in the last two minutes of regulation. Officials whose fourth quarter carries the most of their whistle, and the ones who call the most late.
| Official | Games | Q1 share | Q4 share | vs league |
|---|---|---|---|---|
| Ron Olesiak (retired) | 280 | 20.2% | 30.0% | +0.9 |
| Nick Buchert | 948 | 19.8% | 29.1% | +0.7 |
| Steve Javie (retired) | 507 | 20.0% | 29.8% | +0.7 |
| Zach Zarba | 1579 | 19.5% | 29.2% | +0.7 |
| J.B. DeRosa | 389 | 20.6% | 28.5% | +0.6 |
| Kevin Fehr (retired) | 597 | 20.3% | 29.6% | +0.6 |
| Brett Nansel | 441 | 20.0% | 28.7% | +0.6 |
| CJ Washington | 450 | 20.8% | 28.6% | +0.5 |
| Official | Games | Per game | vs league |
|---|---|---|---|
| Ron Olesiak (retired) | 280 | 2.67 | +0.30 |
| Luis Grillo (retired) | 323 | 2.55 | +0.19 |
| Jim Clark (retired) | 437 | 2.53 | +0.17 |
| Phenizee Ransom | 375 | 2.06 | +0.15 |
| Jenna Schroeder | 277 | 2.04 | +0.15 |
| Kevin Fehr (retired) | 597 | 2.40 | +0.14 |
| Nick Buchert | 948 | 2.10 | +0.13 |
| Dedric Taylor | 659 | 2.08 | +0.13 |
The test: after a foul on one team, how often is the next foul within 60 seconds on the other team? League-wide it is 46.3%. An official well above that flips the whistle back and forth; one well below lets a run of calls stand. This is the closest the data gets to "game management".
| Official | Games | Flip rate | vs league | z |
|---|---|---|---|---|
| Scott Wall (retired) | 1125 | 47.6% | +1.3 | +3.5 |
| Ben Taylor | 733 | 47.8% | +1.4 | +3.0 |
| Jim Clark (retired) | 437 | 47.5% | +1.6 | +3.0 |
| Phil Robinson (retired) | 426 | 47.7% | +1.6 | +3.0 |
| Joe DeRosa (retired) | 484 | 47.4% | +1.4 | +2.8 |
| Tyler Ford | 677 | 47.7% | +1.2 | +2.5 |
| Leroy Richardson (retired) | 1034 | 47.1% | +0.9 | +2.3 |
| Luis Grillo (retired) | 323 | 47.3% | +1.4 | +2.2 |
| Official | Games | Flip rate | vs league | z |
|---|---|---|---|---|
| Eddie F. Rush (retired) | 615 | 43.9% | -2.2 | -4.7 |
| Steve Javie (retired) | 507 | 44.3% | -1.7 | -3.1 |
| Marc Davis | 1618 | 45.5% | -0.8 | -2.7 |
| Haywoode Workman (retired) | 640 | 45.1% | -1.3 | -2.6 |
| Monty McCutchen (retired) | 1031 | 45.3% | -0.9 | -2.5 |
| Dan Crawford (retired) | 1053 | 45.3% | -0.9 | -2.5 |
| Derek Richardson (retired) | 1145 | 45.4% | -0.9 | -2.4 |
| Gediminas Petraitis | 673 | 45.3% | -1.2 | -2.4 |
With a lead of ten or more, what share of fouls go on the team ahead? League-wide 54.6%: the leader gets whistled a little more, which fits how leads are protected. The officials furthest from that, both ways.
| Official | Games | On the leader | vs league | z |
|---|---|---|---|---|
| Matt Myers | 344 | 59.2% | +3.3 | +4.2 |
| David Guthrie | 1200 | 56.2% | +1.8 | +4.1 |
| Phenizee Ransom | 375 | 58.8% | +2.9 | +3.9 |
| Nick Buchert | 948 | 56.4% | +1.8 | +3.7 |
| Ashley Moyer-Gleich | 278 | 58.9% | +3.2 | +3.6 |
| Brian Forte | 1175 | 56.0% | +1.5 | +3.4 |
| Dedric Taylor | 659 | 56.2% | +1.9 | +3.1 |
| Zach Zarba | 1579 | 55.7% | +1.2 | +3.1 |
| Official | Games | On the leader | vs league | z |
|---|---|---|---|---|
| Jess Kersey (retired) | 263 | 49.5% | -5.2 | -5.5 |
| Ron Garretson (retired) | 1123 | 51.5% | -2.4 | -5.4 |
| John Butler | 345 | 52.0% | -4.0 | -4.9 |
| Tommy Nunez Jr. (retired) | 435 | 52.2% | -2.6 | -3.6 |
| Ken Mauer (retired) | 1249 | 52.7% | -1.4 | -3.3 |
| Natalie Sago | 421 | 53.8% | -2.1 | -3.1 |
| Sean Corbin | 1260 | 53.1% | -1.2 | -2.9 |
| Rodney Mott | 1314 | 53.4% | -1.2 | -2.9 |
Last five minutes, margin within five. League-wide 4.26 fouls per close game land in that stretch. Clutch whistle: in the last five minutes of games within five, fouls run 0.85 a minute vs 0.73 a minute the rest of the game (1.16x). Who swallows it late and who does not.
| Official | Games | Close games | Fouls in clutch | vs league |
|---|---|---|---|---|
| Jack Nies (retired) | 395 | 177 | 4.14 | -0.54 |
| John Butler | 345 | 126 | 3.61 | -0.47 |
| Dan Crawford (retired) | 1053 | 462 | 3.96 | -0.42 |
| Mike Callahan (retired) | 1145 | 517 | 4.04 | -0.31 |
| Evan Scott | 294 | 113 | 3.77 | -0.30 |
| Phenizee Ransom | 375 | 159 | 3.79 | -0.29 |
| Ron Garretson (retired) | 1123 | 451 | 4.09 | -0.28 |
| Mark Wunderlich (retired) | 459 | 214 | 4.35 | -0.28 |
| Official | Games | Close games | Fouls in clutch | vs league |
|---|---|---|---|---|
| Tim Donaghy (retired) | 274 | 118 | 5.51 | +0.79 |
| Jacyn Goble | 568 | 214 | 4.48 | +0.41 |
| Joe DeRosa (retired) | 484 | 215 | 5.04 | +0.41 |
| Joe Crawford (retired) | 839 | 381 | 4.81 | +0.39 |
| Tyler Ford | 677 | 256 | 4.46 | +0.39 |
| Jonathan Sterling | 400 | 146 | 4.45 | +0.38 |
| Nate Green | 258 | 91 | 4.44 | +0.37 |
| Jim Clark (retired) | 437 | 211 | 5.01 | +0.33 |
Share of fouls on the home team in the first quarter against the fourth. A drop means the home whistle softens as the game goes on.
| Official | Games | Home share Q1 | Home share Q4 | Shift vs league |
|---|---|---|---|---|
| Suyash Mehta | 257 | 52.1% | 50.2% | -2.7 |
| Joe Crawford (retired) | 839 | 49.9% | 48.9% | -2.1 |
| Kevin Fehr (retired) | 597 | 48.5% | 47.8% | -2.0 |
| Sean Corbin | 1260 | 49.0% | 48.4% | -1.5 |
| Haywoode Workman (retired) | 640 | 49.3% | 48.5% | -1.5 |
| Brandon Schwab | 239 | 50.5% | 49.9% | -1.5 |
| Ron Olesiak (retired) | 280 | 47.8% | 48.0% | -1.4 |
| Jonathan Sterling | 400 | 49.4% | 48.6% | -1.4 |
Chance alone would put about 5 of 105 officials beyond 2 sd on any one test. Observed: make-up calls 23, leader tilt 47, foul mix 52, whistle per 48 14. Foul mix and leader tilt are real habits that differ by official; make-up calls a little; the rest of the relationships in the takeaways above are across the field, not any one official.
Source: ESPN's public game summaries, pulled game by game for every regular-season and playoff game from October 2003 through the 2026 Finals (29,043 games with a crew listed; a few hundred early games have no officials on record and are left out of referee tables). Officials come from the game info block; team totals and every player line come from the box score. Team foul counts are the sum of player fouls (ESPN's team field is blank for 2017-18, so technicals are missing that season).
Baselines: home-win rate is compared with the league's rate in the same season and game type. Team pairings are compared with the team's own win rate that season at that venue. Player pairings are compared with the player's own per-36 rates that season, weighted by his games with that official. Playoff series state is rebuilt from the schedule.
What the method can't see: which crew member made which call, foul type, the score when a call came, or whether a call was right. It measures tendencies and outcomes. A referee who worked a lot of Spurs games in 2005 will look Spurs-friendly on raw wins; the baselines above exist to strip that out.
NHL
Every NHL game from 2005-06 to 2025-26, every official on the ice, every penalty. Referees, the teams and coaches in front of them, and where the numbers are flat.
Officials should be invisible. The best crew is the one nobody talks about after the game. The grade rewards sitting near the middle of the pack in every category at once, not a tight whistle or a loose one, a home lean or a road lean. I take every active official with 150+ games, find the median in each category, and measure how far each one sits from it in typical spreads (the median absolute deviation, scaled to match a standard deviation). Before measuring, each number is pulled part of the way toward the middle, by an amount set for that category: the more a category moves by luck from game to game, and the fewer games an official has, the further the pull. The distance is then measured in the spread of the raw numbers, so a category that is mostly luck leaves nearly everyone at an A there, and a noisy category cannot hand out the same F as a steady one. One side effect is deliberate: an official with few games cannot yet be shown to stand out, so newer officials sit closer to an A until their games say otherwise. Within half a spread of the median in a category is an A there; within one, a B; within one and a half, a C; within two, a D; further, an F. Consistency is the one category where only the wide side counts, and it has two parts: from one night to the next, how much the penalty total bounces around after allowing for who was playing (overtime scaled back to a regulation game), and how often the power plays came out one-sided. Each part is measured in the same spreads as every other category, steadier than the median counts as zero, the category takes the worse of the two. Both referees share the numbers of a game, so read it as a crew habit until the crews are split. The overall grade is the average of the two categories where the official sits furthest from the pack; two habits set the grade, and one noisy category cannot sink anyone on its own. An A official is one whose numbers you could not tell from the pack. An F is one whose numbers you could.
Categories: Whistle (penalties per game vs the league in the same seasons), Home lean (home-minus-away penalties vs the league lean), Power-play lean (home-minus-away power plays vs the league lean), Misconducts (misconducts per game vs the league in the same seasons), Consistency (night-to-night spread of the whistle around what the two teams usually commit, and lopsided power-play nights; only the wide side counts, and the worse part sets the letter). 35 active officials graded; median values: Whistle +0.06, Home lean +0.01, Power-play lean -0.00, Misconducts +0.02, Consistency 0.00.
For every official with 300+ games I split the games into odd and even (by date), work out each category on both halves, and check how well one half predicts the other across officials. The first number is that correlation; the second projects it to a full career. Near 1 means the category describes the official; near 0 means it is luck. The rule: a category counts toward the grade only if its full-career number here is at least 0.2. Home results fail it in hockey and are left out. Close games (home wins in games decided by one goal) and the half-to-half penalty gap fail it and are shown here but left out of the grade. Both referees share each game, so even the categories that repeat describe a crew habit as much as a person.
| Category | Odd vs even | Full career | Officials | In the grade |
|---|---|---|---|---|
| Whistle | +0.85 | +0.92 | 56 | yes |
| Home lean | +0.41 | +0.58 | 56 | yes |
| Home results | -0.15 | -0.34 | 56 | no |
| Power-play lean | +0.44 | +0.61 | 56 | yes |
| Misconducts | +0.23 | +0.38 | 56 | yes |
| Close games | +0.09 | +0.17 | 56 | no |
| Consistency: night spread | +0.80 | +0.89 | 56 | yes |
| Consistency: lopsided nights | +0.81 | +0.89 | 56 | yes |
A 14 B 15 C 3 D 2 F 1 of 35 active officials
| Official | Games | Whistlerepeats 0.92 | Home leanrepeats 0.58 | Power-play leanrepeats 0.61 | Misconductsrepeats 0.38 | Consistencyrepeats 0.89 | Distance | Grade |
|---|---|---|---|---|---|---|---|---|
| Beau Halkidis | 183 | A | A | A | A | A | 0.09 | A |
| Brandon Schrader | 328 | A | A | A | A | A | 0.14 | A |
| Kendrick Nicholson | 758 | A | A | A | A | A | 0.17 | A |
| Francis Charron | 995 | A | A | A | A | A | 0.25 | A |
| Tom Chmielewski | 703 | A | A | A | A | A | 0.27 | A |
| Pierre Lambert | 597 | A | A | A | A | A | 0.28 | A |
| Jon McIsaac | 746 | A | A | A | A | A | 0.29 | A |
| Peter MacDougall | 526 | A | A | A | A | A | 0.29 | A |
| Kyle Rehman | 1212 | A | A | A | A | A | 0.31 | A |
| Jordan Samuels-Thomas | 161 | A | A | A | A | A | 0.31 | A |
| Justin Kea | 218 | A | A | A | A | A | 0.33 | A |
| Graham Skilliter | 927 | B | A | A | A | A | 0.40 | A |
| Official | Games | Whistlerepeats 0.92 | Home leanrepeats 0.58 | Power-play leanrepeats 0.61 | Misconductsrepeats 0.38 | Consistencyrepeats 0.89 | Distance | Grade |
|---|---|---|---|---|---|---|---|---|
| Chris Rooney | 1571 | D | A | A | C | F | 2.10 | F |
| Wes McCauley | 1726 | D | A | A | B | D | 1.91 | D |
| Dan O'Rourke | 1611 | C | A | A | A | F | 1.73 | D |
| Eric Furlatt | 1628 | C | A | A | B | D | 1.44 | C |
| Kelly Sutherland | 1702 | A | B | B | A | C | 1.05 | C |
| Carter Sandlak | 256 | D | A | A | A | A | 1.03 | C |
| Trevor Hanson | 863 | B | C | B | B | B | 1.00 | B |
| Brian Pochmara | 1334 | A | A | A | A | D | 1.00 | B |
| Garrett Rank | 798 | C | A | A | B | B | 0.91 | B |
| Chris Lee | 1532 | A | A | A | A | D | 0.90 | B |
| Gord Dwyer | 1520 | A | A | A | A | C | 0.85 | B |
| Michael Markovic | 393 | D | A | A | A | A | 0.81 | B |
Every graded official: sortable in the Referees tab (Grade column). Distance is in median absolute deviations; 0 is dead center.
Total penalties called per game with the official on the ice, against the league average in the same seasons. Officials with 150+ games. This is the strongest signal in the data. It’s stable and it’s large, and it says nothing about fairness, only about how much gets called.
| Official | Games | Penalties/game | vs league | z |
|---|---|---|---|---|
| Michael Markovic | 393 | 8.00 | +0.75 | +4.4 |
| Cody Beach | 212 | 7.90 | +0.72 | +3.3 |
| Chris Rooney | 1571 | 9.20 | +0.66 | +5.7 |
| Dan O'Rourke | 1611 | 9.10 | +0.49 | +4.3 |
| Jake Brenk | 679 | 7.70 | +0.48 | +3.8 |
| Eric Furlatt | 1628 | 9.10 | +0.48 | +4.5 |
| Garrett Rank | 798 | 7.70 | +0.46 | +3.7 |
| Trevor Hanson | 863 | 7.80 | +0.44 | +3.6 |
| Jean Hebert | 1014 | 7.80 | +0.38 | +3.5 |
| Chris Schlenker | 546 | 7.60 | +0.35 | +2.6 |
| Official | Games | Penalties/game | vs league | z |
|---|---|---|---|---|
| Carter Sandlak | 256 | 6.40 | -0.86 | -4.6 |
| Wes McCauley | 1726 | 7.90 | -0.61 | -6.2 |
| Corey Syvret | 450 | 6.80 | -0.42 | -3.0 |
| Furman South | 493 | 6.90 | -0.34 | -2.4 |
| Mitch Dunning | 360 | 6.90 | -0.32 | -1.9 |
| Justin Kea | 218 | 7.00 | -0.20 | -0.9 |
| Thomas John Luxmore | 846 | 7.20 | -0.17 | -1.5 |
| Graham Skilliter | 927 | 7.30 | -0.14 | -1.3 |
| Brandon Blandina | 377 | 7.10 | -0.12 | -0.7 |
| Kelly Sutherland | 1702 | 8.50 | -0.10 | -1.0 |
Home penalties minus away penalties in true home games, against the league lean in the same seasons (home teams get called for slightly fewer penalties everywhere). Negative means the official calls even fewer on the home team than the rest of the field does; positive means the visitor gets the friendlier whistle.
| Official | Games | Home penalties − away | vs league | z | Home PP edge vs league |
|---|---|---|---|---|---|
| Trevor Hanson | 863 | -0.50 | -0.26 | -4.2 | +0.24 |
| Jake Brenk | 679 | -0.38 | -0.14 | -2.1 | +0.13 |
| Frederick L'Ecuyer | 1229 | -0.36 | -0.09 | -1.7 | +0.08 |
| Cody Beach | 212 | -0.42 | -0.20 | -1.6 | +0.14 |
| Chris Schlenker | 546 | -0.33 | -0.09 | -1.2 | +0.09 |
| Chris Rooney | 1571 | -0.36 | -0.06 | -1.2 | +0.04 |
| Jon McIsaac | 746 | -0.32 | -0.07 | -1.1 | +0.09 |
| Carter Sandlak | 256 | -0.32 | -0.09 | -1.0 | +0.08 |
| Official | Games | Home penalties − away | vs league | z | Home PP edge vs league |
|---|---|---|---|---|---|
| Kelly Sutherland | 1702 | -0.21 | +0.09 | +2.2 | -0.11 |
| Mitch Dunning | 360 | -0.07 | +0.17 | +2.1 | -0.17 |
| Peter MacDougall | 526 | -0.13 | +0.11 | +1.6 | -0.14 |
| Graham Skilliter | 927 | -0.18 | +0.08 | +1.6 | -0.08 |
| Thomas John Luxmore | 846 | -0.17 | +0.09 | +1.5 | -0.07 |
| Wes McCauley | 1726 | -0.24 | +0.05 | +1.3 | -0.05 |
| Justin Kea | 218 | -0.11 | +0.12 | +1.1 | -0.13 |
| Pierre Lambert | 597 | -0.18 | +0.07 | +1.0 | -0.10 |
10-minute and game misconducts per game, both teams combined, as the NHL logs them.
| Official | Games | Misconducts/game |
|---|---|---|
| Chris Rooney | 1571 | 0.25 |
| Frederick L'Ecuyer | 1229 | 0.22 |
| Garrett Rank | 798 | 0.22 |
| Trevor Hanson | 863 | 0.22 |
| Dan O'Rourke | 1611 | 0.20 |
| Wes McCauley | 1726 | 0.20 |
| Kelly Sutherland | 1702 | 0.19 |
| Gord Dwyer | 1520 | 0.19 |
Home-team win rate in true home games with the official on the ice, against what the matchup predicts: both teams’ records that season (not counting that game), adjusted to the league home rate that season. That keeps a run of early-season home games against weak visitors from looking like a friendly whistle. Across 35 officials with 150+ games, chance alone would put about 2 beyond ±2 and about 0.1 beyond ±3; the count here is 1 and 0.
| Official | Home games | Home win % | Expected | z |
|---|---|---|---|---|
| Francois St-Laurent | 1232 | 57.6% | 54.5% | +2.3 |
| Michael Markovic | 393 | 56.7% | 53.1% | +1.5 |
| Chris Schlenker | 546 | 55.5% | 53.0% | +1.2 |
| Dan O'Rourke | 1586 | 56.2% | 54.8% | +1.2 |
| Kendrick Nicholson | 755 | 56.7% | 54.7% | +1.1 |
| Brandon Schrader | 328 | 55.2% | 52.4% | +1.0 |
| Thomas John Luxmore | 834 | 56.7% | 55.1% | +1.0 |
| Jon McIsaac | 734 | 56.1% | 54.7% | +0.8 |
| Official | Home games | Home win % | Expected | z |
|---|---|---|---|---|
| Graham Skilliter | 923 | 49.8% | 53.0% | -2.0 |
| Pierre Lambert | 596 | 50.5% | 53.9% | -1.7 |
| Mitch Dunning | 360 | 49.7% | 53.6% | -1.5 |
| Cody Beach | 212 | 48.1% | 52.5% | -1.3 |
| Brandon Blandina | 377 | 50.7% | 53.5% | -1.1 |
| Wes McCauley | 1703 | 54.0% | 55.2% | -1.0 |
| Kyle Rehman | 1202 | 52.7% | 53.8% | -0.8 |
| Chris Lee | 1509 | 53.3% | 54.2% | -0.7 |
Games where the matchup made one team at least a 60% favorite, judged from both teams' strength that season (scoring margins against who they played) and where the game was played. League-wide, underdogs won 32.4% of 12,889 such games, against 32.4% expected. Each official is measured against the upsets expected in that official's own games, not a league average. Officials with 75+ of them, most underdog wins first. 35 officials: 0 beyond ±2 (chance: about 1.6), 0 beyond ±3 (chance: about 0.1).
| Official | Games | Underdog wins | Expected | z |
|---|---|---|---|---|
| Wes McCauley | 755 | 35.2% | 32.5% | +1.6 |
| Michael Markovic | 212 | 36.8% | 32.1% | +1.5 |
| Garrett Rank | 424 | 34.0% | 31.7% | +1.0 |
| Eric Furlatt | 752 | 34.4% | 33.0% | +0.8 |
| Brian Pochmara | 625 | 33.8% | 32.3% | +0.8 |
| Jake Brenk | 340 | 32.4% | 31.2% | +0.5 |
| Francis Charron | 451 | 33.5% | 32.5% | +0.4 |
| Francois St-Laurent | 612 | 33.2% | 32.5% | +0.3 |
| Gord Dwyer | 693 | 29.6% | 32.6% | -1.7 |
| Kelly Sutherland | 736 | 30.0% | 32.3% | -1.3 |
| Cody Beach | 112 | 27.7% | 32.6% | -1.1 |
| Jon McIsaac | 384 | 28.9% | 31.5% | -1.1 |
| Corey Syvret | 279 | 27.6% | 30.4% | -1.0 |
| Kyle Rehman | 585 | 30.6% | 32.6% | -1.0 |
| Trevor Hanson | 450 | 29.8% | 32.0% | -1.0 |
| Graham Skilliter | 456 | 30.5% | 32.6% | -1.0 |
Home-team win rate in true home games decided by one goal, officials with 300+ games.
| Official | Games | Close games | Home wins in them |
|---|---|---|---|
| Mitch Dunning | 360 | 35.3% | 45.7% |
| Pierre Lambert | 597 | 39.9% | 46.4% |
| Graham Skilliter | 927 | 41.5% | 48.2% |
| Brandon Blandina | 377 | 40.1% | 48.3% |
| Garrett Rank | 798 | 44.1% | 48.6% |
| Chris Rooney | 1571 | 42.4% | 49.2% |
| Michael Markovic | 393 | 41.7% | 63.4% |
| Brandon Schrader | 328 | 44.2% | 57.9% |
| Dan O'Rourke | 1611 | 46.4% | 56.2% |
| Trevor Hanson | 863 | 42.2% | 55.5% |
| Francois St-Laurent | 1245 | 44.3% | 55.3% |
| Thomas John Luxmore | 846 | 41.4% | 54.5% |
Every pairing with 12+ games. Expected wins come from each game’s matchup (both teams’ records that season without that game, and the home ice), so a strong team is expected to win and drawing the big games is not held against anyone; the question is only whether the team did better or worse than that with this official. 1,080 pairings; 36 beyond ±2 (chance: about 49.8), 2 beyond ±3 (chance: about 2.7).
| Official | Team | Games | Wins | Win % | Expected | z | Penalty diff vs team norm |
|---|---|---|---|---|---|---|---|
| Jordan Samuels-Thomas | Anaheim Ducks | 19 | 3 | 15.8% | 47.2% | -2.9 | -0.15 |
| Jake Brenk | New York Rangers | 39 | 13 | 33.3% | 54.9% | -2.8 | -0.39 |
| Brandon Blandina | Toronto Maple Leafs | 19 | 6 | 31.6% | 58.9% | -2.5 | +0.03 |
| Chris Lee | Detroit Red Wings | 110 | 44 | 40.0% | 50.3% | -2.3 | -0.26 |
| Kendrick Nicholson | Anaheim Ducks | 37 | 10 | 27.0% | 44.8% | -2.3 | +0.20 |
| Justin Kea | New York Islanders | 16 | 3 | 18.8% | 46.2% | -2.2 | +0.14 |
| Michael Markovic | New York Islanders | 24 | 7 | 29.2% | 51.2% | -2.2 | -0.06 |
| Frederick L'Ecuyer | St. Louis Blues | 75 | 32 | 42.7% | 55.1% | -2.2 | +0.11 |
| Kyle Rehman | Washington Capitals | 71 | 30 | 42.3% | 54.8% | -2.2 | +0.32 |
| Kyle Rehman | Utah Mammoth | 13 | 3 | 23.1% | 52.8% | -2.2 | +0.26 |
| Justin Kea | Columbus Blue Jackets | 16 | 3 | 18.8% | 44.6% | -2.1 | -0.17 |
| Mitch Dunning | Detroit Red Wings | 29 | 8 | 27.6% | 46.7% | -2.1 | +0.51 |
| Official | Team | Games | Wins | Win % | Expected | z | Penalty diff vs team norm |
|---|---|---|---|---|---|---|---|
| Garrett Rank | Los Angeles Kings | 50 | 36 | 72.0% | 48.3% | +3.5 | +0.80 |
| Jake Brenk | Minnesota Wild | 44 | 32 | 72.7% | 50.5% | +3.0 | -0.18 |
| Garrett Rank | Arizona Coyotes | 35 | 21 | 60.0% | 37.9% | +2.8 | -0.25 |
| Chris Rooney | Columbus Blue Jackets | 98 | 56 | 57.1% | 45.4% | +2.4 | -0.01 |
| Justin Kea | Minnesota Wild | 16 | 12 | 75.0% | 45.9% | +2.4 | +0.24 |
| Carter Sandlak | Detroit Red Wings | 14 | 11 | 78.6% | 47.5% | +2.4 | -0.04 |
| Francis Charron | Pittsburgh Penguins | 75 | 50 | 66.7% | 53.4% | +2.4 | -0.11 |
| Cody Beach | New Jersey Devils | 14 | 11 | 78.6% | 47.8% | +2.3 | -0.32 |
| Brandon Schrader | Montréal Canadiens | 16 | 11 | 68.8% | 41.2% | +2.3 | +0.35 |
| Carter Sandlak | Minnesota Wild | 18 | 15 | 83.3% | 56.9% | +2.3 | +0.34 |
| Corey Syvret | Utah Mammoth | 15 | 12 | 80.0% | 51.2% | +2.3 | -0.45 |
| Justin Kea | Washington Capitals | 15 | 12 | 80.0% | 51.2% | +2.3 | -0.41 |
Pairings with 24+ games where the team's penalty differential (own penalties minus opponent's) with this official differs most from the team's normal differential that season. Negative means the team got called for fewer penalties than usual with this official on the ice.
| Official | Team | Games | Penalty diff | vs team norm | PP diff |
|---|---|---|---|---|---|
| Brandon Blandina | Chicago Blackhawks | 26 | -0.85 | -0.83 | +0.69 |
| Brandon Blandina | Pittsburgh Penguins | 27 | -0.93 | -0.74 | +0.93 |
| Jake Brenk | Colorado Avalanche | 49 | -0.92 | -0.64 | +0.92 |
| Brandon Schrader | Philadelphia Flyers | 26 | -0.69 | -0.64 | +0.62 |
| Furman South | New York Rangers | 29 | -0.48 | -0.63 | +0.41 |
| Pierre Lambert | Vegas Golden Knights | 35 | -0.83 | -0.63 | +0.86 |
| Trevor Hanson | Los Angeles Kings | 79 | -0.58 | -0.63 | +0.53 |
| Eric Furlatt | Montréal Canadiens | 148 | -0.41 | -0.60 | +0.32 |
| Garrett Rank | Los Angeles Kings | 50 | +0.84 | +0.80 | -0.80 |
| Pierre Lambert | Calgary Flames | 34 | +0.68 | +0.69 | -0.74 |
| Eric Furlatt | Atlanta Thrashers | 32 | +0.66 | +0.65 | -0.66 |
| Brandon Blandina | Ottawa Senators | 24 | +0.46 | +0.65 | -0.25 |
| Frederick L'Ecuyer | Vegas Golden Knights | 40 | +0.48 | +0.65 | -0.57 |
| Pierre Lambert | Florida Panthers | 40 | +0.60 | +0.61 | -0.55 |
| Pierre Lambert | Vancouver Canucks | 35 | +0.49 | +0.60 | -0.46 |
| Jon McIsaac | Columbus Blue Jackets | 54 | +0.63 | +0.57 | -0.65 |
The same question asked of the bench instead of the program: how does a coach's team do with each official on the ice, across every team the coach has had. Expected wins come from each game’s matchup, so a rebuilding year is not pinned on a referee and drawing the big games is not held against anyone. Pairings need 10+ games. 136 coaches, 1,877 coach×referee pairings; 3 sit beyond three standard deviations against about 4.0 by chance, 82 beyond two against about 84.0 (chance simulated by replaying every game from its expected odds). The NHL lists the head coach for every game, so a mid-season change splits into two separate records.
| Coach | Teams | Official | G | W | Win % | Expected | z |
|---|---|---|---|---|---|---|---|
| John Tortorella | TBL, NYR, VAN, CBJ, PHI | Eric Furlatt | 90 | 28 | 31.1 | 49.0 | -3.51 |
| Lane Lambert | NYI, SEA | Michael Markovic | 15 | 1 | 6.7 | 47.5 | -3.21 |
| Lindy Ruff | NJD, BUF | Carter Sandlak | 13 | 2 | 15.4 | 54.7 | -2.95 |
| Dominique Ducharme | MTL | Graham Skilliter | 11 | 0 | 0.0 | 43.4 | -2.92 |
| Derek Lalonde | DET | Kendrick Nicholson | 13 | 1 | 7.7 | 46.5 | -2.85 |
| Patrick Roy | COL, NYI | Kendrick Nicholson | 16 | 2 | 12.5 | 46.5 | -2.79 |
| David Quinn | NYR, SJS | Thomas John Luxmore | 22 | 3 | 13.6 | 41.0 | -2.74 |
| Claude Julien | MTL, NJD, BOS | Dan O'Rourke | 85 | 34 | 40.0 | 54.0 | -2.66 |
| Spencer Carbery | WSH | Kyle Rehman | 10 | 1 | 10.0 | 50.1 | -2.57 |
| Travis Green | NJD, OTT | Carter Sandlak | 11 | 2 | 18.2 | 53.9 | -2.46 |
| Coach | Teams | Official | G | W | Win % | Expected | z |
|---|---|---|---|---|---|---|---|
| Craig Berube | STL, TOR | Trevor Hanson | 32 | 24 | 75.0 | 48.4 | 3.12 |
| Lane Lambert | NYI, SEA | Frederick L'Ecuyer | 11 | 9 | 81.8 | 41.3 | 2.80 |
| Bruce Cassidy | BOS, VGK | Trevor Hanson | 62 | 47 | 75.8 | 59.0 | 2.76 |
| Lindy Ruff | DAL, NJD, BUF | Thomas John Luxmore | 40 | 27 | 67.5 | 46.8 | 2.72 |
| John Hynes | NSH, MIN | Carter Sandlak | 17 | 15 | 88.2 | 56.1 | 2.72 |
| Dallas Eakins | ANA | Thomas John Luxmore | 24 | 14 | 58.3 | 32.9 | 2.71 |
| Mike Sullivan | PIT, NYR | Francis Charron | 56 | 38 | 67.9 | 50.8 | 2.62 |
| Sheldon Keefe | TOR, NJD | Cody Beach | 13 | 11 | 84.6 | 49.4 | 2.59 |
| Paul Maurice | TOR, CAR, WPG, FLA | Dan O'Rourke | 91 | 57 | 62.6 | 49.4 | 2.58 |
| David Quinn | NYR, SJS | Jon McIsaac | 22 | 15 | 68.2 | 42.8 | 2.58 |
Penalty margin against the team's own norm that season, for pairings with 20+ games. Negative means fewer whistles on the coach's team than usual with that official.
| Coach | Teams | Official | G | Penalties vs norm | PP margin |
|---|---|---|---|---|---|
| John Hynes | NJD, NSH, MIN | Chris Schlenker | 28 | -0.99 | 1.18 |
| Dave Tippett | ARI, EDM | Chris Schlenker | 21 | -0.94 | 1.00 |
| Jacques Lemaire | MIN, NJD | Dan O'Rourke | 32 | -0.90 | 1.16 |
| Andrew Brunette | FLA, NSH | Brandon Schrader | 21 | -0.90 | 0.95 |
| Glen Gulutzan | DAL, CGY | Francois St-Laurent | 23 | -0.81 | 0.57 |
| Guy Boucher | TBL, OTT | Chris Lee | 23 | -0.81 | 0.91 |
| John Stevens | PHI, LAK | Kelly Sutherland | 25 | 1.01 | -1.44 |
| Craig MacTavish | EDM | Dan O'Rourke | 21 | 0.98 | -0.95 |
| Marc Crawford | VAN, LAK, DAL | Gord Dwyer | 22 | 0.98 | -0.41 |
| Bob Boughner | FLA, SJS | Garrett Rank | 20 | 0.83 | -0.85 |
| Bob Boughner | FLA, SJS | Gord Dwyer | 23 | 0.82 | -0.70 |
| Rick Bowness | DAL, WPG, CBJ | Francois St-Laurent | 22 | 0.82 | -0.95 |
Nobody was flagged in advance. These are the officials whose whistle, home lean and home results sit furthest from the field, added together. Far from the field is not the same as wrong. A tight whistle is a style, and a home-results tilt over a few hundred games is usually luck. The cards say which is which.
1,726 games over 21 seasons (2005-10-05 to 2026-06-11), 232 in the playoffs.
| Season | Games | Home win % | Expected | Penalties/game | League |
|---|---|---|---|---|---|
| 2005-06 | 71 | 57.7% | 59.4% | 12.10 | 13.80 |
| 2006-07 | 79 | 58.2% | 55.6% | 10.30 | 12.00 |
| 2007-08 | 78 | 56.4% | 53.7% | 10.00 | 11.00 |
| 2008-09 | 78 | 59.0% | 56.3% | 9.50 | 10.80 |
| 2009-10 | 81 | 43.2% | 55.2% | 9.00 | 9.80 |
| 2010-11 | 81 | 50.6% | 52.3% | 9.10 | 9.20 |
| 2011-12 | 87 | 50.6% | 54.5% | 8.60 | 8.60 |
| 2012-13 | 59 | 55.9% | 57.9% | 7.70 | 8.80 |
| 2013-14 | 89 | 64.8% | 56.0% | 8.10 | 8.50 |
| 2014-15 | 92 | 50.0% | 55.5% | 7.00 | 7.90 |
| 2015-16 | 91 | 56.7% | 53.5% | 7.10 | 8.00 |
| 2016-17 | 91 | 64.8% | 57.4% | 8.50 | 7.50 |
| 2017-18 | 88 | 51.8% | 56.5% | 6.80 | 7.40 |
| 2018-19 | 87 | 50.6% | 55.1% | 6.10 | 7.10 |
| 2019-20 | 79 | 39.7% | 54.1% | 6.50 | 7.20 |
| 2020-21 | 56 | 46.3% | 54.9% | 6.20 | 6.90 |
| 2021-22 | 89 | 52.8% | 53.5% | 7.00 | 7.40 |
| 2022-23 | 91 | 50.5% | 51.4% | 6.60 | 7.60 |
| 2023-24 | 80 | 61.3% | 59.4% | 7.10 | 7.40 |
| 2024-25 | 88 | 62.5% | 55.6% | 7.30 | 6.80 |
| 2025-26 | 91 | 46.2% | 52.8% | 6.90 | 7.30 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Columbus Blue Jackets | 108 | 39 | 36.1% | 45.5% | -2.0 |
| St. Louis Blues | 107 | 44 | 41.1% | 50.1% | -1.9 |
| Boston Bruins | 136 | 67 | 49.3% | 57.1% | -1.9 |
| Atlanta Thrashers | 36 | 12 | 33.3% | 47.9% | -1.8 |
| Seattle Kraken | 24 | 7 | 29.2% | 45.6% | -1.7 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Philadelphia Flyers | 121 | 68 | 56.2% | 47.6% | +2.0 |
| Dallas Stars | 120 | 70 | 58.3% | 50.4% | +1.8 |
| Vancouver Canucks | 83 | 48 | 57.8% | 48.7% | +1.7 |
| Carolina Hurricanes | 135 | 80 | 59.3% | 52.6% | +1.6 |
| Calgary Flames | 88 | 49 | 55.7% | 48.6% | +1.4 |
863 games over 13 seasons (2013-10-24 to 2026-05-11), 53 in the playoffs.
| Season | Games | Home win % | Expected | Penalties/game | League |
|---|---|---|---|---|---|
| 2013-14 | 8 | 37.5% | 57.9% | 9.10 | 8.50 |
| 2014-15 | 19 | 47.4% | 52.5% | 9.40 | 7.80 |
| 2015-16 | 75 | 62.7% | 51.7% | 8.50 | 7.90 |
| 2016-17 | 79 | 55.7% | 54.9% | 7.90 | 7.60 |
| 2017-18 | 77 | 51.3% | 55.7% | 8.20 | 7.30 |
| 2018-19 | 79 | 63.3% | 53.0% | 7.20 | 7.00 |
| 2019-20 | 70 | 56.5% | 51.7% | 7.90 | 7.20 |
| 2020-21 | 50 | 48.0% | 52.0% | 6.60 | 6.90 |
| 2021-22 | 79 | 48.1% | 51.1% | 8.20 | 7.30 |
| 2022-23 | 86 | 51.2% | 51.7% | 8.00 | 7.50 |
| 2023-24 | 80 | 52.5% | 52.5% | 7.30 | 7.40 |
| 2024-25 | 78 | 57.1% | 55.9% | 7.70 | 6.80 |
| 2025-26 | 83 | 46.9% | 50.7% | 7.00 | 7.30 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| New York Islanders | 42 | 16 | 38.1% | 50.7% | -1.7 |
| New York Rangers | 45 | 18 | 40.0% | 51.3% | -1.6 |
| Utah Mammoth | 16 | 5 | 31.2% | 49.6% | -1.5 |
| Vancouver Canucks | 61 | 24 | 39.3% | 47.7% | -1.4 |
| Pittsburgh Penguins | 48 | 21 | 43.8% | 53.0% | -1.3 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| St. Louis Blues | 51 | 33 | 64.7% | 49.5% | +2.2 |
| Vegas Golden Knights | 61 | 43 | 70.5% | 59.0% | +1.9 |
| Toronto Maple Leafs | 50 | 32 | 64.0% | 52.1% | +1.7 |
| Washington Capitals | 47 | 32 | 68.1% | 58.4% | +1.4 |
| Chicago Blackhawks | 52 | 27 | 51.9% | 43.3% | +1.3 |
1,571 games over 21 seasons (2005-10-05 to 2026-06-11), 191 in the playoffs.
| Season | Games | Home win % | Expected | Penalties/game | League |
|---|---|---|---|---|---|
| 2005-06 | 72 | 48.6% | 55.6% | 15.50 | 13.70 |
| 2006-07 | 75 | 57.3% | 56.0% | 13.50 | 11.80 |
| 2007-08 | 77 | 67.5% | 55.8% | 13.10 | 10.80 |
| 2008-09 | 78 | 66.7% | 56.3% | 10.80 | 10.70 |
| 2009-10 | 78 | 57.1% | 55.3% | 10.40 | 9.80 |
| 2010-11 | 77 | 50.6% | 50.8% | 9.70 | 9.20 |
| 2011-12 | 89 | 51.7% | 54.9% | 10.50 | 8.50 |
| 2012-13 | 58 | 50.0% | 55.9% | 8.70 | 8.70 |
| 2013-14 | 82 | 61.7% | 54.7% | 9.50 | 8.40 |
| 2014-15 | 87 | 51.7% | 55.5% | 8.40 | 7.80 |
| 2015-16 | 78 | 56.4% | 51.9% | 7.40 | 8.00 |
| 2016-17 | 49 | 59.2% | 55.8% | 7.50 | 7.60 |
| 2017-18 | 85 | 56.2% | 56.1% | 7.80 | 7.30 |
| 2018-19 | 91 | 49.5% | 54.7% | 7.10 | 7.00 |
| 2019-20 | 78 | 55.7% | 54.2% | 8.50 | 7.10 |
| 2020-21 | 19 | 57.9% | 53.2% | 7.20 | 6.80 |
| 2021-22 | 88 | 48.3% | 51.9% | 8.30 | 7.30 |
| 2022-23 | 86 | 56.6% | 53.5% | 7.50 | 7.50 |
| 2023-24 | 90 | 46.7% | 52.4% | 7.40 | 7.40 |
| 2024-25 | 83 | 60.2% | 53.6% | 6.60 | 6.80 |
| 2025-26 | 51 | 43.1% | 53.3% | 7.30 | 7.30 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Ottawa Senators | 99 | 38 | 38.4% | 47.7% | -1.9 |
| Minnesota Wild | 93 | 38 | 40.9% | 50.0% | -1.8 |
| Winnipeg Jets | 55 | 22 | 40.0% | 51.7% | -1.8 |
| Detroit Red Wings | 101 | 43 | 42.6% | 48.6% | -1.3 |
| Nashville Predators | 101 | 47 | 46.5% | 52.3% | -1.2 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Columbus Blue Jackets | 98 | 56 | 57.1% | 45.4% | +2.4 |
| Phoenix Coyotes | 48 | 28 | 58.3% | 47.4% | +1.6 |
| Pittsburgh Penguins | 115 | 67 | 58.3% | 53.3% | +1.1 |
| New York Islanders | 98 | 50 | 51.0% | 45.8% | +1.1 |
| Dallas Stars | 112 | 64 | 57.1% | 52.6% | +1.0 |
256 games over 5 seasons (2022-03-22 to 2026-04-15), 0 in the playoffs.
| Season | Games | Home win % | Expected | Penalties/game | League |
|---|---|---|---|---|---|
| 2021-22 | 2 | 50.0% | 56.9% | 6.50 | 7.40 |
| 2022-23 | 34 | 58.8% | 48.7% | 5.90 | 7.60 |
| 2023-24 | 76 | 61.8% | 54.7% | 7.10 | 7.40 |
| 2024-25 | 73 | 38.4% | 54.2% | 6.10 | 6.90 |
| 2025-26 | 71 | 49.3% | 52.1% | 6.30 | 7.40 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| New Jersey Devils | 15 | 4 | 26.7% | 49.7% | -1.9 |
| Columbus Blue Jackets | 25 | 7 | 28.0% | 43.5% | -1.6 |
| San Jose Sharks | 21 | 4 | 19.0% | 31.2% | -1.3 |
| Ottawa Senators | 16 | 7 | 43.8% | 56.7% | -1.1 |
| Calgary Flames | 12 | 5 | 41.7% | 54.1% | -0.9 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Detroit Red Wings | 14 | 11 | 78.6% | 47.5% | +2.4 |
| Minnesota Wild | 18 | 15 | 83.3% | 56.9% | +2.3 |
| Florida Panthers | 16 | 13 | 81.2% | 55.1% | +2.2 |
| Dallas Stars | 17 | 13 | 76.5% | 61.0% | +1.4 |
| Edmonton Oilers | 16 | 12 | 75.0% | 62.5% | +1.1 |
212 games over 5 seasons (2022-04-12 to 2026-04-16), 0 in the playoffs.
| Season | Games | Home win % | Expected | Penalties/game | League |
|---|---|---|---|---|---|
| 2021-22 | 2 | 50.0% | 50.0% | 7.00 | 7.40 |
| 2022-23 | 22 | 54.5% | 51.9% | 8.50 | 7.50 |
| 2023-24 | 40 | 40.0% | 49.0% | 8.60 | 7.40 |
| 2024-25 | 73 | 50.7% | 56.3% | 7.60 | 6.80 |
| 2025-26 | 75 | 48.0% | 50.9% | 7.50 | 7.30 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Los Angeles Kings | 13 | 3 | 23.1% | 49.6% | -2.0 |
| Minnesota Wild | 13 | 4 | 30.8% | 56.7% | -1.9 |
| Columbus Blue Jackets | 13 | 4 | 30.8% | 45.0% | -1.0 |
| Carolina Hurricanes | 15 | 8 | 53.3% | 64.8% | -1.0 |
| San Jose Sharks | 17 | 5 | 29.4% | 36.2% | -0.6 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| New Jersey Devils | 14 | 11 | 78.6% | 47.8% | +2.3 |
| St. Louis Blues | 15 | 10 | 66.7% | 50.5% | +1.3 |
| Seattle Kraken | 17 | 10 | 58.8% | 44.2% | +1.2 |
| Edmonton Oilers | 21 | 15 | 71.4% | 59.2% | +1.2 |
| Vegas Golden Knights | 15 | 10 | 66.7% | 55.3% | +0.9 |
679 games over 11 seasons (2015-11-12 to 2026-04-29), 17 in the playoffs.
| Season | Games | Home win % | Expected | Penalties/game | League |
|---|---|---|---|---|---|
| 2015-16 | 3 | 100.0% | 55.0% | 9.70 | 7.90 |
| 2016-17 | 37 | 48.6% | 52.3% | 8.30 | 7.60 |
| 2017-18 | 73 | 50.7% | 56.5% | 7.40 | 7.40 |
| 2018-19 | 69 | 46.4% | 51.9% | 6.90 | 7.00 |
| 2019-20 | 64 | 54.7% | 50.9% | 7.30 | 7.20 |
| 2020-21 | 51 | 51.0% | 52.2% | 7.30 | 6.80 |
| 2021-22 | 79 | 54.4% | 51.5% | 8.20 | 7.30 |
| 2022-23 | 78 | 59.0% | 55.5% | 7.70 | 7.50 |
| 2023-24 | 68 | 47.0% | 52.3% | 8.50 | 7.40 |
| 2024-25 | 77 | 59.7% | 55.5% | 7.50 | 6.80 |
| 2025-26 | 80 | 57.5% | 50.5% | 7.90 | 7.30 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| New York Rangers | 39 | 13 | 33.3% | 54.9% | -2.8 |
| Calgary Flames | 32 | 12 | 37.5% | 50.7% | -1.5 |
| Los Angeles Kings | 54 | 21 | 38.9% | 48.4% | -1.4 |
| Ottawa Senators | 42 | 15 | 35.7% | 42.9% | -1.0 |
| San Jose Sharks | 48 | 16 | 33.3% | 39.0% | -0.8 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Minnesota Wild | 44 | 32 | 72.7% | 50.5% | +3.0 |
| Nashville Predators | 48 | 29 | 60.4% | 51.9% | +1.2 |
| Edmonton Oilers | 39 | 24 | 61.5% | 53.7% | +1.0 |
| Pittsburgh Penguins | 45 | 27 | 60.0% | 54.0% | +0.8 |
| Boston Bruins | 46 | 30 | 65.2% | 59.5% | +0.8 |
713 games had a power-play gap of 5 or more, 2.63 per 100 games. Officials with 150+ games, most frequent first, with the count you'd expect from their share of games.
| Official | Games | Lopsided games | Expected | Per 100 |
|---|---|---|---|---|
| Chris Rooney | 1571 | 61 | 41.30 | 3.88 |
| Gord Dwyer | 1520 | 43 | 39.90 | 2.83 |
| Dan O'Rourke | 1611 | 45 | 42.30 | 2.79 |
| Chris Lee | 1532 | 41 | 40.20 | 2.68 |
| Eric Furlatt | 1628 | 39 | 42.80 | 2.40 |
| Brian Pochmara | 1334 | 32 | 35.00 | 2.40 |
| Frederick L'Ecuyer | 1229 | 29 | 32.30 | 2.36 |
| Jake Brenk | 679 | 14 | 17.80 | 2.06 |
| Date | Away | Home | Away goals | Home goals | Home PP − away | Home penalties − away | Official | Official |
|---|---|---|---|---|---|---|---|---|
| 2008-12-16 | Montréal Canadiens | Carolina Hurricanes | 2 | 3 | +10 | -10 | Frederick L'Ecuyer | |
| 2007-12-11 | Pittsburgh Penguins | Philadelphia Flyers | 2 | 8 | +10 | -10 | Chris Rooney | |
| 2009-04-07 | New York Islanders | Carolina Hurricanes | 0 | 9 | +10 | -9 | Brian Pochmara | |
| 2006-04-24 | Philadelphia Flyers | Buffalo Sabres | 2 | 8 | +9 | -8 | ||
| 2005-10-14 | Toronto Maple Leafs | Atlanta Thrashers | 9 | 1 | -9 | +7 | Dan O'Rourke | |
| 2006-03-19 | Detroit Red Wings | Vancouver Canucks | 7 | 3 | -9 | +9 | Chris Rooney | |
| 2015-12-22 | Vancouver Canucks | Tampa Bay Lightning | 2 | 1 | +9 | -9 | Frederick L'Ecuyer | |
| 2008-04-04 | Florida Panthers | Carolina Hurricanes | 4 | 3 | +9 | -9 | ||
| 2005-12-23 | Florida Panthers | Carolina Hurricanes | 3 | 4 | +8 | -8 | Francois St-Laurent | |
| 2005-12-28 | Washington Capitals | New Jersey Devils | 2 | 7 | +8 | -8 |
Penalties, power plays and home-ice advantage season by season, with the number of officials who worked at least one game on the NHL feed.
| Season | Games | Officials | Penalties/game | PP/game | Home win (true home) | OT or shootout |
|---|---|---|---|---|---|---|
| 2005-06 | 1313 | 40 | 13.8 | 11.7 | 57.2% | 22.9% |
| 2006-07 | 1311 | 38 | 11.9 | 9.7 | 55.1% | 22.7% |
| 2007-08 | 1315 | 40 | 10.9 | 8.6 | 54.0% | 21.9% |
| 2008-09 | 1317 | 39 | 10.7 | 8.3 | 56.2% | 22.6% |
| 2009-10 | 1319 | 38 | 9.8 | 7.5 | 55.9% | 24.2% |
| 2010-11 | 1319 | 38 | 9.2 | 7.1 | 52.2% | 24.2% |
| 2011-12 | 1316 | 37 | 8.6 | 6.7 | 55.1% | 24.7% |
| 2012-13 | 806 | 37 | 8.7 | 6.7 | 58.1% | 23.4% |
| 2013-14 | 1323 | 43 | 8.5 | 6.6 | 54.3% | 25.2% |
| 2014-15 | 1319 | 42 | 7.8 | 6.1 | 54.4% | 24.6% |
| 2015-16 | 1320 | 40 | 7.9 | 6.2 | 52.8% | 22.3% |
| 2016-17 | 1317 | 42 | 7.6 | 6.0 | 55.8% | 24.0% |
| 2017-18 | 1355 | 43 | 7.4 | 6.1 | 55.9% | 22.6% |
| 2018-19 | 1358 | 44 | 7.0 | 5.9 | 53.6% | 21.2% |
| 2019-20 | 1202 | 42 | 7.2 | 6.1 | 53.2% | 23.0% |
| 2020-21 | 952 | 42 | 6.9 | 5.8 | 53.4% | 23.3% |
| 2021-22 | 1399 | 45 | 7.4 | 5.8 | 54.2% | 21.6% |
| 2022-23 | 1399 | 43 | 7.5 | 6.1 | 51.9% | 23.2% |
| 2023-24 | 1400 | 43 | 7.4 | 6.0 | 53.7% | 20.6% |
| 2024-25 | 1398 | 44 | 6.8 | 5.5 | 56.5% | 20.8% |
| 2025-26 | 1393 | 43 | 7.3 | 5.8 | 51.9% | 24.9% |
Source: The NHL's public game feed (play-by-play and game information), pulled game by game for every NHL game from 2005-06 to 2025-26 (27,151 games with at least two officials listed; games with no crew on record are left out of referee tables). Referees and head coaches come from the game information block. Penalties come from the play-by-play: minors, bench minors, majors, match penalties and penalty shots count as penalties; 10-minute and game misconducts are counted separately. Power plays are the NHL's own power-play opportunities. Shootout wins count as wins, as in the standings.
Baselines: home-win rate is compared with a matchup expectation (both teams' records that season without that game, log5, shifted to the league home rate in the same season), true home games only; neutral-site games are excluded from every home-ice number. Team and coach pairings use the same matchup expectation, game by game.
What the method can't see: which of the two referees made a call, or whether a call was right. It measures tendencies and outcomes. Both referees are on record for every NHL game from 2005-06 on; linesmen are left out because they rarely call penalties.
WNBA
Every WNBA game on ESPN's feed from 2004 to 2026, every official on the floor, every box score. Referees, the teams and players in front of them, and where the numbers are flat.
No letter grades for WNBA officials. Only 29 have enough games to measure how much a category repeats, and at that size most categories come out as luck: two of them repeat less than chance. Whistle (fouls per game against the league) is the one number that describes a WNBA official reliably, and it is in the Referees tab.
For every official with 150+ games I split the games into odd and even (by date), work out each category on both halves, and check how well one half predicts the other across officials. The first number is that correlation; the second projects it to a full career. Near 1 means the category describes the official; near 0 means it is luck. The rule: a category counts toward the grade only if the full-career number is at least 0.2 in the NBA or college game. Close games (home wins in games decided by three or fewer) and the half-to-half foul gap fail it and are shown here but left out of the grade. All three officials on the floor share each game, so even the categories that repeat describe a crew habit as much as a person.
| Category | Odd vs even | Full career | Officials | In the grade |
|---|---|---|---|---|
| Whistle | +0.88 | +0.94 | 29 | not graded |
| Home lean | +0.03 | +0.06 | 29 | not graded |
| Home results | +0.18 | +0.30 | 29 | not graded |
| Free-throw lean | -0.32 | -0.92 | 29 | not graded |
| Technicals | +0.03 | +0.07 | 29 | not graded |
| Close games | +0.25 | +0.39 | 27 | not graded |
| Consistency: night spread | -0.16 | -0.40 | 29 | not graded |
| Consistency: lopsided nights | +0.08 | +0.15 | 29 | not graded |
0 is dead center.
Total fouls called per game with the official on the floor, against the league average in the same seasons. Officials with 100+ games. This is the strongest signal in the data. It’s stable and it’s large, and it says nothing about fairness, only about how much contact gets called.
| Official | Games | Fouls/game | vs league | z |
|---|---|---|---|---|
| Kevin Fahy | 196 | 37.80 | +2.14 | +4.3 |
| Ryan Sassano | 116 | 37.90 | +1.80 | +2.9 |
| Charles Watson | 120 | 37.40 | +1.39 | +2.3 |
| Toni Patillo | 153 | 37.20 | +1.34 | +2.5 |
| Fatou Cissoko-Stephens | 267 | 36.70 | +0.84 | +2.0 |
| Roy Gulbeyan | 679 | 37.50 | +0.75 | +2.8 |
| Angel Kent | 116 | 36.60 | +0.59 | +0.9 |
| Blanca Burns | 109 | 36.30 | +0.50 | +0.7 |
| Randy Richardson | 265 | 36.20 | +0.44 | +1.1 |
| Clare Simmons | 111 | 36.30 | +0.19 | +0.3 |
| Official | Games | Fouls/game | vs league | z |
|---|---|---|---|---|
| Tim Greene | 384 | 35.10 | -1.10 | -3.1 |
| Amy Bonner | 401 | 36.10 | -0.87 | -2.4 |
| Agon Abazi | 101 | 35.80 | -0.81 | -1.2 |
| Maj Forsberg | 472 | 35.80 | -0.60 | -1.9 |
| Angelica Suffren | 261 | 36.10 | -0.34 | -0.9 |
| Ashley Gloss | 110 | 35.80 | -0.30 | -0.5 |
| Tiara Cruse | 305 | 36.00 | -0.20 | -0.5 |
| Isaac Barnett | 304 | 35.80 | -0.14 | -0.4 |
| Clare Simmons | 111 | 36.30 | +0.19 | +0.3 |
| Randy Richardson | 265 | 36.20 | +0.44 | +1.1 |
Home fouls minus away fouls in true home games, against the league lean in the same seasons (home teams get called for slightly fewer fouls everywhere). Negative means the official calls even fewer on the home team than the rest of the field does; positive means the visitor gets the friendlier whistle.
| Official | Games | Home fouls − away | vs league | z | Home FTA edge vs league |
|---|---|---|---|---|---|
| Toni Patillo | 153 | -1.27 | -0.66 | -1.7 | +0.17 |
| Maj Forsberg | 472 | -1.24 | -0.32 | -1.4 | +0.63 |
| Tiara Cruse | 305 | -1.09 | -0.34 | -1.2 | +0.35 |
| Kevin Fahy | 196 | -0.90 | -0.34 | -1.0 | +0.27 |
| Ryan Sassano | 116 | -0.86 | -0.36 | -1.0 | +0.48 |
| Randy Richardson | 265 | -0.71 | -0.06 | -0.2 | +0.27 |
| Angel Kent | 116 | -0.60 | -0.04 | -0.1 | -0.10 |
| Clare Simmons | 111 | -0.40 | +0.18 | +0.4 | -0.05 |
| Official | Games | Home fouls − away | vs league | z | Home FTA edge vs league |
|---|---|---|---|---|---|
| Roy Gulbeyan | 679 | -0.59 | +0.64 | +3.5 | -0.88 |
| Ashley Gloss | 110 | +0.35 | +1.01 | +2.5 | -1.33 |
| Agon Abazi | 101 | +0.37 | +1.01 | +2.1 | -1.12 |
| Angelica Suffren | 261 | -0.61 | +0.37 | +1.2 | -1.04 |
| Charles Watson | 120 | -0.16 | +0.44 | +1.1 | +0.10 |
| Amy Bonner | 401 | -0.92 | +0.22 | +1.0 | -0.07 |
| Blanca Burns | 109 | -0.28 | +0.34 | +0.8 | -0.17 |
| Tim Greene | 384 | -0.68 | +0.19 | +0.7 | -0.43 |
Technical fouls per game as ESPN logs them for both teams combined. ESPN's box scores are thin on this field in some seasons, so treat it as a lower bound.
| Official | Games | Techs/game |
|---|---|---|
| Roy Gulbeyan | 679 | 1.02 |
| Tiara Cruse | 305 | 1.01 |
| Agon Abazi | 101 | 0.99 |
| Clare Simmons | 111 | 0.95 |
| Fatou Cissoko-Stephens | 267 | 0.95 |
| Kevin Fahy | 196 | 0.91 |
| Blanca Burns | 109 | 0.90 |
| Toni Patillo | 153 | 0.90 |
Home-team win rate in true home games with the official on the floor, against what the matchup predicts: both teams’ records that season (not counting that game), adjusted to the league home rate that season. That keeps a run of early-season home games against weak visitors from looking like a friendly whistle. Across 18 officials with 100+ games, chance alone would put about 1 beyond ±2 and about 0.0 beyond ±3; the count here is 0 and 0.
| Official | Home games | Home win % | Expected | z |
|---|---|---|---|---|
| Angel Kent | 116 | 60.3% | 54.4% | +1.4 |
| Randy Richardson | 243 | 58.4% | 54.6% | +1.3 |
| Tiara Cruse | 281 | 59.4% | 56.4% | +1.1 |
| Fatou Cissoko-Stephens | 244 | 57.4% | 54.5% | +1.0 |
| Maj Forsberg | 443 | 60.0% | 58.2% | +0.9 |
| Ryan Sassano | 116 | 59.5% | 56.5% | +0.7 |
| Kevin Fahy | 186 | 57.0% | 55.0% | +0.6 |
| Charles Watson | 119 | 55.5% | 54.0% | +0.4 |
| Official | Home games | Home win % | Expected | z |
|---|---|---|---|---|
| Isaac Barnett | 283 | 51.6% | 56.5% | -1.8 |
| Angelica Suffren | 261 | 51.3% | 55.8% | -1.6 |
| Agon Abazi | 101 | 46.5% | 53.4% | -1.6 |
| Clare Simmons | 111 | 48.6% | 52.8% | -1.0 |
| Toni Patillo | 152 | 52.6% | 55.9% | -0.9 |
| Roy Gulbeyan | 651 | 57.1% | 58.7% | -0.9 |
| Blanca Burns | 108 | 51.9% | 55.4% | -0.8 |
| Tim Greene | 359 | 54.6% | 55.8% | -0.5 |
Games where the matchup made one team at least a 70% favorite, judged from both teams' strength that season (scoring margins against who they played) and where the game was played. League-wide, underdogs won 19.2% of 2,523 such games, against 19.2% expected. Each official is measured against the upsets expected in that official's own games, not a league average. Officials with 50+ of them, most underdog wins first. 21 officials: 2 beyond ±2 (chance: about 1), 0 beyond ±3 (chance: about 0.1).
| Official | Games | Underdog wins | Expected | z |
|---|---|---|---|---|
| Isaac Barnett | 161 | 25.5% | 18.3% | +2.4 |
| Teresa Stuck | 53 | 28.3% | 17.7% | +2.1 |
| Clare Simmons | 71 | 25.4% | 16.7% | +2.0 |
| Angelica Suffren | 147 | 23.8% | 17.7% | +2.0 |
| Kevin Fahy | 114 | 24.6% | 18.7% | +1.6 |
| Tim Greene | 218 | 22.5% | 19.3% | +1.2 |
| Ashley Gloss | 61 | 23.0% | 18.4% | +0.9 |
| Marcy Williams | 61 | 21.3% | 18.0% | +0.7 |
| Amy Bonner | 214 | 16.4% | 20.2% | -1.4 |
| Ryan Sassano | 69 | 13.0% | 17.3% | -1.0 |
| Tiara Cruse | 173 | 16.2% | 19.0% | -0.9 |
| Agon Abazi | 56 | 16.1% | 18.3% | -0.4 |
| Blanca Burns | 65 | 16.9% | 18.5% | -0.3 |
| Roy Gulbeyan | 324 | 19.1% | 19.7% | -0.3 |
| Randy Richardson | 146 | 18.5% | 18.8% | -0.1 |
| Maj Forsberg | 254 | 19.7% | 19.8% | -0.0 |
Home-team win rate in true home games decided by three points or fewer, officials with 200+ games.
| Official | Games | Close games | Home wins in them |
|---|---|---|---|
| Tim Greene | 384 | 14.1% | 39.6% |
| Angelica Suffren | 261 | 14.6% | 44.7% |
| Maj Forsberg | 472 | 12.3% | 47.2% |
| Roy Gulbeyan | 679 | 19.3% | 48.4% |
| Randy Richardson | 265 | 12.5% | 63.3% |
| Isaac Barnett | 304 | 12.8% | 55.6% |
| Fatou Cissoko-Stephens | 267 | 16.5% | 54.8% |
| Tiara Cruse | 305 | 11.5% | 52.9% |
Every pairing with 12+ games. Expected wins come from each game’s matchup (both teams’ records that season without that game, and the home floor), so a strong team is expected to win and drawing the big games is not held against anyone; the question is only whether the team did better or worse than that with this official. 273 pairings; 17 beyond ±2 (chance: about 12.0), 2 beyond ±3 (chance: about 0.6).
| Official | Team | Games | Wins | Win % | Expected | z | Foul diff vs team norm |
|---|---|---|---|---|---|---|---|
| Teresa Stuck | Indiana Fever | 12 | 3 | 25.0% | 63.8% | -3.0 | +1.35 |
| Maj Forsberg | Los Angeles Sparks | 74 | 29 | 39.2% | 52.7% | -2.6 | +0.28 |
| Ashley Gloss | Dallas Wings | 20 | 5 | 25.0% | 49.7% | -2.5 | +0.13 |
| Ryan Sassano | Connecticut Sun | 16 | 2 | 12.5% | 38.0% | -2.4 | +0.31 |
| Gina Cross | Chicago Sky | 14 | 1 | 7.1% | 33.9% | -2.3 | +1.61 |
| Fatou Cissoko-Stephens | Atlanta Dream | 43 | 12 | 27.9% | 43.6% | -2.3 | +0.02 |
| Amy Bonner | Indiana Fever | 64 | 27 | 42.2% | 54.3% | -2.2 | +0.74 |
| Catherine Chang | Indiana Fever | 15 | 3 | 20.0% | 44.7% | -2.1 | -0.56 |
| Angel Kent | New York Liberty | 19 | 8 | 42.1% | 62.4% | -2.0 | -0.20 |
| Clare Simmons | Las Vegas Aces | 18 | 10 | 55.6% | 74.7% | -2.0 | +1.80 |
| Catherine Chang | Phoenix Mercury | 13 | 3 | 23.1% | 45.8% | -1.8 | -0.43 |
| Roy Gulbeyan | Chicago Sky | 92 | 35 | 38.0% | 46.8% | -1.8 | +0.17 |
| Official | Team | Games | Wins | Win % | Expected | z | Foul diff vs team norm |
|---|---|---|---|---|---|---|---|
| Kevin Fahy | Minnesota Lynx | 25 | 22 | 88.0% | 60.1% | +3.1 | +0.45 |
| Toni Patillo | Atlanta Dream | 24 | 17 | 70.8% | 47.4% | +2.6 | -0.07 |
| Maj Forsberg | Seattle Storm | 84 | 59 | 70.2% | 58.6% | +2.4 | -1.39 |
| Angel Kent | Washington Mystics | 17 | 12 | 70.6% | 45.4% | +2.3 | +0.22 |
| Ashley Gloss | Indiana Fever | 16 | 11 | 68.8% | 44.1% | +2.2 | -0.40 |
| Teresa Stuck | Washington Mystics | 16 | 12 | 75.0% | 51.8% | +2.1 | +1.35 |
| Randy Richardson | Los Angeles Sparks | 47 | 29 | 61.7% | 48.3% | +2.0 | +0.25 |
| Catherine Chang | Las Vegas Aces | 14 | 13 | 92.9% | 71.2% | +2.0 | +0.02 |
| Maj Forsberg | New York Liberty | 75 | 44 | 58.7% | 48.7% | +2.0 | -0.31 |
| Ashley Gloss | Connecticut Sun | 17 | 12 | 70.6% | 50.5% | +1.9 | -0.91 |
| Agon Abazi | Indiana Fever | 18 | 13 | 72.2% | 51.7% | +1.9 | +0.35 |
| Blanca Burns | Chicago Sky | 13 | 8 | 61.5% | 40.3% | +1.7 | -0.30 |
Pairings with 24+ games where the team's foul differential (own fouls minus opponent's) with this official differs most from the team's normal differential that season. Negative means the team got called for fewer fouls than usual with this official on the floor.
| Official | Team | Games | Foul diff | vs team norm | FTA diff |
|---|---|---|---|---|---|
| Kevin Fahy | Indiana Fever | 37 | -1.27 | -1.57 | +2.11 |
| Kevin Fahy | Los Angeles Sparks | 36 | -2.11 | -1.55 | +0.78 |
| Randy Richardson | Dallas Wings | 41 | -0.68 | -1.41 | +0.10 |
| Maj Forsberg | Seattle Storm | 84 | -1.24 | -1.39 | +1.58 |
| Fatou Cissoko-Stephens | Chicago Sky | 40 | -0.02 | -1.30 | +0.72 |
| Angelica Suffren | Phoenix Mercury | 32 | -0.53 | -1.09 | +1.72 |
| Toni Patillo | Dallas Wings | 24 | -0.04 | -1.03 | +1.38 |
| Tim Greene | New York Liberty | 79 | -0.44 | -1.01 | +1.35 |
| Toni Patillo | Seattle Storm | 25 | +2.04 | +1.62 | -3.56 |
| Maj Forsberg | Dallas Wings | 67 | +1.49 | +1.19 | -1.73 |
| Maj Forsberg | Connecticut Sun | 73 | +1.22 | +1.09 | -1.68 |
| Kevin Fahy | Atlanta Dream | 33 | +0.33 | +0.94 | +0.24 |
| Maj Forsberg | Atlanta Dream | 57 | +0.86 | +0.94 | -0.58 |
| Kevin Fahy | Phoenix Mercury | 29 | +0.97 | +0.93 | -1.07 |
| Tiara Cruse | Las Vegas Aces | 59 | -1.02 | +0.88 | +0.85 |
| Angelica Suffren | Indiana Fever | 40 | +0.92 | +0.87 | -4.00 |
Every player-official pairing with 12+ games of 8+ minutes (WNBA rosters are small and careers long, so these pairings run deep). Free throws, personal fouls and points are per 30 minutes and measured against the player's own rates in the same seasons. 2,378 pairings. Beyond ±3: free throws 8, fouls 6, points 7; chance alone would give about 6.4 on each.
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Maj Forsberg | Elena Delle Donne | WSH | 41 | 3.40 | 4.89 | -3.1 | 1.61 | 1.61 | -0.0 |
| Amy Bonner | Shenise Johnson | MIN | 21 | 0.70 | 2.36 | -2.8 | 2.32 | 2.15 | +0.4 |
| Kevin Fahy | Jewell Loyd | LV | 33 | 2.74 | 3.89 | -2.8 | 2.18 | 1.84 | +1.7 |
| Maj Forsberg | Kiah Stokes | LV | 49 | 0.30 | 0.97 | -2.7 | 3.18 | 2.99 | +0.6 |
| Catherine Chang | Lexie Hull | IND | 13 | 0.00 | 1.18 | -2.6 | 4.71 | 3.94 | +1.2 |
| Maj Forsberg | Amanda Zahui B. | LA | 19 | 0.71 | 2.40 | -2.6 | 5.64 | 5.16 | +0.7 |
| Randy Richardson | Crystal Langhorne | SEA | 13 | 0.26 | 2.06 | -2.5 | 1.96 | 2.00 | -0.1 |
| Randy Richardson | Sydney Colson | LV | 13 | 0.18 | 1.96 | -2.4 | 3.68 | 3.52 | +0.2 |
| Blanca Burns | Teaira McCowan | DAL | 12 | 2.01 | 4.35 | -2.4 | 4.96 | 4.21 | +1.3 |
| Angelica Suffren | Allisha Gray | DAL | 24 | 3.04 | 4.30 | -2.4 | 2.42 | 2.36 | +0.2 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Kelly Broomfield | Diamond Miller | CON | 12 | 7.61 | 3.44 | +4.1 | 4.18 | 4.28 | -0.1 |
| Angelica Suffren | Kiah Stokes | GS | 21 | 1.59 | 0.59 | +3.4 | 2.26 | 2.78 | -1.1 |
| Fatou Cissoko-Stephens | Natisha Hiedeman | MIN | 41 | 3.25 | 2.02 | +3.3 | 2.69 | 2.51 | +0.6 |
| Toni Patillo | Kia Nurse | CHI | 17 | 3.58 | 1.89 | +3.3 | 2.34 | 2.87 | -1.2 |
| Roy Gulbeyan | Jackie Young | LV | 58 | 4.00 | 2.95 | +3.2 | 2.37 | 2.14 | +1.4 |
| Catherine Chang | Gabby Williams | GS | 12 | 4.74 | 2.46 | +3.2 | 0.84 | 1.48 | -1.6 |
| Kevin Fahy | Napheesa Collier | MIN | 17 | 6.19 | 4.00 | +3.2 | 2.85 | 2.31 | +1.7 |
| Tiara Cruse | Monique Currie | WSH | 14 | 6.57 | 3.54 | +2.9 | 3.95 | 3.60 | +0.6 |
| Marcy Williams | Ezi Magbegor | SEA | 14 | 3.89 | 2.31 | +2.9 | 3.33 | 3.26 | +0.2 |
| Angelica Suffren | Jonquel Jones | CON | 30 | 4.71 | 3.30 | +2.9 | 2.81 | 3.08 | -0.9 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Roy Gulbeyan | Krystal Thomas | WSH | 19 | 1.21 | 1.91 | -1.3 | 7.31 | 4.98 | +3.7 |
| Fatou Cissoko-Stephens | Kamilla Cardoso | CHI | 12 | 3.84 | 4.16 | -0.3 | 5.68 | 3.87 | +3.1 |
| Clare Simmons | Chelsea Gray | LV | 17 | 2.73 | 2.07 | +1.2 | 2.63 | 1.75 | +3.1 |
| Roy Gulbeyan | Temeka Johnson | SEA | 39 | 2.42 | 2.11 | +0.8 | 3.56 | 2.69 | +3.1 |
| Maj Forsberg | Jantel Lavender | SEA | 43 | 1.06 | 1.58 | -1.6 | 3.33 | 2.43 | +3.0 |
| Charles Watson | Lindsay Allen | CON | 12 | 2.16 | 1.66 | +0.7 | 4.16 | 2.41 | +3.0 |
| Agon Abazi | Courtney Williams | MIN | 12 | 1.54 | 1.09 | +1.1 | 3.32 | 2.07 | +2.9 |
| Tiara Cruse | Shenise Johnson | IND | 13 | 2.63 | 3.09 | -0.6 | 4.10 | 2.45 | +2.9 |
| Maj Forsberg | Kayla Thornton | NY | 45 | 1.63 | 1.87 | -0.7 | 3.68 | 2.81 | +2.8 |
| Ryan Sassano | Brittney Sykes | SEA | 14 | 4.75 | 4.76 | -0.0 | 3.73 | 2.56 | +2.8 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Maj Forsberg | A'ja Wilson | LV | 51 | 6.63 | 6.18 | +1.0 | 1.28 | 1.84 | -3.1 |
| Tim Greene | Mercedes Russell | SEA | 22 | 1.90 | 2.12 | -0.4 | 1.22 | 2.74 | -2.9 |
| Gina Cross | Brittney Griner | ATL | 13 | 2.68 | 3.79 | -1.5 | 1.20 | 2.37 | -2.7 |
| Maj Forsberg | Awak Kuier | DAL | 13 | 1.83 | 1.83 | 0.0 | 0.44 | 1.89 | -2.7 |
| Amy Bonner | Lexie Hull | IND | 21 | 1.54 | 1.54 | +0.0 | 2.57 | 3.83 | -2.6 |
| Amy Bonner | Tina Charles | NY | 50 | 3.29 | 3.53 | -0.6 | 1.62 | 2.07 | -2.6 |
| Amy Bonner | Briann January | IND | 28 | 2.93 | 2.60 | +0.7 | 1.95 | 2.88 | -2.5 |
| Angelica Suffren | Brittney Sykes | WSH | 21 | 3.95 | 3.88 | +0.1 | 1.79 | 2.56 | -2.4 |
| Amy Bonner | Jackie Young | LV | 37 | 2.17 | 3.03 | -2.2 | 1.72 | 2.23 | -2.4 |
| Isaac Barnett | Brittney Griner | PHX | 31 | 4.18 | 4.07 | +0.2 | 1.93 | 2.60 | -2.4 |
WNBA officials work for the league, which sets the schedule and books the travel, so every mile here is one the league chose. This section rebuilds every official's itinerary from the venues on record: each hop between consecutive games is measured city to city, a hop of 300 miles or less counts as a drive, anything longer counts as a flight. 97.2% of games could be placed on the map (4,626). Miles are straight-line, so real road miles run higher. Two games in two cities on the same day cannot both be right, so those hops are dropped.
Across everyone on record that is 5,250,915 miles, 5,173,545 of them in the air over 4,219 flights. The typical official with 100+ games covers 22,851 miles a season, 18.5 flights, about 1,056 miles per trip between games.
Per season, straight-line, every hop between games counted.
| Official | Seasons | Miles/season | Air miles | Ground miles | Flights/season | Hard trips | Longest hop | Most common city |
|---|---|---|---|---|---|---|---|---|
| Roy Gulbeyan | 23 | 29,147 | 661,282 | 9,077 | 23.90 | 43 | 2,569 | Seattle, WA |
| Agon Abazi | 4 | 29,085 | 115,230 | 1,109 | 22.80 | 8 | 2,569 | Chicago, IL |
| Randy Richardson | 10 | 27,201 | 268,408 | 3,597 | 21.10 | 19 | 2,651 | Las Vegas, NV |
| Ryan Sassano | 5 | 26,504 | 130,122 | 2,401 | 19.80 | 7 | 2,466 | Brooklyn, NY |
| Isaac Barnett | 11 | 26,470 | 285,889 | 5,286 | 21.60 | 15 | 2,569 | Uncasville, CT |
| Charles Watson | 5 | 26,403 | 131,088 | 925 | 21.40 | 11 | 2,651 | Indianapolis, IN |
| Angel Kent | 5 | 25,273 | 124,689 | 1,675 | 20.20 | 8 | 2,569 | Arlington, TX |
| Maj Forsberg | 19 | 24,807 | 464,789 | 6,541 | 19.50 | 22 | 2,541 | Seattle, WA |
| Kevin Fahy | 9 | 23,265 | 206,680 | 2,701 | 18.00 | 11 | 2,547 | Arlington, TX |
| Tim Greene | 17 | 22,437 | 377,275 | 4,158 | 18.10 | 19 | 2,591 | Uncasville, CT |
| Clare Simmons | 5 | 21,175 | 104,325 | 1,549 | 18.80 | 5 | 2,541 | Minneapolis, MN |
| Tiara Cruse | 14 | 20,962 | 288,317 | 5,158 | 16.60 | 9 | 2,466 | Washington, DC |
Fewest miles per game among the busiest officials. These are the ones the league can send on a day's notice.
| Official | G | Miles/game | Miles/season | Flights/season | Most common city |
|---|---|---|---|---|---|
| Amy Bonner | 397 | 932.1 | 18,543 | 16.40 | Indianapolis, IN |
| Isaac Barnett | 304 | 993.8 | 26,470 | 21.60 | Uncasville, CT |
| Tiara Cruse | 305 | 1008.5 | 20,962 | 16.60 | Washington, DC |
| Fatou Cissoko-Stephens | 267 | 1029.4 | 20,112 | 15.70 | Uncasville, CT |
| Tim Greene | 384 | 1039.3 | 22,437 | 18.10 | Uncasville, CT |
| Angelica Suffren | 253 | 1041.5 | 17,779 | 14.80 | Chicago, IL |
| Roy Gulbeyan | 664 | 1045.8 | 29,147 | 23.90 | Seattle, WA |
| Maj Forsberg | 469 | 1047.4 | 24,807 | 19.50 | Seattle, WA |
A hard trip is a flight on no rest: more than 300 miles between a game one day and a game the next. On 310 occasions an official worked a game straight off one (5.88% of all assignments on the map). The officials who take the most of them, then the single worst ones.
| Official | G | Hard trips | 3 cities in 3 days | Flights/season | Heaviest week (mi) |
|---|---|---|---|---|---|
| Roy Gulbeyan | 664 | 43 | 0 | 23.90 | 6,882 |
| Maj Forsberg | 469 | 22 | 0 | 19.50 | 5,888 |
| Tim Greene | 384 | 19 | 0 | 18.10 | 6,769 |
| Randy Richardson | 265 | 19 | 0 | 21.10 | 7,426 |
| Amy Bonner | 397 | 17 | 0 | 16.40 | 6,923 |
| Isaac Barnett | 304 | 15 | 0 | 21.60 | 6,034 |
| Fatou Cissoko-Stephens | 267 | 12 | 0 | 15.70 | 5,902 |
| Kevin Fahy | 196 | 11 | 0 | 18.00 | 6,667 |
| Ashley Gloss | 110 | 11 | 0 | 15.70 | 6,035 |
| Charles Watson | 120 | 11 | 0 | 21.40 | 5,525 |
| Official | Date | From | To | Miles | Game | Fouls |
|---|---|---|---|---|---|---|
| Sarah Williams | 2025-07-26 | San Francisco, CA | Washington, DC | 2,434 | Seattle Storm at Washington Mystics | 32 |
| Gina Cross | 2026-05-31 | Toronto, ON | San Francisco, CA | 2,263 | Las Vegas Aces at Golden State Valkyries | 39 |
| Ryan Johnson | 2026-06-08 | Toronto, ON | Las Vegas, NV | 1,948 | Seattle Storm at Las Vegas Aces | 36 |
| Tim Greene | 2025-07-10 | Indianapolis, IN | Los Angeles, CA | 1,806 | Minnesota Lynx at Los Angeles Sparks | 26 |
| Agon Abazi | 2026-08-09 | Chicago, IL | Los Angeles, CA | 1,742 | Golden State Valkyries at Los Angeles Sparks | 33 |
| Angelica Suffren | 2025-07-13 | Chicago, IL | Seattle, WA | 1,733 | Washington Mystics at Seattle Storm | 40 |
| Kevin Fahy | 2026-07-20 | Arlington, TX | Seattle, WA | 1,669 | Minnesota Lynx at Seattle Storm | 33 |
| Blanca Burns | 2026-08-15 | Indianapolis, IN | Las Vegas, NV | 1,591 | Minnesota Lynx at Las Vegas Aces | 36 |
| Ashley Gloss | 2025-08-22 | College Park, GA | Phoenix, AZ | 1,586 | Golden State Valkyries at Phoenix Mercury | 33 |
| Tim Greene | 2025-08-17 | Minneapolis, MN | San Francisco, CA | 1,580 | Atlanta Dream at Golden State Valkyries | 32 |
The heaviest three-day stretches on record: three games, three different cities, the miles across both hops.
| Official | Date | From | To | Miles | Game | Fouls |
|---|
Each official compared with themselves: the games they worked straight off a flight on no rest against their other games. Fouls per game the whistle moves -1.32 fouls after a hard trip against the same official's other games (27 officials), unlikely to be chance on its own (p = 0.016), though it is one of four tests here, so treat it as a lead rather than a finding. Home foul lean the lean moves +0.06 fouls after a hard trip against the same official's other games (27 officials), inside what chance would give (p = 0.85). Home results the home side moves +0.05 wins per game after a hard trip against the same official's other games (27 officials), inside what chance would give (p = 0.15). Technicals technicals moves -0.06 per game after a hard trip against the same official's other games (27 officials), inside what chance would give (p = 0.42).
League-wide, by rest and by how the official got there (stay means the same city as the last game):
| Rest | Got there by | G | Fouls/G | vs league | Techs/G |
|---|---|---|---|---|---|
| 2-3 days | drive | 199 | 36.48 | +0.27 | 0.95 |
| 2-3 days | fly | 1847 | 36.47 | +0.16 | 0.89 |
| 2-3 days | stay | 140 | 34.88 | -0.68 | 0.61 |
| 4+ days | drive | 160 | 36.80 | +0.49 | 0.98 |
| 4+ days | fly | 2152 | 36.25 | -0.15 | 0.87 |
| 4+ days | stay | 120 | 34.99 | -1.09 | 0.76 |
| no rest | drive | 74 | 38.72 | +2.14 | 0.92 |
| no rest | fly | 310 | 36.83 | -0.21 | 0.83 |
| no rest | stay | 42 | 35.76 | +0.09 | 0.60 |
55 officials in this study also turn up in women's college (matched by name, since ESPN gives officials no ID). 17 of them have 100+ W games and 150+ women's college games, enough to compare the same person in both. Their whistles track each other (correlation 0.67), so style travels with the official. But the W squeezes it: the spread of those same officials is 1.39 fouls a game in college and 1.13 in the W, about 81% as wide. The same people sit closer to the middle in the W. One league office setting one standard would do that, though the data can only show the pattern, not the reason. The loosest college whistle among them, Brenda Pantoja at -4.4, is -1.9 in the W; the tightest, Angel Kent at +2.0, is +0.6.
Loosest college whistle first. Whistle is fouls per game against each league's own average; home lean is home fouls minus away fouls against each league's lean.
| Official | NCAAW G | NCAAW whistle | NCAAW home lean | W G | W whistle | W home lean |
|---|---|---|---|---|---|---|
| Brenda Pantoja | 799 | -4.41 | +0.31 | 116 | -1.86 | +0.31 |
| Michael Price | 723 | -2.16 | +0.40 | 491 | -2.52 | -0.16 |
| Daryl Humphrey | 333 | -2.13 | +1.09 | 368 | +0.06 | +0.56 |
| Tim Greene | 454 | -1.87 | -0.38 | 384 | -1.10 | +0.19 |
| Billy Smith | 796 | -1.45 | -0.87 | 291 | -0.91 | +0.19 |
| Ashley Gloss | 241 | -1.41 | -0.36 | 110 | -0.30 | +1.01 |
| Clarke Stevens | 249 | -1.27 | -0.16 | 160 | +0.28 | -0.06 |
| Jeffrey Smith | 691 | -1.22 | -0.05 | 498 | -0.26 | -0.18 |
| Denise Brooks | 764 | -0.59 | -0.12 | 292 | -1.49 | -0.16 |
| Tiara Cruse | 789 | -0.58 | -0.10 | 305 | -0.20 | -0.34 |
| Che Flores | 269 | -0.53 | +0.46 | 232 | -0.14 | +0.28 |
| Amy Bonner | 464 | -0.20 | +0.42 | 401 | -0.87 | +0.22 |
| Tiffany Bird | 718 | -0.17 | +0.31 | 152 | +1.15 | +0.02 |
| Fatou Cissoko-Stephens | 754 | +0.08 | -0.27 | 267 | +0.84 | +0.19 |
| Sue Blauch | 347 | +0.10 | -1.17 | 390 | +0.32 | -0.61 |
| Kevin Sparrock | 364 | +0.56 | +0.27 | 241 | +2.00 | +0.16 |
| Angel Kent | 346 | +2.01 | -0.76 | 116 | +0.59 | -0.04 |
55 officials work both the W and women’s college. 38 of them have worked both in the same calendar year, which means a W season from May into September or October and a college season from November into April, with the travel of both. Put on one calendar, the typical one of them works 55 games a year and covers 42,668 straight-line miles.
The turnaround is the gap between an official’s last W game and first college game that fall. Across 158 official-years it has a median of 58 days; 0 were under two weeks and 0 under one.
| Official | Years in both | Games/yr | Miles/yr | Flights/yr | Heaviest year (mi) | Hard trips (career) | Turnaround (days) |
|---|---|---|---|---|---|---|---|
| Michael Price | 10 | 84.4 | 69,698 | 65.5 | 107,347 | 245 | 32 |
| Teresa Stuck | 4 | 90.5 | 64,726 | 66.2 | 73,400 | 168 | 55 |
| Angelica Suffren | 2 | 77.5 | 63,710 | 60.5 | 64,148 | 47 | 29 |
| Denise Brooks | 3 | 91.0 | 62,865 | 69.3 | 77,753 | 269 | 30 |
| Sue Blauch | 6 | 88.2 | 58,431 | 66.3 | 76,635 | 124 | 37 |
| Billy Smith | 10 | 84.1 | 56,196 | 60.5 | 68,742 | 231 | 52 |
| Tiara Cruse | 14 | 78.1 | 54,353 | 56.0 | 88,383 | 259 | 52 |
| Jeffrey Smith | 11 | 81.3 | 54,185 | 54.1 | 82,234 | 215 | 58 |
| Brenda Pantoja | 3 | 61.3 | 52,064 | 50.7 | 67,619 | 276 | 55 |
| Fatou Cissoko-Stephens | 13 | 77.3 | 51,817 | 52.1 | 104,938 | 256 | 62 |
| Official | Year | Last W game | First college game | Days off |
|---|---|---|---|---|
| Tim Greene | 2024 | 2024-10-20 | 2024-11-04 | 15 |
| Tiara Cruse | 2024 | 2024-10-18 | 2024-11-04 | 17 |
| Michael Price | 2023 | 2023-10-18 | 2023-11-06 | 19 |
| Tiara Cruse | 2023 | 2023-10-18 | 2023-11-06 | 19 |
| Fatou Cissoko-Stephens | 2024 | 2024-10-13 | 2024-11-04 | 22 |
| Amy Bonner | 2016 | 2016-10-20 | 2016-11-11 | 22 |
| Tiara Cruse | 2021 | 2021-10-17 | 2021-11-09 | 23 |
| Jeff Wooten | 2025 | 2025-10-10 | 2025-11-03 | 24 |
| Michael Price | 2021 | 2021-10-15 | 2021-11-09 | 25 |
| Billy Smith | 2021 | 2021-10-15 | 2021-11-09 | 25 |
Each official compared with their own other games, and each game measured against its own league: whistle against that league’s average, home lean against its lean, home result against what the matchup predicts.
The first game back after switching leagues is always a season opener, and openers run differently for everyone. So the fair test is against officials who work only women’s college, in their own first game of the season: their whistle moves +0.57 fouls and the home side +0.061 wins a game; the two-league officials move +1.24 and +0.083. The gap between the two groups is inside what chance gives, so the switch itself does not show.
Officials are matched across leagues by name; ESPN gives them no ID. Miles are straight-line between arenas, counted only between games within 30 days of each other.
Nobody was flagged in advance. These are the officials whose whistle, home lean and home results sit furthest from the field, added together. Far from the field is not the same as wrong. A tight whistle is a style, and a home-results tilt over a few hundred games is usually luck. The cards say which is which.
679 games over 23 seasons (2004-05-26 to 2026-09-21), 99 in the playoffs.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2004 | 16 | 62.5% | 66.1% | 37.90 | 37.60 |
| 2005 | 19 | 73.7% | 73.1% | 37.70 | 39.50 |
| 2006 | 14 | 28.6% | 50.2% | 42.40 | 39.30 |
| 2007 | 22 | 68.2% | 58.6% | 40.30 | 39.10 |
| 2008 | 23 | 56.5% | 63.8% | 40.20 | 41.30 |
| 2009 | 25 | 44.0% | 68.7% | 38.60 | 40.80 |
| 2010 | 27 | 70.4% | 60.9% | 40.00 | 38.40 |
| 2011 | 24 | 70.8% | 66.3% | 36.70 | 36.20 |
| 2012 | 30 | 60.0% | 55.2% | 36.00 | 35.40 |
| 2013 | 34 | 47.1% | 55.4% | 36.90 | 35.50 |
| 2014 | 31 | 58.1% | 60.6% | 38.50 | 36.50 |
| 2015 | 36 | 47.2% | 57.2% | 37.00 | 36.90 |
| 2016 | 31 | 50.0% | 52.7% | 42.50 | 38.80 |
| 2017 | 31 | 29.0% | 54.0% | 38.70 | 37.60 |
| 2018 | 29 | 58.6% | 50.0% | 36.30 | 36.20 |
| 2019 | 36 | 63.9% | 61.2% | 36.40 | 34.50 |
| 2020 | 27 | 37.20 | 35.30 | ||
| 2021 | 30 | 56.7% | 56.3% | 36.70 | 34.30 |
| 2022 | 37 | 70.3% | 60.5% | 35.90 | 34.70 |
| 2023 | 37 | 54.1% | 55.3% | 34.30 | 35.50 |
| 2024 | 41 | 63.4% | 57.4% | 32.80 | 34.40 |
| 2025 | 42 | 61.9% | 59.2% | 34.90 | 34.90 |
| 2026 | 37 | 56.8% | 57.3% | 41.50 | 39.40 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Chicago Sky | 92 | 35 | 38.0% | 46.8% | -1.8 |
| Dallas Wings | 104 | 37 | 35.6% | 42.3% | -1.5 |
| Seattle Storm | 121 | 64 | 52.9% | 57.1% | -1.1 |
| Sacramento Monarchs | 17 | 8 | 47.1% | 56.8% | -0.9 |
| Indiana Fever | 103 | 45 | 43.7% | 46.6% | -0.7 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Las Vegas Aces | 117 | 68 | 58.1% | 51.7% | +1.6 |
| New York Liberty | 108 | 61 | 56.5% | 50.2% | +1.4 |
| Washington Mystics | 100 | 51 | 51.0% | 46.5% | +1.0 |
| Phoenix Mercury | 119 | 63 | 52.9% | 49.1% | +0.9 |
| Connecticut Sun | 113 | 65 | 57.5% | 53.5% | +0.9 |
196 games over 9 seasons (2018-05-26 to 2026-09-22), 2 in the playoffs.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2018 | 10 | 40.0% | 55.3% | 37.20 | 36.20 |
| 2019 | 16 | 56.2% | 59.7% | 38.40 | 34.50 |
| 2020 | 10 | 39.70 | 35.40 | ||
| 2021 | 17 | 76.5% | 53.4% | 34.90 | 34.60 |
| 2022 | 18 | 33.3% | 42.0% | 39.20 | 34.50 |
| 2023 | 27 | 51.9% | 54.4% | 37.10 | 35.10 |
| 2024 | 27 | 55.6% | 60.3% | 36.20 | 33.90 |
| 2025 | 32 | 71.9% | 59.0% | 36.10 | 34.80 |
| 2026 | 39 | 56.4% | 53.3% | 40.90 | 39.50 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Phoenix Mercury | 29 | 10 | 34.5% | 48.2% | -1.7 |
| Seattle Storm | 34 | 14 | 41.2% | 48.1% | -0.9 |
| Connecticut Sun | 27 | 13 | 48.1% | 54.9% | -0.8 |
| Atlanta Dream | 33 | 16 | 48.5% | 51.3% | -0.4 |
| Las Vegas Aces | 26 | 17 | 65.4% | 67.2% | -0.2 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Minnesota Lynx | 25 | 22 | 88.0% | 60.1% | +3.1 |
| Los Angeles Sparks | 36 | 18 | 50.0% | 45.8% | +0.6 |
| Dallas Wings | 35 | 16 | 45.7% | 42.5% | +0.4 |
| Indiana Fever | 37 | 16 | 43.2% | 41.6% | +0.2 |
| Washington Mystics | 22 | 11 | 50.0% | 48.3% | +0.2 |
153 games over 8 seasons (2018-05-25 to 2026-09-22), 5 in the playoffs.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2018 | 10 | 70.0% | 58.1% | 37.80 | 36.10 |
| 2019 | 15 | 53.3% | 58.2% | 35.80 | 34.70 |
| 2021 | 17 | 52.9% | 60.2% | 35.10 | 34.60 |
| 2022 | 30 | 53.3% | 56.0% | 37.90 | 34.40 |
| 2023 | 34 | 44.1% | 53.6% | 33.70 | 35.50 |
| 2024 | 4 | 75.0% | 64.8% | 36.20 | 34.10 |
| 2025 | 12 | 63.6% | 55.7% | 37.20 | 34.80 |
| 2026 | 31 | 48.4% | 53.0% | 42.20 | 39.40 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Chicago Sky | 28 | 10 | 35.7% | 48.5% | -1.5 |
| Seattle Storm | 25 | 7 | 28.0% | 40.2% | -1.4 |
| Indiana Fever | 19 | 4 | 21.1% | 34.0% | -1.3 |
| Minnesota Lynx | 19 | 11 | 57.9% | 60.7% | -0.3 |
| Phoenix Mercury | 26 | 11 | 42.3% | 44.1% | -0.2 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Atlanta Dream | 24 | 17 | 70.8% | 47.4% | +2.6 |
| New York Liberty | 26 | 16 | 61.5% | 53.5% | +0.9 |
| Connecticut Sun | 22 | 12 | 54.5% | 48.1% | +0.7 |
| Dallas Wings | 24 | 13 | 54.2% | 48.5% | +0.6 |
| Las Vegas Aces | 32 | 22 | 68.8% | 66.9% | +0.2 |
101 games over 4 seasons (2023-05-25 to 2026-09-22), 1 in the playoffs.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2023 | 18 | 55.6% | 42.9% | 36.80 | 35.20 |
| 2024 | 22 | 50.0% | 56.1% | 35.00 | 34.10 |
| 2025 | 25 | 40.0% | 56.5% | 35.30 | 34.90 |
| 2026 | 36 | 44.4% | 54.9% | 36.20 | 40.10 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Atlanta Dream | 13 | 3 | 23.1% | 43.3% | -1.7 |
| Seattle Storm | 15 | 4 | 26.7% | 44.0% | -1.6 |
| Las Vegas Aces | 13 | 8 | 61.5% | 72.7% | -0.9 |
| Los Angeles Sparks | 15 | 4 | 26.7% | 36.1% | -0.8 |
| Minnesota Lynx | 12 | 8 | 66.7% | 74.6% | -0.7 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Indiana Fever | 18 | 13 | 72.2% | 51.7% | +1.9 |
| Connecticut Sun | 15 | 8 | 53.3% | 44.3% | +0.8 |
| Phoenix Mercury | 18 | 9 | 50.0% | 42.4% | +0.7 |
| Washington Mystics | 18 | 10 | 55.6% | 51.3% | +0.4 |
| Chicago Sky | 16 | 5 | 31.2% | 29.2% | +0.2 |
116 games over 5 seasons (2022-05-17 to 2026-09-22), 2 in the playoffs.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2022 | 13 | 53.8% | 60.1% | 35.30 | 34.80 |
| 2023 | 22 | 68.2% | 55.7% | 36.70 | 35.20 |
| 2024 | 13 | 46.2% | 58.7% | 35.40 | 34.10 |
| 2025 | 34 | 61.8% | 55.0% | 36.20 | 34.80 |
| 2026 | 34 | 58.8% | 56.4% | 42.40 | 39.30 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Connecticut Sun | 16 | 2 | 12.5% | 38.0% | -2.4 |
| Minnesota Lynx | 18 | 9 | 50.0% | 60.6% | -1.1 |
| Dallas Wings | 12 | 4 | 33.3% | 44.5% | -0.9 |
| Seattle Storm | 18 | 7 | 38.9% | 43.5% | -0.4 |
| Washington Mystics | 16 | 8 | 50.0% | 52.9% | -0.2 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Las Vegas Aces | 21 | 18 | 85.7% | 70.8% | +1.6 |
| Chicago Sky | 18 | 9 | 50.0% | 38.0% | +1.2 |
| New York Liberty | 21 | 15 | 71.4% | 64.2% | +0.8 |
| Atlanta Dream | 21 | 12 | 57.1% | 50.3% | +0.7 |
| Indiana Fever | 17 | 11 | 64.7% | 58.6% | +0.6 |
384 games over 17 seasons (2010-06-05 to 2026-09-21), 33 in the playoffs.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2010 | 2 | 50.0% | 46.7% | 35.00 | 38.70 |
| 2011 | 9 | 77.8% | 67.6% | 37.20 | 36.20 |
| 2012 | 15 | 53.3% | 66.6% | 35.90 | 35.50 |
| 2013 | 14 | 50.0% | 54.9% | 34.30 | 35.80 |
| 2014 | 12 | 66.7% | 55.2% | 36.00 | 36.80 |
| 2015 | 18 | 33.3% | 57.4% | 36.50 | 36.90 |
| 2016 | 16 | 56.2% | 51.6% | 39.40 | 39.30 |
| 2017 | 20 | 60.0% | 56.8% | 38.70 | 37.70 |
| 2018 | 23 | 56.5% | 53.5% | 37.40 | 36.10 |
| 2019 | 25 | 64.0% | 64.7% | 33.50 | 35.00 |
| 2020 | 25 | 33.80 | 36.10 | ||
| 2021 | 25 | 48.0% | 55.4% | 33.80 | 34.70 |
| 2022 | 34 | 64.7% | 57.6% | 35.00 | 34.80 |
| 2023 | 34 | 61.8% | 55.3% | 33.80 | 35.50 |
| 2024 | 37 | 48.6% | 52.7% | 31.70 | 34.60 |
| 2025 | 37 | 45.9% | 52.8% | 31.70 | 35.30 |
| 2026 | 38 | 50.0% | 51.1% | 38.80 | 39.80 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Phoenix Mercury | 56 | 20 | 35.7% | 45.7% | -1.6 |
| Washington Mystics | 65 | 26 | 40.0% | 48.6% | -1.6 |
| Indiana Fever | 66 | 26 | 39.4% | 42.4% | -0.6 |
| Seattle Storm | 54 | 28 | 51.9% | 54.9% | -0.5 |
| Chicago Sky | 63 | 28 | 44.4% | 47.1% | -0.5 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| New York Liberty | 79 | 46 | 58.2% | 51.6% | +1.4 |
| Minnesota Lynx | 67 | 47 | 70.1% | 63.5% | +1.2 |
| Atlanta Dream | 66 | 36 | 54.5% | 48.4% | +1.1 |
| Dallas Wings | 55 | 22 | 40.0% | 37.3% | +0.5 |
| Connecticut Sun | 73 | 40 | 54.8% | 53.2% | +0.3 |
110 games had a free-throw gap of 20 or more, 2.33 per 100 games. Officials with 100+ games, most frequent first, with the count you'd expect from their share of games.
| Official | Games | Lopsided games | Expected | Per 100 |
|---|---|---|---|---|
| Randy Richardson | 265 | 7 | 6.20 | 2.64 |
| Tiara Cruse | 305 | 8 | 7.10 | 2.62 |
| Kevin Fahy | 196 | 5 | 4.60 | 2.55 |
| Maj Forsberg | 472 | 12 | 11.00 | 2.54 |
| Amy Bonner | 401 | 10 | 9.30 | 2.49 |
| Agon Abazi | 101 | 2 | 2.30 | 1.98 |
| Ashley Gloss | 110 | 2 | 2.60 | 1.82 |
| Clare Simmons | 111 | 2 | 2.60 | 1.80 |
| Date | Away | Home | Away pts | Home pts | Home FTA − away | Home fouls − away | Official | Official | Official |
|---|---|---|---|---|---|---|---|---|---|
| 2007-07-20 | Washington Mystics | Minnesota Lynx | 91 | 87 | -40 | +10 | Roy Gulbeyan | ||
| 2004-09-10 | San Antonio Silver Stars | Indiana Fever | 82 | 65 | -32 | +10 | |||
| 2013-07-07 | Phoenix Mercury | Minnesota Lynx | 59 | 91 | +30 | -16 | |||
| 2015-09-03 | Washington Mystics | Los Angeles Sparks | 91 | 93 | +29 | -12 | Maj Forsberg | ||
| 2026-08-23 | Washington Mystics | Portland Fire | 105 | 100 | -29 | +15 | Tim Greene | Ashley Gloss | Gerda Gatling |
| 2025-06-08 | Connecticut Sun | Washington Mystics | 67 | 104 | +28 | -13 | Angelica Suffren | Tiara Cruse | Angel Kent |
| 2008-09-06 | Minnesota Lynx | Seattle Storm | 88 | 96 | +27 | -8 | Roy Gulbeyan | ||
| 2014-05-30 | Tulsa Shock | Phoenix Mercury | 78 | 100 | +27 | -8 | |||
| 2019-08-29 | Los Angeles Sparks | Indiana Fever | 87 | 83 | +27 | -9 | Tiara Cruse | Kevin Fahy | |
| 2015-06-23 | Los Angeles Sparks | Washington Mystics | 80 | 84 | +26 | -7 |
Fouls, free throws and home-floor advantage season by season, with the number of officials who worked at least one game on the feed.
| Season | Games | Officials | Fouls/game | FTA/game | Home win (true home) | OT |
|---|---|---|---|---|---|---|
| 2004 | 114 | 33 | 37.7 | 37.2 | 62.3% | 7.0% |
| 2005 | 131 | 31 | 39.2 | 39.1 | 63.4% | 8.4% |
| 2006 | 158 | 31 | 39.5 | 41.7 | 58.9% | 3.8% |
| 2007 | 168 | 33 | 39.3 | 40.2 | 58.9% | 7.7% |
| 2008 | 174 | 32 | 41.2 | 42.6 | 60.3% | 6.3% |
| 2009 | 179 | 33 | 40.5 | 42.0 | 67.6% | 11.7% |
| 2010 | 165 | 30 | 38.6 | 41.1 | 60.6% | 6.7% |
| 2011 | 170 | 31 | 36.2 | 37.2 | 64.1% | 4.1% |
| 2012 | 183 | 29 | 35.5 | 36.9 | 60.7% | 6.0% |
| 2013 | 221 | 32 | 35.7 | 37.7 | 59.3% | 5.4% |
| 2014 | 222 | 31 | 36.8 | 37.4 | 57.7% | 9.0% |
| 2015 | 225 | 29 | 36.9 | 36.8 | 61.8% | 6.2% |
| 2016 | 219 | 31 | 39.3 | 42.3 | 55.3% | 9.1% |
| 2017 | 219 | 30 | 37.8 | 39.1 | 59.4% | 5.5% |
| 2018 | 220 | 34 | 36.2 | 36.9 | 55.5% | 3.2% |
| 2019 | 220 | 31 | 34.8 | 34.3 | 61.8% | 2.3% |
| 2020 | 147 | 24 | 35.7 | 36.3 | 2.7% | |
| 2021 | 200 | 31 | 34.6 | 34.3 | 54.8% | 8.0% |
| 2022 | 240 | 32 | 34.9 | 35.9 | 54.2% | 5.0% |
| 2023 | 261 | 32 | 35.3 | 36.6 | 52.9% | 4.2% |
| 2024 | 260 | 32 | 34.2 | 35.7 | 54.4% | 3.8% |
| 2025 | 311 | 35 | 34.9 | 36.4 | 56.5% | 2.6% |
| 2026 | 324 | 34 | 39.7 | 41.6 | 54.5% | 3.7% |
Source: ESPN's public game summaries, pulled game by game for every WNBA game on the feed from 2004 to 2026 (4,731 games with at least two officials listed; games with no crew on record are left out of referee tables). Officials come from the game info block; team totals and every player line come from the box score. Where ESPN's team foul field is blank, team fouls are the sum of the player lines.
Baselines: home-win rate is compared with a matchup expectation (both teams' records that season without that game, log5, shifted to the league home rate in the same season), true home games only; neutral-floor games are excluded from every home-floor number. Team and coach pairings use the same matchup expectation, game by game. Player pairings are compared with the player's own per-30-minute rates that season.
What the method can't see: which crew member made which call, foul type, the score when a call came, or whether a call was right. It measures tendencies and outcomes. WNBA officials are a small staff who see every team many times a season, and ESPN has the crew on nearly every game from 2004 on, so the early years carry fewer games per official.
NCAAW
Every Division I women's game on ESPN's feed from 2013-14 to 2025-26, every official on the floor, every box score. Referees, the teams and players in front of them, and where the numbers are flat.
Officials should be invisible. The best crew is the one nobody talks about after the game. The grade rewards sitting near the middle of the pack in every category at once, not a tight whistle or a loose one, a home lean or a road lean. I take every active official with 150+ games, find the median in each category, and measure how far each one sits from it in typical spreads (the median absolute deviation, scaled to match a standard deviation). Before measuring, each number is pulled part of the way toward the middle, by an amount set for that category: the more a category moves by luck from game to game, and the fewer games an official has, the further the pull. The distance is then measured in the spread of the raw numbers, so a category that is mostly luck leaves nearly everyone at an A there, and a noisy category can't hand out the same F as a steady one. One side effect is deliberate: an official with few games can't be shown yet to stand out, so newer officials sit closer to an A until their games say otherwise. Within half a spread of the median in a category is an A there; within one, a B; within one and a half, a C; within two, a D; further, an F. Consistency is the one category where only the wide side counts, and it has two parts: from one night to the next, how much the foul total bounces around after allowing for who was playing (overtime scaled back to a regulation game), and how often the free throws came out one-sided. Each part is measured in the same spreads as every other category, steadier than the median counts as zero, the category takes the worse of the two. All three officials on the floor share the numbers of a game, so read it as a crew habit until the crews are split. The overall grade is the average distance across all 6 categories. An A official is one whose numbers you couldn't tell from the pack. An F is one whose numbers you could.
Categories: Whistle (fouls per game vs Division I in the same seasons), Home lean (home-minus-away fouls vs the Division I lean), Home results (home wins vs expected), Free-throw lean (home-minus-away free throws vs the Division I lean), Technicals (technicals per game vs the Division I in the same seasons), Consistency (night-to-night spread of the whistle around what the two teams usually commit, and lopsided free-throw nights; only the wide side counts, and the worse part sets the letter). 217 active officials graded; median values: Whistle -0.09, Home lean +0.12, Home results -0.64, Free-throw lean -0.21, Technicals -0.00, Consistency 0.00.
For every official with 300+ games I split the games into odd and even (by date), work out each category on both halves, and check how well one half predicts the other across officials. The first number is that correlation; the second projects it to a full career. Near 1 means the category describes the official; near 0 means it is luck. The rule: a category counts toward the grade only if the full-career number is at least 0.2 in the NBA or college game. Close games (home wins in games decided by three or fewer) and the half-to-half foul gap fail it and are shown here but left out of the grade. All three officials on the floor share each game, so even the categories that repeat describe a crew habit as much as a person.
| Category | Odd vs even | Full career | Officials | In the grade |
|---|---|---|---|---|
| Whistle | +0.91 | +0.95 | 149 | yes |
| Home lean | +0.53 | +0.69 | 149 | yes |
| Home results | +0.17 | +0.29 | 149 | yes |
| Free-throw lean | +0.44 | +0.61 | 149 | yes |
| Technicals | +0.41 | +0.59 | 149 | yes |
| Close games | +0.00 | +0.01 | 147 | no |
| Consistency: night spread | +0.30 | +0.46 | 149 | yes |
| Consistency: lopsided nights | +0.42 | +0.59 | 149 | yes |
A 140 B 72 C 5 D 0 F 0 of 217 active officials
| Official | Games | Whistlerepeats 0.95 | Home leanrepeats 0.69 | Home resultsrepeats 0.29 | Free-throw leanrepeats 0.61 | Technicalsrepeats 0.59 | Consistencyrepeats 0.46 | Distance | Grade |
|---|---|---|---|---|---|---|---|---|---|
| Jaclyn Watson | 186 | A | A | A | A | A | A | 0.11 | A |
| Alex Leap | 203 | A | A | A | A | A | A | 0.12 | A |
| Simona Petronyte Sandoval | 177 | A | A | A | A | A | A | 0.12 | A |
| Norma Jones | 547 | A | A | A | A | A | A | 0.16 | A |
| Kylie Galloway | 515 | A | A | A | A | A | A | 0.16 | A |
| Cara Seggie | 377 | A | A | A | A | A | A | 0.16 | A |
| Teresa Turner | 464 | A | A | A | A | A | A | 0.17 | A |
| Marla Gearhart | 340 | A | A | A | A | A | A | 0.17 | A |
| Karleena Tobin | 204 | A | A | A | A | A | A | 0.17 | A |
| Denise Brooks | 764 | A | A | A | A | A | A | 0.18 | A |
| Tom Danaher | 733 | A | A | A | A | A | A | 0.19 | A |
| Natosha Harris | 337 | A | A | A | A | A | A | 0.19 | A |
| Official | Games | Whistlerepeats 0.95 | Home leanrepeats 0.69 | Home resultsrepeats 0.29 | Free-throw leanrepeats 0.61 | Technicalsrepeats 0.59 | Consistencyrepeats 0.46 | Distance | Grade |
|---|---|---|---|---|---|---|---|---|---|
| Jamie Broderick | 590 | C | D | B | D | A | F | 1.25 | C |
| Felicia Grinter | 1080 | C | D | B | D | B | C | 1.23 | C |
| Brian Garland | 886 | C | C | A | D | C | C | 1.21 | C |
| Bill Larance | 691 | D | C | B | D | A | B | 1.07 | C |
| Scott Yarbrough | 535 | C | C | A | D | C | C | 1.05 | C |
| Maggie Tieman | 767 | B | C | B | C | A | B | 0.90 | B |
| Doug Knight | 867 | A | C | A | C | B | C | 0.90 | B |
| Missy Brooks | 569 | A | D | A | C | B | B | 0.85 | B |
| Esther Hsu | 296 | A | C | A | C | B | B | 0.78 | B |
| Metta Christensen | 300 | C | C | A | C | A | B | 0.78 | B |
| Chastity Taylor | 152 | B | C | A | B | C | B | 0.77 | B |
| Billy Smith | 796 | B | C | A | B | A | C | 0.77 | B |
Every graded official: sortable in the Referees tab (Grade column). Distance is in median absolute deviations; 0 is dead center.
Total fouls called per game with the official on the floor, against the national average in the same seasons. Officials with 150+ games. This is the strongest signal in the data. It’s stable and it’s large, and it says nothing about fairness, only about how much contact gets called.
| Official | Games | Fouls/game | vs D-I | z |
|---|---|---|---|---|
| Sarah Fleming | 186 | 37.90 | +4.46 | +8.2 |
| Jennifer White | 177 | 37.70 | +4.28 | +8.0 |
| Darren McClure | 166 | 37.90 | +4.14 | +7.2 |
| Neonta Williams | 255 | 37.10 | +3.64 | +7.8 |
| Bill Larance | 691 | 37.50 | +3.59 | +12.5 |
| Victoria Francis | 163 | 36.80 | +3.40 | +5.7 |
| Jessica Fortner | 255 | 36.70 | +3.31 | +7.3 |
| Demoya Pugh | 216 | 36.50 | +3.12 | +6.0 |
| LaShonda Kidd | 195 | 36.60 | +3.01 | +6.3 |
| Tiffany Bridges | 216 | 36.30 | +2.93 | +5.9 |
| Official | Games | Fouls/game | vs D-I | z |
|---|---|---|---|---|
| Brenda Pantoja | 799 | 29.40 | -4.41 | -18.1 |
| Bob Scofield | 628 | 30.80 | -3.14 | -11.7 |
| Elizabeth Kuzmeski | 341 | 30.40 | -3.14 | -8.1 |
| Ashley Olsen | 205 | 30.40 | -2.94 | -6.0 |
| Mark McClenney | 351 | 30.80 | -2.92 | -7.6 |
| Rod Creech | 534 | 30.80 | -2.83 | -9.9 |
| Ashlee Goode | 569 | 30.80 | -2.67 | -9.3 |
| Charles Gonzales | 157 | 30.80 | -2.56 | -4.5 |
| Ryan Nunes | 230 | 31.00 | -2.51 | -5.5 |
| Nicole Leon | 395 | 31.10 | -2.38 | -6.5 |
Home fouls minus away fouls in true home games, against the national lean in the same seasons (home teams get called for slightly fewer fouls everywhere). Negative means the official calls even fewer on the home team than the rest of the field does; positive means the visitor gets the friendlier whistle.
| Official | Games | Home fouls − away | vs D-I | z | Home FTA edge vs D-I |
|---|---|---|---|---|---|
| Felicia Grinter | 1080 | -2.58 | -1.16 | -6.8 | +1.59 |
| Brian Garland | 886 | -2.48 | -1.21 | -6.0 | +1.85 |
| Missy Brooks | 569 | -2.71 | -1.45 | -5.8 | +1.87 |
| Bill Larance | 691 | -2.43 | -1.09 | -4.9 | +1.76 |
| Doug Knight | 867 | -2.30 | -0.97 | -4.9 | +1.46 |
| Cameron Inouye | 1130 | -2.17 | -0.77 | -4.6 | +0.78 |
| Jamie Broderick | 590 | -2.29 | -1.19 | -4.5 | +1.31 |
| Brian Hall | 973 | -2.18 | -0.79 | -4.5 | +1.28 |
| Official | Games | Home fouls − away | vs D-I | z | Home FTA edge vs D-I |
|---|---|---|---|---|---|
| Cheryll Blue | 410 | -0.10 | +1.06 | +3.8 | -0.93 |
| Elizabeth Kuzmeski | 341 | -0.09 | +1.02 | +3.8 | -1.18 |
| Danielle Johnson | 159 | +0.26 | +1.42 | +3.6 | -1.89 |
| Daryl Humphrey | 333 | -0.28 | +1.09 | +3.6 | -1.64 |
| Brad Schmidt | 218 | +0.07 | +1.19 | +3.5 | -2.06 |
| Erika Herriman | 509 | -0.41 | +0.86 | +3.3 | -0.59 |
| Esther Hsu | 296 | -0.02 | +1.05 | +3.2 | -1.57 |
| Sean Martin | 253 | -0.08 | +0.99 | +3.2 | -1.64 |
Technical fouls per game as ESPN logs them for both teams combined. ESPN's box scores are thin on this field in some seasons, so treat it as a lower bound.
| Official | Games | Techs/game |
|---|---|---|
| John Fletcher | 199 | 0.37 |
| Tommi Paris | 328 | 0.28 |
| Chastity Taylor | 152 | 0.28 |
| Chris Sauceda | 247 | 0.27 |
| Darren McClure | 166 | 0.27 |
| Demoya Pugh | 216 | 0.26 |
| Kaz Beverley | 314 | 0.26 |
| Deanna Jackson | 394 | 0.26 |
Home-team win rate in true home games with the official on the floor, against what the matchup predicts: both teams’ records that season (not counting that game), adjusted to the national home rate that season. That keeps a run of early-season home games against weak visitors from looking like a friendly whistle. Across 217 officials with 150+ games, chance alone would put about 10 beyond ±2 and about 0.6 beyond ±3; the count here is 26 and 5.
| Official | Home games | Home win % | Expected | z |
|---|---|---|---|---|
| Cameron Inouye | 950 | 69.3% | 64.7% | +3.5 |
| Bill Larance | 588 | 71.6% | 65.8% | +3.5 |
| Felicia Grinter | 900 | 71.3% | 66.7% | +3.4 |
| Nicole Brannon | 288 | 72.6% | 64.8% | +3.1 |
| Saif Esho | 443 | 66.4% | 60.4% | +3.0 |
| Brian Hall | 799 | 70.3% | 66.3% | +2.8 |
| Nicole Leon | 352 | 69.9% | 63.9% | +2.7 |
| Brian Garland | 724 | 69.3% | 65.5% | +2.6 |
| Official | Home games | Home win % | Expected | z |
|---|---|---|---|---|
| D'Lynn Schertz | 244 | 55.3% | 64.6% | -3.6 |
| Kristie Weed | 228 | 51.3% | 59.0% | -2.7 |
| Scott Berkins | 331 | 54.7% | 60.4% | -2.5 |
| Nicholas Cappel | 445 | 59.6% | 64.2% | -2.4 |
| Travis Jones | 261 | 55.9% | 61.8% | -2.3 |
| Jessica Fortner | 232 | 57.3% | 63.6% | -2.3 |
| Hillary Harwell | 155 | 50.3% | 57.9% | -2.2 |
| Kiesha Brown | 204 | 56.9% | 63.4% | -2.2 |
Games where the matchup made one team at least a 70% favorite, judged from both teams' strength that season (scoring margins against who they played) and where the game was played. Nationally, underdogs won 10.5% of 30,451 such games, against 10.6% expected. Each official is measured against the upsets expected in that official's own games, not a league average. Officials with 75+ of them, most underdog wins first. 260 officials: 14 beyond ±2 (chance: about 11.8), 0 beyond ±3 (chance: about 0.7).
| Official | Games | Underdog wins | Expected | z |
|---|---|---|---|---|
| Natasha Camy | 448 | 13.6% | 10.1% | +2.6 |
| Ed Sidlasky | 530 | 14.0% | 10.7% | +2.5 |
| Kenya Kirkland | 159 | 16.4% | 10.7% | +2.4 |
| Ashley Olsen | 147 | 15.6% | 10.3% | +2.2 |
| Ashley Gloss | 165 | 13.9% | 9.5% | +2.1 |
| David Troyan | 166 | 15.1% | 10.5% | +2.0 |
| Simona Petronyte Sandoval | 100 | 15.0% | 9.5% | +2.0 |
| Brenda Pantoja | 509 | 13.9% | 11.4% | +1.9 |
| Rochelle Bennett | 235 | 6.0% | 11.1% | -2.6 |
| David Jenkins | 147 | 5.4% | 12.1% | -2.6 |
| John Capolino | 436 | 7.6% | 10.7% | -2.2 |
| Missy Brooks | 382 | 6.0% | 9.0% | -2.1 |
| Alicia Iwakiri | 93 | 5.4% | 12.3% | -2.1 |
| Tiara Cruse | 551 | 7.4% | 9.9% | -2.0 |
| Royce Blevins | 271 | 6.6% | 10.2% | -2.0 |
| Hannah Reynolds | 81 | 6.2% | 13.6% | -2.0 |
Home-team win rate in true home games decided by three points or fewer, officials with 300+ games.
| Official | Games | Close games | Home wins in them |
|---|---|---|---|
| Cheryll Blue | 410 | 10.2% | 29.7% |
| Roy Jackson | 375 | 10.4% | 36.4% |
| Tommi Paris | 328 | 9.5% | 37.5% |
| Jeff Cross | 444 | 11.5% | 37.8% |
| Tiffany Bird | 718 | 11.1% | 38.5% |
| Tim Bryant | 488 | 8.8% | 38.9% |
| Angel Kent | 346 | 6.9% | 70.0% |
| Richard Waters | 327 | 8.9% | 68.2% |
| Keith Miller | 336 | 13.1% | 68.2% |
| Kayla Maxey | 311 | 7.7% | 66.7% |
| Daryl Humphrey | 333 | 10.8% | 65.6% |
| Ashlee Goode | 569 | 10.9% | 65.2% |
Every pairing with 12+ games. Expected wins come from each game’s matchup (both teams’ records that season without that game, and the home floor), so a strong team is expected to win and drawing the big games is not held against anyone; the question is only whether the team did better or worse than that with this official. 4,415 pairings; 299 beyond ±2 (chance: about 197.0), 19 beyond ±3 (chance: about 10.2).
| Official | Team | Games | Wins | Win % | Expected | z | Foul diff vs team norm |
|---|---|---|---|---|---|---|---|
| Chad Tucker | Mercer Bears | 13 | 3 | 23.1% | 66.3% | -3.6 | +1.09 |
| Mark McClenney | George Mason Patriots | 16 | 3 | 18.8% | 56.6% | -3.5 | -0.44 |
| Elizabeth Kuzmeski | Central Connecticut Blue Devils | 15 | 0 | 0.0% | 38.5% | -3.5 | +1.99 |
| Roy Jackson | Morgan State Lady Bears | 12 | 2 | 16.7% | 58.1% | -3.3 | +0.24 |
| Roy Jackson | UNC Asheville Bulldogs | 16 | 2 | 12.5% | 50.0% | -3.3 | +0.48 |
| Pete Arceo | Hawai'i Rainbow Warriors | 16 | 6 | 37.5% | 70.5% | -3.1 | +0.97 |
| Jorge Martinez | Siena Saints | 12 | 2 | 16.7% | 53.9% | -3.0 | +2.14 |
| Frank Steratore | Eastern Michigan Eagles | 18 | 3 | 16.7% | 46.0% | -2.9 | +1.14 |
| Rod Creech | Tulane Green Wave | 13 | 2 | 15.4% | 53.1% | -2.9 | +2.59 |
| Hannah Reynolds | Austin Peay Governors | 19 | 5 | 26.3% | 55.8% | -2.8 | +2.37 |
| Felicia Grinter | Ole Miss Rebels | 35 | 12 | 34.3% | 53.8% | -2.8 | +0.49 |
| Kristie Weed | UNC Asheville Bulldogs | 19 | 5 | 26.3% | 54.1% | -2.8 | +0.79 |
| Official | Team | Games | Wins | Win % | Expected | z | Foul diff vs team norm |
|---|---|---|---|---|---|---|---|
| Tyler Trimble | Idaho Vandals | 31 | 25 | 80.6% | 53.2% | +3.6 | -2.15 |
| Brian Woods | BYU Cougars | 22 | 20 | 90.9% | 58.5% | +3.5 | -2.34 |
| Joseph Vaszily | Notre Dame Fighting Irish | 56 | 44 | 78.6% | 59.3% | +3.5 | +0.91 |
| In'Fini Robinson | UCLA Bruins | 34 | 30 | 88.2% | 63.4% | +3.3 | -0.69 |
| Talisa Green | Louisville Cardinals | 20 | 20 | 100.0% | 71.9% | +3.3 | +0.27 |
| Jeffrey Smith | Temple Owls | 18 | 15 | 83.3% | 52.6% | +3.1 | -2.52 |
| Brian Garland | Kentucky Wildcats | 33 | 24 | 72.7% | 49.6% | +3.1 | +0.26 |
| Kevin Dillard | Notre Dame Fighting Irish | 18 | 18 | 100.0% | 74.0% | +3.1 | -0.90 |
| Nicole Leon | Washington Huskies | 20 | 18 | 90.0% | 59.9% | +3.1 | -3.05 |
| Cameron Inouye | Iowa State Cyclones | 37 | 29 | 78.4% | 56.1% | +3.0 | +0.68 |
| Ed Sidlasky | Creighton Bluejays | 15 | 13 | 86.7% | 51.7% | +3.0 | -0.47 |
| Ed Sidlasky | Notre Dame Fighting Irish | 35 | 32 | 91.4% | 72.8% | +3.0 | -1.31 |
Pairings with 24+ games where the team's foul differential (own fouls minus opponent's) with this official differs most from the team's normal differential that season. Negative means the team got called for fewer fouls than usual with this official on the floor.
| Official | Team | Games | Foul diff | vs team norm | FTA diff |
|---|---|---|---|---|---|
| Brian Woods | Stanford Cardinal | 28 | -4.00 | -2.92 | +5.82 |
| Cameron Inouye | Missouri Tigers | 24 | -2.29 | -2.58 | 0.00 |
| Kevin Pethtel | West Virginia Mountaineers | 49 | -2.63 | -2.54 | +4.20 |
| Michol Murray | New Mexico Lobos | 25 | -3.36 | -2.42 | +3.80 |
| Cameron Inouye | Florida Gators | 30 | -1.93 | -2.38 | +2.27 |
| Brad Maxey | Northern Kentucky Norse | 26 | -4.38 | -2.32 | +4.77 |
| Karen Lasuik | Stanford Cardinal | 24 | -3.62 | -2.30 | +5.17 |
| Scott Osborne | San Diego State Aztecs | 24 | -2.04 | -2.28 | +2.75 |
| Brian Garland | Georgia Lady Bulldogs | 33 | +4.36 | +3.50 | -6.45 |
| Michael McConnell | Texas Longhorns | 33 | +2.58 | +3.05 | -1.73 |
| Lisa Jones | West Virginia Mountaineers | 24 | +2.75 | +2.86 | -2.58 |
| Gina Cross | Texas Tech Lady Raiders | 28 | +2.11 | +2.85 | -3.54 |
| Roy Gulbeyan | Ole Miss Rebels | 33 | +4.12 | +2.78 | -5.00 |
| Gina Cross | Rutgers Scarlet Knights | 25 | +4.84 | +2.57 | -5.48 |
| Tiffany Bird | California Golden Bears | 24 | +1.62 | +2.47 | -0.62 |
| Roy Gulbeyan | Arkansas Razorbacks | 39 | +0.10 | +2.37 | -0.44 |
Every player-official pairing with 12+ games of 8+ minutes (college careers are short, so the bar is lower than the NBA ledger's and the pairings are noisier). Free throws, personal fouls and points are per 30 minutes and measured against the player's own rates in the same seasons. 5,931 pairings. Beyond ±3: free throws 8, fouls 19, points 12; chance alone would give about 16.0 on each.
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Maj Forsberg | Aubrey Griffin | CONN | 15 | 0.70 | 3.70 | -3.3 | 2.44 | 1.83 | +1.4 |
| Brenda Pantoja | Chantel Osahor | WASH | 19 | 1.20 | 2.66 | -2.9 | 3.21 | 3.53 | -0.7 |
| Karen Preato | Sara Puckett | TENN | 13 | 0.00 | 1.37 | -2.7 | 2.45 | 2.50 | -0.1 |
| Karen Preato | Ariel Stephenson | GW | 12 | 1.02 | 3.06 | -2.7 | 2.04 | 2.12 | -0.1 |
| Cameron Inouye | Kristin Scott | ISU | 12 | 0.74 | 2.94 | -2.7 | 2.75 | 2.51 | +0.4 |
| Maj Forsberg | Khadija Faye | TTU | 13 | 1.92 | 4.85 | -2.5 | 4.63 | 4.74 | -0.1 |
| Amy Sanchez | Paige Meyer | SDST | 13 | 1.42 | 3.15 | -2.5 | 1.62 | 2.16 | -1.2 |
| Jeff Cross | Chelby Koker | NIU | 14 | 1.61 | 3.78 | -2.5 | 3.40 | 3.22 | +0.4 |
| Maj Forsberg | Shaylee Gonzales | TEX | 12 | 0.50 | 1.69 | -2.5 | 1.44 | 2.03 | -1.5 |
| Rod Creech | Elizabeth Kitley | VT | 13 | 2.06 | 3.99 | -2.5 | 2.33 | 2.13 | +0.5 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Jamie Broderick | Kendall Bostic | ILL | 17 | 3.10 | 1.42 | +3.6 | 2.00 | 1.86 | +0.4 |
| Kevin Pethtel | Tyia Singleton | RUTG | 15 | 3.69 | 1.58 | +3.4 | 4.56 | 4.24 | +0.4 |
| Trey Miles | Anastasiia Boldyreva | MTSU | 13 | 5.48 | 2.84 | +3.4 | 3.46 | 3.02 | +0.6 |
| Felicia Grinter | Tyasha Harris | SC | 16 | 3.96 | 2.14 | +3.4 | 1.99 | 1.64 | +1.0 |
| Felicia Grinter | Aaliyah Patty | OSU | 14 | 3.60 | 1.77 | +3.4 | 4.13 | 3.92 | +0.3 |
| Michelle Del Duco | Hannah Spitzley | WMU | 12 | 4.05 | 2.13 | +3.1 | 2.24 | 2.67 | -1.0 |
| Doug Knight | Madisen Smith | WVU | 12 | 3.77 | 2.05 | +3.1 | 2.03 | 1.96 | +0.2 |
| Julie Krommenhoek | Bella Fontleroy | BAY | 22 | 3.51 | 2.10 | +3.0 | 3.12 | 3.17 | -0.1 |
| Julie Krommenhoek | Braxtin Miller | OSU | 12 | 4.51 | 2.53 | +3.0 | 1.79 | 1.83 | -0.1 |
| Kevin Pethtel | Ashley Owusu | MD | 16 | 7.29 | 5.02 | +2.9 | 2.40 | 2.14 | +0.7 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Melissa Barlow | Quinn Dornstauder | ASU | 15 | 4.44 | 4.22 | +0.2 | 3.78 | 1.88 | +3.8 |
| Natasha Camy | Bailey Maupin | TTU | 14 | 3.40 | 4.27 | -1.1 | 3.76 | 2.26 | +3.7 |
| Doug Knight | Ayoka Lee | KSU | 15 | 6.18 | 5.27 | +1.0 | 4.93 | 3.20 | +3.6 |
| Brian Garland | Taylah Thomas | TTU | 16 | 3.10 | 2.66 | +0.7 | 4.61 | 3.07 | +3.2 |
| In'Fini Robinson | Cate Reese | ARIZ | 14 | 2.38 | 3.90 | -1.8 | 4.20 | 2.69 | +3.2 |
| Roy Gulbeyan | DiDi Richards | BAY | 13 | 1.93 | 2.31 | -0.5 | 3.81 | 2.27 | +3.2 |
| Michelle Del Duco | Quinesha Lockett | TOL | 17 | 4.11 | 3.88 | +0.3 | 3.82 | 2.66 | +3.2 |
| Lisa Jones | Francesca Belibi | STAN | 16 | 5.92 | 4.03 | +1.9 | 4.89 | 3.03 | +3.2 |
| Felicia Grinter | Zakiyah Franklin | KU | 16 | 2.51 | 2.85 | -0.6 | 2.34 | 1.47 | +3.1 |
| David Troyan | Grace O'Neill | DREX | 12 | 1.50 | 0.85 | +1.8 | 2.43 | 1.51 | +3.1 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Brenda Pantoja | Kiki Iriafen | USC | 15 | 5.54 | 5.20 | +0.4 | 1.89 | 3.42 | -3.1 |
| Elizabeth Kuzmeski | Lola Mullaney | HARV | 12 | 0.66 | 1.66 | -1.7 | 0.90 | 1.93 | -2.9 |
| Brenda Pantoja | Aleah Goodman | ORST | 12 | 0.63 | 1.39 | -1.4 | 0.50 | 1.65 | -2.9 |
| Joseph Vaszily | Aisha Sheppard | VT | 13 | 1.58 | 2.10 | -0.8 | 1.04 | 2.40 | -2.8 |
| Maggie Tieman | Jocelyn Tate | MSU | 15 | 2.92 | 3.52 | -0.7 | 2.20 | 4.00 | -2.7 |
| Kyle Bacon | Monique Billings | UCLA | 13 | 4.59 | 5.26 | -0.7 | 1.77 | 2.93 | -2.7 |
| Michol Murray | Hannah Stines | WASH | 15 | 0.76 | 1.01 | -0.6 | 0.67 | 1.44 | -2.6 |
| Brian Hall | Makayla Daniels | ARK | 15 | 3.44 | 3.71 | -0.4 | 1.67 | 2.65 | -2.6 |
| Maj Forsberg | Sam Fuehring | LOU | 13 | 2.42 | 3.27 | -0.9 | 1.46 | 2.94 | -2.5 |
| Doug Knight | Gabby Gregory | OU | 14 | 3.48 | 3.57 | -0.1 | 1.17 | 2.16 | -2.5 |
The same question asked of the bench instead of the program: how does a coach's team do with each official on the floor, across every team the coach has had. Expected wins come from each game’s matchup, so a rebuilding year isn't pinned on a referee and drawing the big games is not held against anyone. Pairings need 10+ games. 748 coaches, 4,526 coach×referee pairings; 16 sit beyond three standard deviations against about 10.7 by chance, 271 beyond two against about 201.2 (chance simulated by replaying every game from its expected odds). ESPN lists one head coach per team-season, so a mid-season change is credited to the coach on the record.
| Coach | Teams | Official | G | W | Win % | Expected | z |
|---|---|---|---|---|---|---|---|
| Zenarae Antoine | TXST | Tiffany Bridges | 10 | 2 | 20.0 | 61.3 | -3.78 |
| Susie Gardner | MER | Chad Tucker | 11 | 2 | 18.2 | 65.3 | -3.59 |
| Edward Davis Jr. | MORG | Roy Jackson | 12 | 2 | 16.7 | 58.1 | -3.31 |
| Brittany Young | APSU | Hannah Reynolds | 14 | 3 | 21.4 | 59.2 | -3.16 |
| Laura Beeman | HAW | Pete Arceo | 16 | 6 | 37.5 | 70.5 | -3.12 |
| Danielle Santos Atkinson | HOF | Michael DoCouto | 11 | 1 | 9.1 | 48.5 | -2.84 |
| Kristen Mattio | UNCO | Teresa Turner | 10 | 2 | 20.0 | 57.7 | -2.74 |
| Vanessa Blair-Lewis | GMU | Mark McClenney | 10 | 3 | 30.0 | 66.4 | -2.69 |
| Melissa Jackson | AKR, YSU | Kevin Pethtel | 11 | 3 | 27.3 | 62.0 | -2.66 |
| Carolyn Kieger | MARQ, PSU | Mark Berger | 13 | 2 | 15.4 | 48.7 | -2.66 |
| Coach | Teams | Official | G | W | Win % | Expected | z |
|---|---|---|---|---|---|---|---|
| Addie Micir | LEH | Ken Kelly | 10 | 10 | 100.0 | 46.3 | 3.68 |
| Courtney Banghart | PRIN, UNC | Tom Danaher | 15 | 12 | 80.0 | 42.8 | 3.34 |
| Cori Close | UCLA | In'Fini Robinson | 34 | 30 | 88.2 | 63.4 | 3.32 |
| Jeff Walz | LOU | Talisa Green | 20 | 20 | 100.0 | 71.9 | 3.30 |
| Jon Newlee | IDHO | Tyler Trimble | 25 | 19 | 76.0 | 47.9 | 3.18 |
| Mark Trakh | NMSU, USC | Brenda Pantoja | 17 | 15 | 88.2 | 56.3 | 3.06 |
| Chris Gobrecht | AF | Michol Murray | 14 | 11 | 78.6 | 44.9 | 3.06 |
| Bill Fennelly | ISU | Cameron Inouye | 37 | 29 | 78.4 | 56.1 | 3.05 |
| Amanda Butler | FLA | Scott Yarbrough | 10 | 9 | 90.0 | 50.1 | 3.04 |
| Jim Flanery | CREI | Ed Sidlasky | 15 | 13 | 86.7 | 51.7 | 3.04 |
Foul margin against the team's own norm that season, for pairings with 20+ games. Negative means fewer whistles on the coach's team than usual with that official.
| Coach | Teams | Official | G | Fouls vs norm | FTA margin |
|---|---|---|---|---|---|
| Mike Bradbury | UNM | Kyle Bacon | 20 | -3.23 | 5.55 |
| Mike Carey | WVU | Kevin Pethtel | 37 | -2.92 | 4.46 |
| Tara VanDerveer | STAN | Brian Woods | 28 | -2.92 | 5.82 |
| Rick Insell | MTSU | Trey Miles | 21 | -2.84 | 7.10 |
| Mike McGuire | RAD | Brian Wallace | 23 | -2.78 | 4.65 |
| Lynn Bria | STET | Jelisse Oliver | 20 | -2.77 | 5.35 |
| Kim Mulkey | BAY, LSU | Brenda Pantoja | 30 | 3.42 | -0.90 |
| Yolett McPhee-McCuin | JAX, MISS | Roy Gulbeyan | 21 | 3.28 | -5.24 |
| Kara Lawson | DUKE | Joseph Vaszily | 24 | 2.97 | -4.46 |
| Joe Tartamella | SJU | John Capolino | 21 | 2.80 | -0.71 |
| Amy Williams | NEB | Cameron Inouye | 22 | 2.63 | -1.59 |
| Karen Aston | TEX, UTSA | Roy Gulbeyan | 23 | 2.62 | -4.26 |
Every league has its own game, and the whistle follows it. This section looks only at conference games (both teams in the same league, regular season), so a Big East night is compared with other Big East nights. 33 conferences with 150+ games on record, 27,558 conference games. Membership is taken season by season, so a team that moved is counted in the league it was in at the time. Conferences shown below are the ones that exist this season.
Fouls per game, against the Division I average in the same seasons, and how the home team does. Home foul lean is home fouls minus away fouls; negative means the home team gets the friendlier whistle. Most fouls first.
| Conference | G | Fouls/G | vs D-I | Home win % | Home foul lean | Home FTA lean | Techs/G | Close % | Lopsided/100 | Officials |
|---|---|---|---|---|---|---|---|---|---|---|
| Southwestern Athletic Conference | 532 | 39.00 | +5.36 | 56.6% | -0.85 | +1.08 | 0.32 | 12.4% | 4.32 | 169 |
| Mid-Eastern Athletic Conference | 446 | 36.10 | +2.46 | 54.0% | -0.55 | +0.80 | 0.39 | 10.3% | 2.91 | 109 |
| Sun Belt Conference | 899 | 35.70 | +2.14 | 55.4% | -0.62 | +1.04 | 0.30 | 13.0% | 2.78 | 154 |
| Southeastern Conference | 1624 | 35.40 | +1.28 | 56.9% | -1.26 | +1.58 | 0.22 | 12.1% | 3.14 | 82 |
| Conference USA | 844 | 35.30 | +1.67 | 60.1% | -1.11 | +1.60 | 0.15 | 14.2% | 2.96 | 157 |
| Summit League | 600 | 35.20 | +1.69 | 56.1% | -0.85 | +0.85 | 0.10 | 7.8% | 2.50 | 103 |
| Southland Conference | 756 | 35.10 | +1.49 | 53.8% | -0.39 | +0.39 | 0.23 | 13.0% | 3.04 | 110 |
| Big 12 Conference | 1429 | 34.80 | +0.75 | 58.3% | -1.54 | +2.24 | 0.16 | 10.4% | 2.66 | 80 |
| Ohio Valley Conference | 796 | 34.30 | +0.78 | 57.7% | -0.79 | +1.05 | 0.13 | 12.4% | 2.89 | 171 |
| Missouri Valley Conference | 801 | 34.20 | +0.63 | 56.4% | -0.81 | +1.07 | 0.09 | 11.2% | 3.00 | 106 |
| Atlantic Sun Conference | 731 | 34.00 | +0.42 | 57.2% | -0.48 | +0.44 | 0.14 | 10.3% | 2.46 | 239 |
| Mid-American Conference | 824 | 33.90 | +0.35 | 56.4% | -0.65 | +0.72 | 0.16 | 12.9% | 2.91 | 102 |
| West Coast Conference | 778 | 33.50 | -0.16 | 55.4% | -0.04 | -0.06 | 0.10 | 12.5% | 1.54 | 108 |
| Big Ten Conference | 1787 | 33.30 | -0.70 | 58.1% | -1.34 | +1.78 | 0.12 | 9.1% | 2.46 | 93 |
| Atlantic Coast Conference | 1829 | 33.10 | -0.96 | 57.4% | -1.44 | +1.92 | 0.17 | 11.4% | 2.46 | 94 |
| Northeast Conference | 600 | 33.10 | -0.52 | 59.0% | -0.42 | +0.34 | 0.20 | 12.2% | 1.83 | 140 |
| Horizon League | 873 | 32.90 | -0.61 | 57.4% | -0.87 | +0.85 | 0.16 | 10.2% | 3.09 | 117 |
| American Athletic Conference | 995 | 32.80 | -1.05 | 55.9% | -0.89 | +0.94 | 0.25 | 13.7% | 2.61 | 115 |
| Big South Conference | 636 | 32.70 | -0.91 | 53.7% | -0.64 | +0.96 | 0.21 | 14.9% | 2.99 | 96 |
| Big West Conference | 751 | 32.50 | -0.97 | 54.0% | -0.05 | +0.12 | 0.10 | 15.3% | 2.80 | 113 |
| MAAC | 838 | 32.40 | -1.18 | 56.8% | -0.27 | +0.29 | 0.14 | 11.2% | 1.67 | 127 |
| Big Sky Conference | 766 | 32.20 | -1.33 | 56.0% | -0.03 | -0.13 | 0.10 | 13.1% | 1.96 | 115 |
| Atlantic 10 Conference | 964 | 31.50 | -2.03 | 56.4% | -0.87 | +1.00 | 0.14 | 13.0% | 2.18 | 150 |
| Ivy League | 370 | 31.10 | -2.46 | 51.6% | +0.13 | -0.08 | 0.07 | 7.6% | 2.97 | 106 |
| Southern Conference | 475 | 30.90 | -2.74 | 56.7% | -0.65 | +0.94 | 0.11 | 9.7% | 1.26 | 140 |
| Coastal Athletic Association | 399 | 30.60 | -2.73 | 53.2% | -0.35 | +0.21 | 0.17 | 17.5% | 1.00 | 131 |
| Mountain West Conference | 813 | 30.40 | -3.13 | 57.3% | -1.09 | +1.33 | 0.17 | 15.4% | 1.97 | 124 |
| Big East Conference | 902 | 29.90 | -3.68 | 54.3% | -0.64 | +0.82 | 0.09 | 11.6% | 1.44 | 105 |
| America East Conference | 570 | 29.90 | -3.62 | 60.1% | -0.98 | +1.05 | 0.07 | 14.7% | 0.88 | 166 |
| Patriot League | 681 | 29.80 | -3.78 | 59.1% | -0.66 | +0.49 | 0.07 | 12.6% | 1.47 | 165 |
Officials who work one conference more than any other: the share of their conference games that league gets, and how the home team does with them there.
| Official | Conference | G | Share | Fouls/G | vs own avg | Home win % | Expected | z |
|---|---|---|---|---|---|---|---|---|
| Tim Daley | Big Ten Conference | 207 | 30.8% | 34.50 | -0.19 | 53.2% | 54.0% | -0.25 |
| Bob Scofield | Pac-12 Conference | 167 | 26.6% | 29.80 | -1.00 | 58.0% | 58.1% | -0.03 |
| Jules Gallien | Atlantic Coast Conference | 145 | 26.5% | 31.70 | -0.94 | 57.2% | 56.6% | 0.18 |
| Melissa Barlow | Pac-12 Conference | 190 | 25.2% | 33.00 | -0.18 | 53.2% | 55.1% | -0.56 |
| Chuck Gonzalez | Pac-12 Conference | 137 | 24.8% | 31.90 | -0.41 | 60.7% | 58.5% | 0.55 |
| Kyle Bacon | Pac-12 Conference | 157 | 23.8% | 32.40 | +0.04 | 58.3% | 58.9% | -0.16 |
| Scott Yarbrough | Big 12 Conference | 127 | 23.7% | 35.40 | -1.19 | 58.9% | 56.4% | 0.59 |
| Bryan Brunette | Atlantic Coast Conference | 166 | 23.5% | 32.20 | +0.49 | 58.6% | 58.0% | 0.17 |
| Angie Enlund | Big Ten Conference | 159 | 23.5% | 33.10 | +0.28 | 61.0% | 56.8% | 1.14 |
| Michol Murray | Pac-12 Conference | 168 | 23.3% | 31.50 | -0.44 | 55.1% | 57.6% | -0.69 |
| Darren Krzesnik | Pac-12 Conference | 108 | 22.7% | 32.20 | -0.31 | 53.7% | 56.3% | -0.63 |
| Ed Sidlasky | Atlantic Coast Conference | 177 | 22.5% | 32.90 | +0.88 | 58.1% | 59.5% | -0.41 |
Officials with 60+ games in a conference whose foul count there sits furthest from their own average everywhere. Looser there first, then tighter.
| Official | Conference | G | Fouls/G | vs own avg | z |
|---|---|---|---|---|---|
| Katie Lukanich | Big East Conference | 69 | 27.20 | -4.95 | -7.17 |
| Joseph Vaszily | Big East Conference | 111 | 29.50 | -3.32 | -5.72 |
| Lisa Jones | Mountain West Conference | 76 | 29.00 | -3.86 | -5.37 |
| Saif Esho | Big East Conference | 72 | 29.80 | -3.14 | -5.16 |
| Tiara Cruse | Big East Conference | 64 | 29.30 | -3.90 | -5.11 |
| Karen Preato | Big East Conference | 86 | 29.30 | -3.36 | -5.03 |
| Michol Murray | Mountain West Conference | 73 | 28.60 | -3.25 | -5.03 |
| Bruce Morris | Big East Conference | 72 | 28.80 | -3.22 | -4.95 |
| Michael McConnell | Southeastern Conference | 102 | 36.60 | +2.52 | 3.81 |
| Brenda Pantoja | Big 12 Conference | 73 | 31.80 | +2.39 | 3.32 |
| John Capolino | American Athletic Conference | 84 | 34.70 | +3.00 | 3.32 |
| Luis Gonzalez | Southeastern Conference | 69 | 37.90 | +3.04 | 3.32 |
| Benny Luna | West Coast Conference | 79 | 36.70 | +2.54 | 3.24 |
| Karen Preato | Southeastern Conference | 77 | 34.70 | +2.04 | 3.21 |
| Maj Forsberg | Big 12 Conference | 176 | 34.30 | +1.68 | 2.89 |
| Lisa Jones | Big 12 Conference | 139 | 34.40 | +1.52 | 2.79 |
Home wins against expected, for official and conference pairings with 60+ games. 7 pairings sit beyond two standard deviations against about 10.4 by chance. Worst for the home side first.
| Official | Conference | G | Home win % | Expected | z |
|---|---|---|---|---|---|
| Alecia Ann Murray | Big West Conference | 66 | 41.7% | 58.0% | -2.58 |
| D'Lynn Schertz | Southland Conference | 66 | 44.7% | 58.6% | -2.21 |
| Natasha Camy | Southeastern Conference | 65 | 40.4% | 51.9% | -2.04 |
| Michelle Del Duco | Horizon League | 95 | 50.0% | 57.9% | -1.79 |
| Scott Yarbrough | Southeastern Conference | 80 | 49.3% | 58.5% | -1.78 |
| Travis Jones | Southland Conference | 70 | 44.0% | 54.5% | -1.70 |
| Brian Hall | Conference USA | 76 | 78.1% | 59.6% | 2.42 |
| Felicia Grinter | Big Ten Conference | 207 | 66.5% | 58.7% | 2.36 |
| Shane Bennett | Ohio Valley Conference | 62 | 66.7% | 53.7% | 2.22 |
| Brian Hall | Southeastern Conference | 148 | 69.6% | 62.0% | 2.09 |
| Melissa Barlow | Mountain West Conference | 70 | 68.8% | 56.9% | 1.91 |
| Karen Lasuik | Big Sky Conference | 67 | 64.7% | 53.0% | 1.90 |
| Conference | Most games (conference games on record) |
|---|---|
| Atlantic Coast Conference | Joseph Vaszily (189), Karen Preato (185), Maj Forsberg (183) |
| Big Ten Conference | Michael McConnell (221), Tim Daley (207), Felicia Grinter (207) |
| Southeastern Conference | Roy Gulbeyan (186), Brian Garland (169), Felicia Grinter (157) |
| Big 12 Conference | Brian Hall (181), Maj Forsberg (176), Felicia Grinter (172) |
| American Athletic Conference | Joseph Vaszily (118), Ed Sidlasky (93), Karen Preato (93) |
| Atlantic 10 Conference | Shawn Goode (74), John Capolino (71), Brandon Enterline (70) |
| Big East Conference | Joseph Vaszily (111), Tom Danaher (100), Rod Creech (98) |
| Sun Belt Conference | Kylie Galloway (80), Jody Cantrell (70), Britton Sherry (69) |
| Horizon League | Angie Enlund (101), Michelle Del Duco (95), Kalei Enterline (86) |
| Conference USA | Bill Larance (106), Carla Fountain (92), Brian Hall (76) |
| MAAC | Scott Berkins (80), Brandon Appel (76), Anthony Reddin (73) |
| Mid-American Conference | Kalei Enterline (99), Michelle Del Duco (97), Angie Enlund (97) |
| Mountain West Conference | Michael Price (94), Lisa Jones (76), Chuck Gonzalez (74) |
| Missouri Valley Conference | Missy Brooks (87), Doug Knight (83), Brad Maxey (82) |
| Ohio Valley Conference | Joshua Hall (67), Shane Bennett (62), Daniel Drake (55) |
| West Coast Conference | Brian Woods (85), Benny Luna (79), Kenneth Nash (75) |
| Big Sky Conference | Tyler Trimble (103), Bob Scofield (89), Steve Belnap (74) |
| Southland Conference | John Fletcher (79), LaShonda Kidd (70), Travis Jones (70) |
| Big West Conference | Esther Hsu (71), Deon Lewis (66), Alecia Ann Murray (66) |
| Atlantic Sun Conference | Jelisse Oliver (50), Deanna Jackson (39), Chastity Taylor (38) |
| Patriot League | Ken Kelly (54), Tim Greene (46), Kevin Farlow (40) |
| Big South Conference | Kristie Weed (59), Whitney Armstrong (52), Roy Jackson (51) |
| Northeast Conference | Ken Kelly (49), Kevin Farlow (43), Laura Isabelle (42) |
| Summit League | Zac Brost (90), Missy Brooks (86), Erica Parker (69) |
| America East Conference | Norma Jones (56), Kevin Dillard (49), Courtney Robinson (44) |
| Southwestern Athletic Conference | LaShonda Kidd (56), Neonta Williams (45), Sarah Fleming (42) |
| Southern Conference | Kristie Weed (49), Tim Buckner (36), Dawn Marsh (35) |
| Mid-Eastern Athletic Conference | Edward Matthews (72), Tim Bryant (58), Roy Jackson (48) |
| Coastal Athletic Association | Tim Bryant (26), Roy Jackson (24), Kevin Sparrock (24) |
| Ivy League | Anthony Reddin (26), Ashley Rivera (25), Brandon Appel (24) |
Officials are not employed by a league. They are independent contractors who take assignments from several conferences at once and get themselves there. This section rebuilds every official's itinerary from the venues on record: each hop between consecutive games is measured city to city, a hop of 300 miles or less counts as a drive, anything longer counts as a flight. 99.7% of games could be placed on the map (46,756). Miles are straight-line, so real road miles run higher. Two games in two cities on the same day cannot both be right, so those hops are dropped. 31 names do it three or more times, which almost always means two officials share the name, and they are left out of the rankings.
Across everyone on record that is 50,840,044 miles, 44,675,989 of them in the air over 59,942 flights. The typical official with 150+ games covers 14,326 miles a season, 17.4 flights, about 461 miles per trip between games.
Per season, straight-line, every hop between games counted.
| Official | Seasons | Miles/season | Air miles | Ground miles | Flights/season | Hard trips | Longest hop | Most common city |
|---|---|---|---|---|---|---|---|---|
| Michael McConnell | 13 | 42,945 | 517,778 | 40,485 | 53.80 | 376 | 2,358 | Indianapolis, IN |
| Tiffany Bird | 13 | 42,529 | 539,924 | 12,898 | 42.60 | 226 | 5,577 | Las Vegas, NV |
| Brenda Pantoja | 13 | 41,575 | 524,473 | 15,866 | 45.20 | 272 | 4,321 | Las Vegas, NV |
| Michael Price | 13 | 38,228 | 483,583 | 13,274 | 42.20 | 225 | 3,292 | Las Vegas, NV |
| Doug Knight | 13 | 37,578 | 462,474 | 25,982 | 49.30 | 317 | 2,165 | Sioux Falls, SD |
| Ashlee Goode | 8 | 36,573 | 263,902 | 28,626 | 43.80 | 219 | 2,575 | Philadelphia, PA |
| Tiara Cruse | 13 | 36,136 | 442,400 | 27,285 | 42.60 | 250 | 2,616 | University Park, PA |
| Kyle Bacon | 12 | 35,346 | 408,469 | 15,571 | 40.60 | 185 | 3,360 | Los Angeles, CA |
| Billy Smith | 13 | 34,768 | 419,171 | 32,782 | 42.60 | 221 | 4,048 | Atlanta, GA |
| Demoya Pugh | 4 | 34,697 | 131,070 | 7,717 | 40.00 | 55 | 5,037 | Houston, TX |
| Ed Sidlasky | 13 | 34,643 | 421,934 | 28,414 | 41.80 | 207 | 2,637 | Coral Gables, FL |
| Melissa Barlow | 13 | 34,205 | 428,901 | 15,632 | 40.50 | 204 | 3,680 | Las Vegas, NV |
Fewest miles per game among the busiest officials. These are the ones a conference can call on a day's notice.
| Official | G | Miles/game | Miles/season | Flights/season | Most common city |
|---|---|---|---|---|---|
| Ken Kelly | 331 | 184.5 | 4,889 | 4.20 | Philadelphia, PA |
| Scott Berkins | 344 | 186.1 | 5,616 | 4.10 | Philadelphia, PA |
| Elizabeth Kuzmeski | 336 | 203.6 | 5,044 | 5.00 | Boston, MA |
| Anthony Reddin | 332 | 216.5 | 6,280 | 6.90 | Philadelphia, PA |
| Michelle Del Duco | 316 | 257.5 | 6,001 | 4.30 | Detroit, MI |
| Brandon Appel | 329 | 265.6 | 9,415 | 9.10 | Philadelphia, PA |
| Tom Hallead | 310 | 281.8 | 6,437 | 7.30 | Detroit, MI |
| Brad Maxey | 482 | 297.2 | 10,700 | 13.40 | Chicago, IL |
A hard trip is a flight on no rest: more than 300 miles between a game one day and a game the next. On 14,139 occasions an official worked a game straight off one (15.39% of all assignments on the map). The officials who take the most of them, then the single worst ones.
| Official | G | Hard trips | 3 cities in 3 days | Flights/season | Heaviest week (mi) |
|---|---|---|---|---|---|
| Michael McConnell | 1005 | 376 | 268 | 53.80 | 6,390 |
| Doug Knight | 867 | 317 | 172 | 49.30 | 7,108 |
| Nykesha Thompson | 769 | 279 | 207 | 39.70 | 6,475 |
| Brenda Pantoja | 799 | 272 | 181 | 45.20 | 8,344 |
| Natasha Camy | 629 | 254 | 165 | 40.50 | 6,338 |
| Tiara Cruse | 789 | 250 | 147 | 42.60 | 7,373 |
| Denise Brooks | 764 | 250 | 115 | 41.80 | 5,887 |
| Fatou Cissoko-Stephens | 753 | 244 | 232 | 37.20 | 8,751 |
| Tiffany Bird | 718 | 226 | 128 | 42.60 | 8,285 |
| Michael Price | 723 | 225 | 108 | 42.20 | 7,660 |
| Official | Date | From | To | Miles | Game | Fouls |
|---|---|---|---|---|---|---|
| Michael Price | 2022-12-19 | Kihei, HI | Fort Collins, CO | 3,292 | Regis University REGIS UNIVERSITY at Colorado State Rams | 30 |
| Brad Schmidt | 2024-12-19 | Denver, CO | Makawao, HI | 3,277 | Nevada Wolf Pack at Miami Hurricanes | 29 |
| Esther Hsu | 2026-01-01 | Salt Lake City, UT | Honolulu, HI | 2,992 | UC Riverside Highlanders at Hawai'i Rainbow Warriors | 34 |
| Brian Woods | 2022-01-23 | Honolulu, HI | Tucson, AZ | 2,965 | Colorado Buffaloes at Arizona Wildcats | 33 |
| Brian Woods | 2023-02-18 | Tucson, AZ | Honolulu, HI | 2,965 | Long Beach State Beach at Hawai'i Rainbow Warriors | 28 |
| Cheryll Blue | 2025-11-29 | Carolina, Puerto Rico | Tucson, AZ | 2,947 | Cal State Bakersfield Roadrunners at Arizona Wildcats | 37 |
| Teresa Turner | 2019-12-20 | Maui, HI | Spokane, WA | 2,845 | Missouri State Lady Bears at Gonzaga Bulldogs | 30 |
| Kyle Bacon | 2023-12-21 | Makawao, HI | St. George, UT | 2,788 | Oklahoma State Cowgirls at Oregon Ducks | 35 |
| Bryan Brunette | 2026-01-08 | Boston, MA | Stanford, CA | 2,690 | Wake Forest Demon Deacons at Stanford Cardinal | 34 |
| Cynthia Do | 2025-12-29 | Stanford, CA | Cambridge, MA | 2,688 | Delaware Blue Hens at Harvard Crimson | 37 |
The heaviest three-day stretches on record: three games, three different cities, the miles across both hops.
| Official | Date | From | To | Miles | Game | Fouls |
|---|---|---|---|---|---|---|
| Brian Woods | 2023-02-18 | Tucson, AZ | Honolulu, HI | 3,664 | Long Beach State Beach at Hawai'i Rainbow Warriors | 28 |
| Brenda Pantoja | 2024-02-02 | Winston-Salem, NC | Seattle, WA | 3,632 | Utah Utes at Washington Huskies | 25 |
| Doug Knight | 2019-12-22 | Boca Raton, FL | Lubbock, TX | 3,557 | UL Monroe Warhawks at Texas Tech Lady Raiders | 38 |
| Michael Price | 2016-02-19 | Piscataway, NJ | Tempe, AZ | 3,514 | Washington State Cougars at Arizona State Sun Devils | 35 |
| Michael Price | 2015-01-17 | Stanford, CA | Lawrence, KS | 3,506 | Baylor Bears at Kansas Jayhawks | 32 |
| Bill Larance | 2025-11-12 | Las Cruces, NM | Worcester, MA | 3,482 | Brown Bears at Holy Cross Crusaders | 34 |
| Tiara Cruse | 2026-01-22 | Seattle, WA | Blacksburg, VA | 3,442 | Clemson Tigers at Virginia Tech Hokies | 46 |
| Stacie Blow | 2026-02-28 | Harrisonburg, VA | Stephenville, TX | 3,442 | California Baptist Lancers at Tarleton State Texans | 46 |
Each official compared with themselves: the games they worked straight off a flight on no rest against their other games. Fouls per game the whistle moves +0.15 fouls after a hard trip against the same official's other games (223 officials), inside what chance would give (p = 0.28). Home foul lean the lean moves -0.19 fouls after a hard trip against the same official's other games (223 officials), unlikely to be chance on its own (p = 0.027), though it is one of four tests here, so treat it as a lead rather than a finding. Home results the home side moves -0.00 wins per game after a hard trip against the same official's other games (223 officials), inside what chance would give (p = 0.88). Technicals technicals moves -0.01 per game after a hard trip against the same official's other games (223 officials), inside what chance would give (p = 0.54).
League-wide, by rest and by how the official got there (stay means the same city as the last game):
| Rest | Got there by | G | Fouls/G | vs D-I | Techs/G |
|---|---|---|---|---|---|
| 2-3 days | drive | 9331 | 33.66 | +0.15 | 0.16 |
| 2-3 days | fly | 15910 | 33.55 | -0.04 | 0.17 |
| 2-3 days | stay | 1383 | 33.25 | -0.45 | 0.14 |
| 4+ days | drive | 14686 | 33.60 | +0.07 | 0.17 |
| 4+ days | fly | 16216 | 33.52 | -0.16 | 0.16 |
| 4+ days | stay | 1646 | 33.65 | +0.15 | 0.14 |
| no rest | drive | 9460 | 33.03 | -0.46 | 0.16 |
| no rest | fly | 14139 | 33.50 | -0.07 | 0.16 |
| no rest | stay | 4351 | 33.89 | +0.21 | 0.16 |
55 officials in this study also turn up in the W (matched by name, since ESPN gives officials no ID). 17 of them have 100+ W games and 150+ women's college games, enough to compare the same person in both. Their whistles track each other (correlation 0.67), so style travels with the official. But the W squeezes it: the spread of those same officials is 1.39 fouls a game in college and 1.13 in the W, about 81% as wide. The same people sit closer to the middle in the W. One league office setting one standard would do that, though the data can only show the pattern, not the reason. The loosest college whistle among them, Brenda Pantoja at -4.4, is -1.9 in the W; the tightest, Angel Kent at +2.0, is +0.6.
Loosest college whistle first. Whistle is fouls per game against each league's own average; home lean is home fouls minus away fouls against each league's lean.
| Official | NCAAW G | NCAAW whistle | NCAAW home lean | W G | W whistle | W home lean |
|---|---|---|---|---|---|---|
| Brenda Pantoja | 799 | -4.41 | +0.31 | 116 | -1.86 | +0.31 |
| Michael Price | 723 | -2.16 | +0.40 | 491 | -2.52 | -0.16 |
| Daryl Humphrey | 333 | -2.13 | +1.09 | 368 | +0.06 | +0.56 |
| Tim Greene | 454 | -1.87 | -0.38 | 384 | -1.10 | +0.19 |
| Billy Smith | 796 | -1.45 | -0.87 | 291 | -0.91 | +0.19 |
| Ashley Gloss | 241 | -1.41 | -0.36 | 110 | -0.30 | +1.01 |
| Clarke Stevens | 249 | -1.27 | -0.16 | 160 | +0.28 | -0.06 |
| Jeffrey Smith | 691 | -1.22 | -0.05 | 498 | -0.26 | -0.18 |
| Denise Brooks | 764 | -0.59 | -0.12 | 292 | -1.49 | -0.16 |
| Tiara Cruse | 789 | -0.58 | -0.10 | 305 | -0.20 | -0.34 |
| Che Flores | 269 | -0.53 | +0.46 | 232 | -0.14 | +0.28 |
| Amy Bonner | 464 | -0.20 | +0.42 | 401 | -0.87 | +0.22 |
| Tiffany Bird | 718 | -0.17 | +0.31 | 152 | +1.15 | +0.02 |
| Fatou Cissoko-Stephens | 754 | +0.08 | -0.27 | 267 | +0.84 | +0.19 |
| Sue Blauch | 347 | +0.10 | -1.17 | 390 | +0.32 | -0.61 |
| Kevin Sparrock | 364 | +0.56 | +0.27 | 241 | +2.00 | +0.16 |
| Angel Kent | 346 | +2.01 | -0.76 | 116 | +0.59 | -0.04 |
55 officials work both the W and women’s college. 38 of them have worked both in the same calendar year, which means a W season from May into September or October and a college season from November into April, with the travel of both. Put on one calendar, the typical one of them works 55 games a year and covers 42,668 straight-line miles.
The turnaround is the gap between an official’s last W game and first college game that fall. Across 158 official-years it has a median of 58 days; 0 were under two weeks and 0 under one.
| Official | Years in both | Games/yr | Miles/yr | Flights/yr | Heaviest year (mi) | Hard trips (career) | Turnaround (days) |
|---|---|---|---|---|---|---|---|
| Michael Price | 10 | 84.4 | 69,698 | 65.5 | 107,347 | 245 | 32 |
| Teresa Stuck | 4 | 90.5 | 64,726 | 66.2 | 73,400 | 168 | 55 |
| Angelica Suffren | 2 | 77.5 | 63,710 | 60.5 | 64,148 | 47 | 29 |
| Denise Brooks | 3 | 91.0 | 62,865 | 69.3 | 77,753 | 269 | 30 |
| Sue Blauch | 6 | 88.2 | 58,431 | 66.3 | 76,635 | 124 | 37 |
| Billy Smith | 10 | 84.1 | 56,196 | 60.5 | 68,742 | 231 | 52 |
| Tiara Cruse | 14 | 78.1 | 54,353 | 56.0 | 88,383 | 259 | 52 |
| Jeffrey Smith | 11 | 81.3 | 54,185 | 54.1 | 82,234 | 215 | 58 |
| Brenda Pantoja | 3 | 61.3 | 52,064 | 50.7 | 67,619 | 276 | 55 |
| Fatou Cissoko-Stephens | 13 | 77.3 | 51,817 | 52.1 | 104,938 | 256 | 62 |
| Official | Year | Last W game | First college game | Days off |
|---|---|---|---|---|
| Tim Greene | 2024 | 2024-10-20 | 2024-11-04 | 15 |
| Tiara Cruse | 2024 | 2024-10-18 | 2024-11-04 | 17 |
| Michael Price | 2023 | 2023-10-18 | 2023-11-06 | 19 |
| Tiara Cruse | 2023 | 2023-10-18 | 2023-11-06 | 19 |
| Fatou Cissoko-Stephens | 2024 | 2024-10-13 | 2024-11-04 | 22 |
| Amy Bonner | 2016 | 2016-10-20 | 2016-11-11 | 22 |
| Tiara Cruse | 2021 | 2021-10-17 | 2021-11-09 | 23 |
| Jeff Wooten | 2025 | 2025-10-10 | 2025-11-03 | 24 |
| Michael Price | 2021 | 2021-10-15 | 2021-11-09 | 25 |
| Billy Smith | 2021 | 2021-10-15 | 2021-11-09 | 25 |
Each official compared with their own other games, and each game measured against its own league: whistle against that league’s average, home lean against its lean, home result against what the matchup predicts.
The first game back after switching leagues is always a season opener, and openers run differently for everyone. So the fair test is against officials who work only women’s college, in their own first game of the season: their whistle moves +0.57 fouls and the home side +0.061 wins a game; the two-league officials move +1.24 and +0.083. The gap between the two groups is inside what chance gives, so the switch itself does not show.
Officials are matched across leagues by name; ESPN gives them no ID. Miles are straight-line between arenas, counted only between games within 30 days of each other.
Nobody was flagged in advance. These are the officials whose whistle, home lean and home results sit furthest from the field, added together. Far from the field is not the same as wrong. A tight whistle is a style, and a home-results tilt over a few hundred games is usually luck. The cards say which is which.
799 games over 13 seasons (2013-11-08 to 2026-04-03), 46 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2013-14 | 26 | 56.0% | 64.1% | 33.30 | 37.00 |
| 2014-15 | 35 | 63.3% | 67.6% | 32.40 | 34.90 |
| 2015-16 | 32 | 63.6% | 68.0% | 30.20 | 35.00 |
| 2016-17 | 43 | 67.7% | 71.5% | 30.30 | 34.90 |
| 2017-18 | 48 | 52.8% | 66.1% | 30.20 | 33.40 |
| 2018-19 | 55 | 48.8% | 57.0% | 28.60 | 33.60 |
| 2019-20 | 89 | 55.0% | 60.4% | 28.70 | 34.00 |
| 2020-21 | 69 | 58.2% | 59.4% | 27.90 | 33.70 |
| 2021-22 | 79 | 62.9% | 63.3% | 28.60 | 32.90 |
| 2022-23 | 78 | 61.2% | 64.2% | 29.20 | 33.80 |
| 2023-24 | 79 | 60.0% | 65.3% | 28.20 | 33.40 |
| 2024-25 | 79 | 67.7% | 64.8% | 30.00 | 33.20 |
| 2025-26 | 87 | 65.2% | 56.8% | 29.90 | 33.60 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Saint Mary's Gaels | 13 | 3 | 23.1% | 42.0% | -1.6 |
| Kansas Jayhawks | 18 | 8 | 44.4% | 56.0% | -1.1 |
| Fresno State Bulldogs | 19 | 9 | 47.4% | 58.8% | -1.1 |
| Colorado State Rams | 25 | 12 | 48.0% | 57.3% | -1.1 |
| San Francisco Dons | 15 | 6 | 40.0% | 51.8% | -1.0 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Texas Longhorns | 27 | 21 | 77.8% | 54.4% | +2.9 |
| Oregon Ducks | 41 | 29 | 70.7% | 59.3% | +1.7 |
| Stanford Cardinal | 53 | 39 | 73.6% | 64.4% | +1.6 |
| Pepperdine Waves | 13 | 5 | 38.5% | 24.5% | +1.4 |
| San José State Spartans | 16 | 7 | 43.8% | 31.9% | +1.3 |
691 games over 13 seasons (2013-11-08 to 2026-03-27), 41 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2013-14 | 49 | 78.9% | 66.5% | 41.20 | 36.90 |
| 2014-15 | 45 | 81.4% | 79.4% | 38.00 | 34.80 |
| 2015-16 | 44 | 70.3% | 63.9% | 38.30 | 34.80 |
| 2016-17 | 28 | 81.8% | 67.9% | 37.80 | 34.70 |
| 2017-18 | 36 | 67.9% | 69.0% | 36.10 | 33.20 |
| 2018-19 | 37 | 86.2% | 78.9% | 36.10 | 33.40 |
| 2019-20 | 60 | 70.4% | 60.1% | 38.50 | 33.90 |
| 2020-21 | 41 | 48.6% | 54.7% | 37.60 | 33.50 |
| 2021-22 | 52 | 73.9% | 71.5% | 37.20 | 32.80 |
| 2022-23 | 69 | 71.7% | 67.8% | 39.00 | 33.70 |
| 2023-24 | 77 | 68.2% | 64.8% | 35.60 | 33.30 |
| 2024-25 | 73 | 73.3% | 61.6% | 36.80 | 33.10 |
| 2025-26 | 80 | 67.6% | 59.7% | 35.90 | 33.50 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Florida Gators | 22 | 6 | 27.3% | 42.9% | -1.6 |
| Florida International Panthers | 16 | 5 | 31.2% | 47.2% | -1.5 |
| UTSA Roadrunners | 17 | 4 | 23.5% | 37.1% | -1.4 |
| UTEP Miners | 29 | 13 | 44.8% | 55.1% | -1.3 |
| Rice Owls | 21 | 11 | 52.4% | 60.1% | -0.8 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Alabama Crimson Tide | 19 | 16 | 84.2% | 56.8% | +2.7 |
| Texas Tech Lady Raiders | 41 | 29 | 70.7% | 57.0% | +2.0 |
| LSU Tigers | 17 | 15 | 88.2% | 71.8% | +1.8 |
| Tennessee Lady Volunteers | 19 | 14 | 73.7% | 57.2% | +1.7 |
| West Virginia Mountaineers | 18 | 13 | 72.2% | 55.4% | +1.6 |
1,080 games over 13 seasons (2013-11-08 to 2026-04-03), 51 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2013-14 | 81 | 70.1% | 69.3% | 38.80 | 36.90 |
| 2014-15 | 75 | 69.7% | 70.6% | 35.70 | 34.80 |
| 2015-16 | 77 | 75.0% | 70.3% | 35.60 | 34.90 |
| 2016-17 | 85 | 62.0% | 66.8% | 35.60 | 34.80 |
| 2017-18 | 78 | 76.5% | 74.8% | 33.80 | 33.30 |
| 2018-19 | 79 | 65.2% | 65.9% | 36.20 | 33.30 |
| 2019-20 | 90 | 70.6% | 66.1% | 35.40 | 33.90 |
| 2020-21 | 59 | 69.6% | 64.0% | 34.40 | 33.60 |
| 2021-22 | 79 | 74.6% | 68.1% | 35.20 | 32.80 |
| 2022-23 | 88 | 77.8% | 63.6% | 36.00 | 33.70 |
| 2023-24 | 92 | 69.2% | 63.7% | 36.20 | 33.30 |
| 2024-25 | 101 | 77.2% | 63.6% | 37.00 | 33.10 |
| 2025-26 | 96 | 69.3% | 62.1% | 35.70 | 33.50 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Ole Miss Rebels | 35 | 12 | 34.3% | 53.8% | -2.8 |
| Texas Tech Lady Raiders | 33 | 9 | 27.3% | 43.6% | -2.3 |
| Missouri State Lady Bears | 21 | 9 | 42.9% | 56.4% | -1.5 |
| Middle Tennessee Blue Raiders | 14 | 7 | 50.0% | 62.6% | -1.1 |
| Penn State Lady Lions | 37 | 14 | 37.8% | 45.1% | -1.0 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Texas A&M Aggies | 37 | 29 | 78.4% | 62.5% | +2.2 |
| South Carolina Gamecocks | 39 | 31 | 79.5% | 66.3% | +2.1 |
| Baylor Bears | 46 | 41 | 89.1% | 80.1% | +1.7 |
| Iowa Hawkeyes | 52 | 38 | 73.1% | 64.4% | +1.4 |
| Tennessee Lady Volunteers | 37 | 24 | 64.9% | 54.8% | +1.4 |
886 games over 13 seasons (2013-11-11 to 2026-03-30), 49 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2013-14 | 26 | 76.5% | 71.6% | 38.90 | 36.90 |
| 2014-15 | 21 | 76.9% | 70.2% | 35.90 | 34.80 |
| 2015-16 | 43 | 64.5% | 71.5% | 34.40 | 34.90 |
| 2016-17 | 59 | 64.4% | 67.3% | 37.50 | 34.70 |
| 2017-18 | 64 | 69.4% | 68.2% | 34.80 | 33.20 |
| 2018-19 | 62 | 73.6% | 69.3% | 35.50 | 33.30 |
| 2019-20 | 83 | 71.1% | 63.4% | 35.60 | 33.90 |
| 2020-21 | 67 | 68.4% | 62.5% | 34.20 | 33.60 |
| 2021-22 | 83 | 60.6% | 62.1% | 35.00 | 32.80 |
| 2022-23 | 91 | 71.6% | 65.0% | 37.80 | 33.70 |
| 2023-24 | 86 | 75.3% | 66.8% | 37.10 | 33.30 |
| 2024-25 | 99 | 70.7% | 65.1% | 36.70 | 33.10 |
| 2025-26 | 102 | 66.3% | 62.5% | 36.40 | 33.40 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Louisiana Tech Lady Techsters | 18 | 9 | 50.0% | 68.3% | -1.9 |
| Western Kentucky Lady Toppers | 24 | 8 | 33.3% | 49.3% | -1.8 |
| BYU Cougars | 13 | 2 | 15.4% | 33.9% | -1.5 |
| Arkansas Razorbacks | 33 | 14 | 42.4% | 53.4% | -1.5 |
| Michigan State Spartans | 12 | 6 | 50.0% | 66.1% | -1.4 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Kentucky Wildcats | 33 | 24 | 72.7% | 49.6% | +3.1 |
| Texas Longhorns | 33 | 29 | 87.9% | 69.3% | +2.6 |
| Ohio State Buckeyes | 16 | 15 | 93.8% | 74.5% | +2.1 |
| Texas Tech Lady Raiders | 37 | 22 | 59.5% | 46.3% | +1.9 |
| South Carolina Gamecocks | 34 | 32 | 94.1% | 83.1% | +1.8 |
1,130 games over 13 seasons (2013-11-08 to 2026-04-01), 54 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2013-14 | 73 | 68.9% | 65.9% | 35.70 | 37.00 |
| 2014-15 | 78 | 72.2% | 74.1% | 32.60 | 35.00 |
| 2015-16 | 82 | 75.3% | 68.3% | 33.70 | 35.00 |
| 2016-17 | 83 | 74.6% | 71.5% | 33.30 | 34.90 |
| 2017-18 | 80 | 67.2% | 62.1% | 31.10 | 33.40 |
| 2018-19 | 75 | 61.4% | 63.7% | 31.80 | 33.50 |
| 2019-20 | 98 | 69.4% | 61.6% | 31.10 | 34.00 |
| 2020-21 | 56 | 60.0% | 55.6% | 32.20 | 33.60 |
| 2021-22 | 109 | 70.3% | 64.0% | 30.80 | 32.90 |
| 2022-23 | 112 | 64.9% | 61.5% | 31.80 | 33.80 |
| 2023-24 | 99 | 72.3% | 63.6% | 33.00 | 33.30 |
| 2024-25 | 88 | 63.5% | 65.7% | 30.50 | 33.20 |
| 2025-26 | 97 | 75.3% | 62.1% | 31.40 | 33.50 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| DePaul Blue Demons | 21 | 6 | 28.6% | 54.6% | -2.8 |
| Illinois Fighting Illini | 30 | 9 | 30.0% | 47.6% | -2.2 |
| Texas Tech Lady Raiders | 29 | 7 | 24.1% | 38.6% | -1.8 |
| Alabama Crimson Tide | 25 | 11 | 44.0% | 57.1% | -1.6 |
| Western Michigan Broncos | 13 | 2 | 15.4% | 33.6% | -1.6 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Iowa State Cyclones | 37 | 29 | 78.4% | 56.1% | +3.0 |
| UConn Huskies | 33 | 30 | 90.9% | 76.0% | +2.3 |
| Tennessee Lady Volunteers | 33 | 25 | 75.8% | 62.1% | +1.8 |
| Maryland Terrapins | 32 | 24 | 75.0% | 62.9% | +1.6 |
| Mississippi State Bulldogs | 29 | 22 | 75.9% | 64.6% | +1.4 |
628 games over 13 seasons (2013-11-11 to 2026-03-11), 27 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2013-14 | 31 | 77.3% | 62.2% | 36.20 | 37.00 |
| 2014-15 | 31 | 70.8% | 67.5% | 30.80 | 34.90 |
| 2015-16 | 39 | 66.7% | 72.2% | 32.50 | 35.00 |
| 2016-17 | 32 | 66.7% | 67.9% | 31.20 | 34.80 |
| 2017-18 | 37 | 64.0% | 64.6% | 31.00 | 33.30 |
| 2018-19 | 38 | 60.7% | 65.9% | 29.10 | 33.50 |
| 2019-20 | 78 | 50.7% | 61.2% | 30.80 | 34.00 |
| 2020-21 | 59 | 62.5% | 60.7% | 30.50 | 33.60 |
| 2021-22 | 64 | 64.8% | 65.1% | 28.30 | 32.90 |
| 2022-23 | 68 | 52.5% | 60.9% | 29.80 | 33.80 |
| 2023-24 | 56 | 64.6% | 63.3% | 29.40 | 33.40 |
| 2024-25 | 52 | 57.1% | 64.4% | 31.90 | 33.20 |
| 2025-26 | 43 | 71.8% | 63.8% | 31.90 | 33.50 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Wyoming Cowgirls | 14 | 4 | 28.6% | 57.5% | -2.4 |
| Arizona Wildcats | 39 | 16 | 41.0% | 53.7% | -2.0 |
| San Francisco Dons | 20 | 5 | 25.0% | 43.4% | -1.9 |
| Seattle U Redhawks | 19 | 3 | 15.8% | 30.7% | -1.9 |
| Eastern Washington Eagles | 23 | 5 | 21.7% | 37.1% | -1.9 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| BYU Cougars | 14 | 11 | 78.6% | 48.4% | +2.8 |
| Stanford Cardinal | 44 | 39 | 88.6% | 75.0% | +2.3 |
| San Diego Toreros | 17 | 11 | 64.7% | 49.7% | +1.5 |
| Montana State Bobcats | 26 | 18 | 69.2% | 59.6% | +1.2 |
| Fresno State Bulldogs | 22 | 14 | 63.6% | 54.5% | +1.0 |
1,500 games had a free-throw gap of 20 or more, 3.2 per 100 games. Officials with 150+ games, most frequent first, with the count you'd expect from their share of games.
| Official | Games | Lopsided games | Expected | Per 100 |
|---|---|---|---|---|
| Felicia Young | 151 | 11 | 4.80 | 7.28 |
| Jamie Broderick | 590 | 40 | 18.90 | 6.78 |
| Traci Drake | 171 | 11 | 5.50 | 6.43 |
| Tiffany Bridges | 216 | 13 | 6.90 | 6.02 |
| Chastity Taylor | 152 | 9 | 4.90 | 5.92 |
| Andrew Bills | 153 | 9 | 4.90 | 5.88 |
| Kayla Maxey | 311 | 18 | 10.00 | 5.79 |
| Tim Buckner | 190 | 11 | 6.10 | 5.79 |
| Date | Away | Home | Away pts | Home pts | Home FTA − away | Home fouls − away | Official | Official | Official |
|---|---|---|---|---|---|---|---|---|---|
| 2024-12-19 | William & Mary Tribe | Maryland Terrapins | 57 | 107 | +44 | -16 | Brian Hall | Jamie Broderick | |
| 2019-03-14 | Presbyterian Blue Hose | Radford Highlanders | 49 | 60 | +43 | -19 | Brian Wallace | ||
| 2013-11-16 | Kansas State Wildcats | UTEP Miners | 39 | 84 | +39 | -20 | Lisa Jones | Felicia Grinter | |
| 2023-12-12 | McNeese Cowgirls | LSU Tigers | 44 | 133 | +39 | -17 | Michael McConnell | Maggie Tieman | Marla Gearhart |
| 2016-11-22 | SE Louisiana Lady Lions | Baylor Bears | 38 | 109 | +39 | -21 | Eric Brewton | Brian Garland | Tiffany Bird |
| 2019-11-05 | Fort Valley State Wildcats | Troy Trojans | 52 | 101 | +38 | -17 | Tim Buckner | Jessica Fortner | |
| 2015-11-24 | Alabama State Lady Hornets | Georgia Tech Yellow Jackets | 54 | 80 | +37 | -15 | Joseph Vaszily | Shawn Goode | Mark McClenney |
| 2025-11-15 | Saint Louis Billikens | Murray State Racers | 88 | 96 | +36 | -21 | Troy Winders | Angel Kent | Terra Moyers |
| 2017-01-01 | Kansas Jayhawks | Baylor Bears | 43 | 90 | +36 | -17 | Doug Knight | Maj Forsberg | Marc Merritt |
| 2021-12-21 | Alabama State Lady Hornets | North Carolina Tar Heels | 47 | 83 | +36 | -16 | Carla Fountain | Tim Bryant | Roy Jackson |
Fouls, free throws and home-floor advantage season by season, with the number of officials who worked at least one game on the feed.
| Season | Games | Officials | Fouls/game | FTA/game | Home win (true home) | OT |
|---|---|---|---|---|---|---|
| 2013-14 | 1703 | 387 | 37.0 | 39.5 | 66.9% | 4.4% |
| 2014-15 | 1549 | 379 | 34.8 | 35.8 | 68.9% | 4.5% |
| 2015-16 | 1740 | 380 | 34.9 | 33.9 | 68.1% | 3.6% |
| 2016-17 | 1993 | 430 | 34.8 | 34.1 | 67.5% | 3.6% |
| 2017-18 | 1813 | 379 | 33.3 | 33.2 | 65.2% | 3.9% |
| 2018-19 | 1726 | 352 | 33.4 | 33.0 | 66.5% | 3.6% |
| 2019-20 | 5113 | 767 | 33.9 | 33.6 | 61.5% | 4.7% |
| 2020-21 | 3539 | 617 | 33.6 | 33.3 | 58.1% | 4.4% |
| 2021-22 | 5103 | 776 | 32.8 | 32.2 | 62.1% | 4.2% |
| 2022-23 | 5498 | 818 | 33.7 | 33.4 | 63.7% | 4.6% |
| 2023-24 | 5575 | 806 | 33.3 | 32.6 | 63.0% | 3.9% |
| 2024-25 | 5667 | 823 | 33.2 | 32.3 | 63.8% | 3.9% |
| 2025-26 | 5825 | 821 | 33.5 | 33.2 | 62.6% | 4.3% |
Source: ESPN's public game summaries, pulled game by game for every Division I women's game on the feed from 2013-14 to 2025-26 (46,844 games with at least two officials listed; games with no crew on record are left out of referee tables). Officials come from the game info block; team totals and every player line come from the box score. Where ESPN's team foul field is blank, team fouls are the sum of the player lines.
Baselines: home-win rate is compared with a matchup expectation (both teams' records that season without that game, log5, shifted to the national home rate in the same season), true home games only; neutral-floor games are excluded from every home-floor number. Team and coach pairings use the same matchup expectation, game by game. Player pairings are compared with the player's own per-30-minute rates that season.
What the method can't see: which crew member made which call, foul type, the score when a call came, or whether a call was right. It measures tendencies and outcomes. College crews are also less stable than NBA crews and ESPN's college officials list is incomplete in the early seasons, so the early years carry fewer games per official. Before 2019-20, ESPN lists the crew for only about a third of women's college games, nearly all of them in the five biggest conferences, so the early seasons describe the power conferences more than the whole division.
NCAAM
Every Division I men's game on ESPN's feed from 2010-11 to 2025-26, every official on the floor, every box score. Referees, the teams and players in front of them, and where the numbers are flat.
Officials should be invisible. The best crew is the one nobody talks about after the game. The grade rewards sitting near the middle of the pack in every category at once, not a tight whistle or a loose one, a home lean or a road lean. I take every active official with 150+ games, find the median in each category, and measure how far each one sits from it in typical spreads (the median absolute deviation, scaled to match a standard deviation). Before measuring, each number is pulled part of the way toward the middle, by an amount set for that category: the more a category moves by luck from game to game, and the fewer games an official has, the further the pull. The distance is then measured in the spread of the raw numbers, so a category that is mostly luck leaves nearly everyone at an A there, and a noisy category can't hand out the same F as a steady one. One side effect is deliberate: an official with few games can't be shown yet to stand out, so newer officials sit closer to an A until their games say otherwise. Within half a spread of the median in a category is an A there; within one, a B; within one and a half, a C; within two, a D; further, an F. Consistency is the one category where only the wide side counts, and it has two parts: from one night to the next, how much the foul total bounces around after allowing for who was playing (overtime scaled back to a regulation game), and how often the free throws came out one-sided. Each part is measured in the same spreads as every other category, steadier than the median counts as zero, the category takes the worse of the two. All three officials on the floor share the numbers of a game, so read it as a crew habit until the crews are split. The overall grade is the average distance across all 6 categories. An A official is one whose numbers you couldn't tell from the pack. An F is one whose numbers you could.
Categories: Whistle (fouls per game vs Division I in the same seasons), Home lean (home-minus-away fouls vs the Division I lean), Home results (home wins vs expected), Free-throw lean (home-minus-away free throws vs the Division I lean), Technicals (technicals per game vs the Division I in the same seasons), Consistency (night-to-night spread of the whistle around what the two teams usually commit, and lopsided free-throw nights; only the wide side counts, and the worse part sets the letter). 320 active officials graded; median values: Whistle +0.05, Home lean +0.16, Home results -1.83, Free-throw lean -0.19, Technicals -0.01, Consistency 0.00.
For every official with 300+ games I split the games into odd and even (by date), work out each category on both halves, and check how well one half predicts the other across officials. The first number is that correlation; the second projects it to a full career. Near 1 means the category describes the official; near 0 means it is luck. The rule: a category counts toward the grade only if the full-career number is at least 0.2 in the NBA or college game. Close games (home wins in games decided by three or fewer) and the half-to-half foul gap fail it and are shown here but left out of the grade. All three officials on the floor share each game, so even the categories that repeat describe a crew habit as much as a person.
| Category | Odd vs even | Full career | Officials | In the grade |
|---|---|---|---|---|
| Whistle | +0.90 | +0.95 | 274 | yes |
| Home lean | +0.55 | +0.71 | 274 | yes |
| Home results | +0.32 | +0.49 | 274 | yes |
| Free-throw lean | +0.48 | +0.65 | 274 | yes |
| Technicals | +0.60 | +0.75 | 274 | yes |
| Close games | +0.03 | +0.05 | 274 | no |
| Consistency: night spread | +0.37 | +0.54 | 274 | yes |
| Consistency: lopsided nights | +0.33 | +0.50 | 274 | yes |
A 176 B 143 C 1 D 0 F 0 of 320 active officials
| Official | Games | Whistlerepeats 0.95 | Home leanrepeats 0.71 | Home resultsrepeats 0.49 | Free-throw leanrepeats 0.65 | Technicalsrepeats 0.75 | Consistencyrepeats 0.50 | Distance | Grade |
|---|---|---|---|---|---|---|---|---|---|
| Chris Ford | 342 | A | A | A | A | A | A | 0.09 | A |
| Steven Anderson | 664 | A | A | A | A | A | A | 0.11 | A |
| Blake Harris | 284 | A | A | A | A | A | A | 0.13 | A |
| Jim Bruno | 403 | A | A | A | A | A | A | 0.14 | A |
| Jason Page | 293 | A | A | A | A | A | A | 0.14 | A |
| Trey Styons | 391 | A | A | A | A | A | A | 0.15 | A |
| Tim Stewart | 597 | A | A | A | A | A | A | 0.16 | A |
| William Humes | 397 | A | A | A | A | A | B | 0.16 | A |
| Barry Mathis | 353 | A | A | A | A | A | A | 0.16 | A |
| Bharat Ramnanan | 305 | A | A | A | A | A | A | 0.16 | A |
| Steve Honacki | 420 | A | A | A | A | A | A | 0.17 | A |
| Scott Arbogast | 201 | A | A | A | A | A | A | 0.17 | A |
| Official | Games | Whistlerepeats 0.95 | Home leanrepeats 0.71 | Home resultsrepeats 0.49 | Free-throw leanrepeats 0.65 | Technicalsrepeats 0.75 | Consistencyrepeats 0.50 | Distance | Grade |
|---|---|---|---|---|---|---|---|---|---|
| Brian Zink | 314 | D | D | A | D | B | A | 1.03 | C |
| Greg Moyer | 242 | B | D | A | D | D | A | 0.99 | B |
| Lewis Garrison | 612 | B | D | B | C | B | B | 0.99 | B |
| Chance Moore | 584 | D | C | B | B | D | A | 0.96 | B |
| D.G. Nelson | 647 | D | C | B | A | B | C | 0.96 | B |
| Jackie Sanders | 230 | D | B | B | B | D | A | 0.95 | B |
| Doug Shows | 1174 | A | D | A | C | A | C | 0.94 | B |
| Lionel Butler | 460 | A | D | B | F | A | B | 0.92 | B |
| Kevin Mathis | 615 | C | B | A | A | D | C | 0.90 | B |
| Les Jones | 966 | A | C | B | D | A | C | 0.88 | B |
| Everett Summers | 212 | F | B | A | A | B | B | 0.87 | B |
| Randal Anders | 214 | F | A | B | A | B | B | 0.85 | B |
Every graded official: sortable in the Referees tab (Grade column). Distance is in median absolute deviations; 0 is dead center.
Total fouls called per game with the official on the floor, against the national average in the same seasons. Officials with 150+ games. This is the strongest signal in the data. It’s stable and it’s large, and it says nothing about fairness, only about how much contact gets called.
| Official | Games | Fouls/game | vs D-I | z |
|---|---|---|---|---|
| Ronald Brokenbrough | 237 | 40.40 | +4.84 | +10.5 |
| Everett Summers | 212 | 40.70 | +4.55 | +8.2 |
| Randal Anders | 214 | 37.90 | +3.58 | +6.4 |
| Chad Shepherd | 329 | 38.70 | +3.48 | +9.0 |
| Kyle Luckie | 163 | 37.50 | +3.39 | +5.5 |
| Jon Wall | 156 | 38.80 | +3.20 | +5.2 |
| Courtney Holmes | 181 | 38.80 | +2.99 | +5.1 |
| Jeff Malham | 608 | 38.60 | +2.93 | +9.4 |
| D.G. Nelson | 647 | 38.20 | +2.81 | +8.8 |
| Doug Aprahamian | 193 | 39.00 | +2.80 | +5.0 |
| Official | Games | Fouls/game | vs D-I | z |
|---|---|---|---|---|
| Josean Vega | 172 | 30.20 | -3.97 | -8.3 |
| Jason Deering | 169 | 30.20 | -3.91 | -7.5 |
| Tyler Sandmann | 164 | 30.10 | -3.91 | -7.6 |
| Roger Ayers | 1525 | 31.80 | -3.87 | -22.7 |
| Vasili Mallios | 232 | 30.30 | -3.73 | -8.9 |
| Matt Jarman | 205 | 31.20 | -3.05 | -6.5 |
| Gary Maxwell | 1053 | 32.80 | -3.01 | -14.2 |
| Chance Moore | 584 | 31.80 | -2.87 | -10.7 |
| Matt Lavigne | 259 | 31.80 | -2.79 | -6.8 |
| Daniel Basile | 166 | 31.30 | -2.78 | -5.0 |
Home fouls minus away fouls in true home games, against the national lean in the same seasons (home teams get called for slightly fewer fouls everywhere). Negative means the official calls even fewer on the home team than the rest of the field does; positive means the visitor gets the friendlier whistle.
| Official | Games | Home fouls − away | vs D-I | z | Home FTA edge vs D-I |
|---|---|---|---|---|---|
| Doug Shows | 1174 | -2.84 | -1.11 | -7.2 | +1.64 |
| Keith Kimble | 1404 | -2.64 | -0.91 | -6.9 | +1.42 |
| Edward Crenshaw | 427 | -3.20 | -1.45 | -5.7 | +1.86 |
| Antinio Petty | 1017 | -2.45 | -0.70 | -4.4 | +1.12 |
| Bert Smith | 1332 | -2.31 | -0.57 | -4.2 | +0.92 |
| Rick Crawford | 1004 | -2.40 | -0.66 | -4.1 | +0.86 |
| Lewis Garrison | 612 | -2.64 | -0.88 | -4.1 | +1.16 |
| Steve McJunkins | 912 | -2.46 | -0.70 | -4.0 | +1.05 |
| Official | Games | Home fouls − away | vs D-I | z | Home FTA edge vs D-I |
|---|---|---|---|---|---|
| Jose Carrion | 587 | -0.51 | +1.29 | +6.2 | -2.15 |
| Lionel Butler | 460 | -0.43 | +1.35 | +6.0 | -2.76 |
| Greg Moyer | 242 | -0.17 | +1.42 | +5.0 | -2.38 |
| Josh Anderson | 154 | +0.09 | +1.69 | +4.7 | -2.10 |
| Brian Zink | 314 | -0.35 | +1.30 | +4.6 | -2.42 |
| Brandon Cruz | 696 | -0.87 | +0.88 | +4.3 | -1.45 |
| Phil Sallustio Jr. | 314 | -0.52 | +1.15 | +4.2 | -1.97 |
| Ed Corliss | 378 | -0.61 | +0.97 | +4.1 | -1.45 |
Technical fouls per game as ESPN logs them for both teams combined. ESPN's box scores are thin on this field in some seasons, so treat it as a lower bound.
| Official | Games | Techs/game |
|---|---|---|
| Nomi Kidwai | 259 | 0.60 |
| Steve Orkus | 344 | 0.57 |
| James Duke | 157 | 0.57 |
| Chris Pacsi | 331 | 0.56 |
| Joe Wilbert III | 297 | 0.55 |
| Kelly Davis | 224 | 0.54 |
| Jackie Sanders | 230 | 0.54 |
| Lucas Santos | 385 | 0.54 |
Home-team win rate in true home games with the official on the floor, against what the matchup predicts: both teams’ records that season (not counting that game), adjusted to the national home rate that season. That keeps a run of early-season home games against weak visitors from looking like a friendly whistle. Across 320 officials with 150+ games, chance alone would put about 15 beyond ±2 and about 0.9 beyond ±3; the count here is 47 and 8.
| Official | Home games | Home win % | Expected | z |
|---|---|---|---|---|
| Amy Bonner | 386 | 75.1% | 67.7% | +3.6 |
| Antinio Petty | 856 | 72.7% | 67.7% | +3.4 |
| Brooks Wells | 690 | 73.9% | 69.0% | +3.2 |
| Doug Sirmons | 1219 | 71.9% | 68.3% | +3.1 |
| Rob Kueneman | 680 | 74.7% | 70.0% | +3.0 |
| Chance Moore | 504 | 74.6% | 69.5% | +2.8 |
| Rick Crawford | 868 | 71.9% | 68.0% | +2.7 |
| Jeb Hartness | 871 | 71.4% | 67.6% | +2.7 |
| Official | Home games | Home win % | Expected | z |
|---|---|---|---|---|
| J.W. Lucas | 349 | 61.6% | 69.5% | -3.6 |
| Lionel Butler | 435 | 57.5% | 64.2% | -3.3 |
| Nathan Hall | 466 | 59.4% | 65.9% | -3.2 |
| Michael Luckie | 502 | 61.4% | 66.8% | -2.9 |
| Daryl Gelinas | 471 | 62.0% | 67.4% | -2.8 |
| Michael McCloskey | 348 | 60.6% | 66.8% | -2.7 |
| Wilson Holland | 357 | 62.5% | 68.1% | -2.7 |
| Barry Mathis | 334 | 61.4% | 67.4% | -2.6 |
Games where the matchup made one team at least a 70% favorite, judged from both teams' strength that season (scoring margins against who they played) and where the game was played. Nationally, underdogs won 13.3% of 52,706 such games, against 13.4% expected. Each official is measured against the upsets expected in that official's own games, not a league average. Officials with 75+ of them, most underdog wins first. 322 officials: 24 beyond ±2 (chance: about 14.7), 2 beyond ±3 (chance: about 0.9).
| Official | Games | Underdog wins | Expected | z |
|---|---|---|---|---|
| Scott Bennett | 90 | 24.4% | 12.9% | +3.4 |
| Mason Siler | 86 | 23.3% | 11.9% | +3.4 |
| Michael Wallace | 159 | 22.0% | 14.1% | +3.0 |
| John Dillon | 156 | 21.8% | 14.5% | +2.6 |
| Nomi Kidwai | 126 | 23.0% | 14.9% | +2.6 |
| Derek Schulte | 85 | 23.5% | 14.0% | +2.6 |
| Trey Styons | 225 | 18.2% | 12.7% | +2.6 |
| Chuck Jones | 569 | 17.4% | 13.8% | +2.6 |
| Brad Ferrie | 308 | 8.8% | 14.3% | -2.9 |
| Martin Cichocki | 188 | 9.0% | 15.2% | -2.4 |
| Danny Chance | 153 | 8.5% | 14.3% | -2.1 |
| Lamar Simpson | 600 | 9.8% | 12.5% | -2.1 |
| Bruce Rothwell | 123 | 8.1% | 14.4% | -2.0 |
| Marques Pettigrew | 591 | 9.8% | 12.4% | -2.0 |
| Doran Gotschall | 214 | 8.4% | 12.8% | -2.0 |
| Carl Luciano | 182 | 9.9% | 14.9% | -2.0 |
Home-team win rate in true home games decided by three points or fewer, officials with 300+ games.
| Official | Games | Close games | Home wins in them |
|---|---|---|---|
| Keith Patterson | 325 | 12.3% | 34.4% |
| Jeff Pon | 393 | 16.0% | 38.0% |
| Glen Mayberry | 568 | 17.4% | 38.3% |
| Randy Richardson | 475 | 13.5% | 38.5% |
| Barry Mathis | 353 | 20.1% | 38.8% |
| J.W. Lucas | 386 | 20.7% | 40.0% |
| Amy Bonner | 443 | 12.6% | 70.2% |
| Michael Palau | 481 | 14.3% | 68.3% |
| Mike Millione | 394 | 16.2% | 67.8% |
| Shane Staggs | 321 | 14.3% | 67.5% |
| Matthew Rukasin | 356 | 13.2% | 67.4% |
| Ed Corliss | 378 | 15.3% | 64.7% |
Every pairing with 12+ games. Expected wins come from each game’s matchup (both teams’ records that season without that game, and the home floor), so a strong team is expected to win and drawing the big games is not held against anyone; the question is only whether the team did better or worse than that with this official. 8,578 pairings; 528 beyond ±2 (chance: about 383.7), 39 beyond ±3 (chance: about 19.9).
| Official | Team | Games | Wins | Win % | Expected | z | Foul diff vs team norm |
|---|---|---|---|---|---|---|---|
| Glen Mayberry | Grand Canyon Lopes | 13 | 5 | 38.5% | 80.2% | -4.0 | -0.36 |
| Garrick Shannon | North Carolina A&T Aggies | 22 | 3 | 13.6% | 47.1% | -3.7 | -0.11 |
| Robert Riley | Wagner Seahawks | 20 | 4 | 20.0% | 53.2% | -3.5 | +2.92 |
| Vasili Mallios | Northeastern Huskies | 14 | 1 | 7.1% | 50.6% | -3.5 | +1.42 |
| D.J. Carstensen | Fairfield Stags | 17 | 2 | 11.8% | 48.2% | -3.5 | +0.04 |
| Kevin Mathis | Southeast Missouri State Redhawks | 25 | 4 | 16.0% | 45.1% | -3.5 | +1.31 |
| Lionel Butler | Army Black Knights | 19 | 5 | 26.3% | 60.9% | -3.4 | +0.90 |
| Tim Smith | Drexel Dragons | 17 | 5 | 29.4% | 65.4% | -3.4 | -0.03 |
| Mark Schnur | George Washington Revolutionaries | 30 | 7 | 23.3% | 49.9% | -3.3 | +0.35 |
| Jim Bruno | Fairleigh Dickinson Knights | 13 | 2 | 15.4% | 53.5% | -3.3 | +2.53 |
| Rick Crawford | Stetson Hatters | 18 | 4 | 22.2% | 54.7% | -3.2 | +2.12 |
| Sean Campbell | Loyola Maryland Greyhounds | 13 | 2 | 15.4% | 54.0% | -3.1 | +0.21 |
| Official | Team | Games | Wins | Win % | Expected | z | Foul diff vs team norm |
|---|---|---|---|---|---|---|---|
| Les Jones | Temple Owls | 12 | 12 | 100.0% | 57.5% | +3.6 | -0.52 |
| Roger Ayers | North Carolina Tar Heels | 105 | 79 | 75.2% | 60.7% | +3.5 | +0.05 |
| D.J. Carstensen | Purdue Boilermakers | 86 | 69 | 80.2% | 64.2% | +3.4 | +0.03 |
| Tim Kelly | Monmouth Hawks | 16 | 12 | 75.0% | 38.1% | +3.4 | -1.79 |
| Carl Luciano | Vermont Catamounts | 44 | 42 | 95.5% | 75.6% | +3.4 | -1.47 |
| Michael McCloskey | Princeton Tigers | 13 | 12 | 92.3% | 53.1% | +3.4 | -1.95 |
| Ron Tyburski | Iona Gaels | 41 | 34 | 82.9% | 59.9% | +3.3 | -1.01 |
| Greg Langsdorf | Kent State Golden Flashes | 42 | 37 | 88.1% | 66.8% | +3.2 | -0.72 |
| Matt Potter | Richmond Spiders | 18 | 16 | 88.9% | 57.2% | +3.2 | -1.36 |
| Earl Walton | Michigan Wolverines | 29 | 22 | 75.9% | 51.9% | +3.1 | -0.95 |
| Brent Dugas | Tulane Green Wave | 23 | 18 | 78.3% | 50.9% | +3.1 | -2.38 |
| Mark Schnur | UNC Wilmington Seahawks | 21 | 18 | 85.7% | 60.5% | +3.1 | -0.32 |
Pairings with 24+ games where the team's foul differential (own fouls minus opponent's) with this official differs most from the team's normal differential that season. Negative means the team got called for fewer fouls than usual with this official on the floor.
| Official | Team | Games | Foul diff | vs team norm | FTA diff |
|---|---|---|---|---|---|
| Matthew Rukasin | Hawai'i Rainbow Warriors | 28 | -5.68 | -3.98 | +7.46 |
| Rhett Lamoreaux | North Dakota State Bison | 33 | -4.94 | -3.70 | +8.45 |
| Barry Mathis | Middle Tennessee Blue Raiders | 25 | -2.40 | -3.70 | +5.08 |
| Edward Crenshaw | SIU Edwardsville Cougars | 31 | -2.71 | -3.57 | +3.39 |
| Edward Crenshaw | Saint Louis Billikens | 27 | -3.48 | -3.36 | +5.81 |
| Rick Crawford | Florida International Panthers | 31 | -2.35 | -3.22 | +4.06 |
| Shane Land | North Florida Ospreys | 27 | -2.44 | -3.19 | +4.37 |
| Todd Von Sossan | North Dakota State Bison | 40 | -4.00 | -3.14 | +6.50 |
| Jeffrey Anderson | Illinois Fighting Illini | 24 | -0.04 | +2.98 | +0.38 |
| Steve McJunkins | Green Bay Phoenix | 25 | +2.68 | +2.76 | -5.44 |
| Brian Dorsey | Nebraska Cornhuskers | 36 | +2.17 | +2.61 | -4.08 |
| Brian Dorsey | Niagara Purple Eagles | 24 | +3.12 | +2.43 | -5.21 |
| Brent Hampton | Georgetown Hoyas | 28 | +2.46 | +2.37 | -3.68 |
| Keith Kimble | Michigan Wolverines | 32 | +0.41 | +2.34 | -1.88 |
| D.J. Carstensen | Akron Zips | 26 | +1.85 | +2.31 | -2.23 |
| Ron Groover | Tennessee Volunteers | 35 | +1.66 | +2.23 | -3.54 |
Every player-official pairing with 12+ games of 8+ minutes (college careers are short, so the bar is lower than the NBA ledger's and the pairings are noisier). Free throws, personal fouls and points are per 30 minutes and measured against the player's own rates in the same seasons. 11,902 pairings. Beyond ±3: free throws 26, fouls 16, points 12; chance alone would give about 32.1 on each.
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Mike Kitts | D.J. Richardson | ILL | 16 | 0.20 | 1.38 | -3.1 | 1.83 | 1.83 | +0.0 |
| Randy McCall | Kyle Collinsworth | BYU | 14 | 1.79 | 3.85 | -3.0 | 2.48 | 2.25 | +0.5 |
| Roger Ayers | Markell Johnson | NCSU | 19 | 0.78 | 2.45 | -2.9 | 2.15 | 2.46 | -0.8 |
| Brian Dorsey | Charles Mitchell | GT | 15 | 0.57 | 3.02 | -2.8 | 3.65 | 3.71 | -0.1 |
| Bryan Anslinger | Grant Nelson | NDSU | 15 | 1.27 | 2.78 | -2.7 | 3.54 | 3.33 | +0.3 |
| Roger Ayers | Jamir Watkins | FSU | 13 | 2.43 | 5.57 | -2.7 | 1.59 | 2.77 | -2.5 |
| Tim Comer | Antwan Burrus | LIB | 13 | 0.93 | 3.02 | -2.7 | 3.02 | 2.65 | +0.7 |
| Don Daily | Justin Hill | WICH | 13 | 1.95 | 4.40 | -2.7 | 1.67 | 1.31 | +1.0 |
| Jeff Malham | Jordan Barnes | INST | 13 | 0.70 | 2.70 | -2.6 | 1.73 | 2.08 | -0.9 |
| Randy McCall | Zylan Cheatham | SDSU | 16 | 2.70 | 5.34 | -2.6 | 4.08 | 3.50 | +1.0 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Brad Ferrie | Ore Arogundade | DRKE | 12 | 4.56 | 1.89 | +4.0 | 2.64 | 2.49 | +0.3 |
| Tim Stewart | Miles Brown | OHIO | 16 | 4.82 | 2.42 | +4.0 | 2.33 | 1.87 | +1.1 |
| Andy O'Brien | Daryl Banks III | SPU | 13 | 5.08 | 2.75 | +3.9 | 2.06 | 1.91 | +0.4 |
| Randy Heimerman | Jeremy Morgan | UNI | 15 | 5.06 | 2.44 | +3.8 | 2.31 | 2.62 | -0.6 |
| Mike Reed | Dorian Pickens | STAN | 14 | 6.37 | 3.49 | +3.8 | 1.55 | 1.89 | -0.9 |
| Lamar Simpson | Alex O'Connell | CREI | 13 | 3.59 | 1.44 | +3.6 | 0.83 | 1.72 | -2.1 |
| Anthony Jordan | William Jackson II | UGA | 12 | 4.27 | 1.52 | +3.6 | 2.66 | 2.39 | +0.5 |
| Bill Covington Jr. | Cameron Johnson | UNC | 16 | 4.87 | 2.63 | +3.6 | 2.02 | 1.96 | +0.2 |
| Frank Harvey | Mark Crowe | CP | 12 | 2.29 | 0.92 | +3.5 | 2.75 | 3.14 | -0.6 |
| Zelton Steed | Jordan Baker | ODU | 12 | 2.22 | 0.85 | +3.3 | 2.74 | 2.56 | +0.4 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Brian Dorsey | Josh Newkirk | IU | 16 | 2.60 | 1.90 | +1.1 | 5.77 | 3.28 | +4.5 |
| Blake Thomas | Camren Hunter | CARK | 12 | 4.54 | 3.83 | +0.9 | 2.61 | 1.46 | +3.5 |
| Bert Smith | Braxton Beverly | NCSU | 17 | 2.46 | 1.52 | +1.7 | 3.86 | 2.32 | +3.5 |
| Jeff Malham | Ja'Monta Black | MOST | 13 | 1.26 | 1.10 | +0.4 | 3.32 | 1.86 | +3.4 |
| Jerry Heater | Devin Wilson | VT | 12 | 2.39 | 2.73 | -0.5 | 4.70 | 2.80 | +3.4 |
| Robert Riley | Chase Audige | NU | 16 | 2.66 | 2.47 | +0.3 | 4.04 | 2.83 | +3.2 |
| Marques Pettigrew | Jase Febres | TEX | 12 | 0.64 | 0.85 | -0.4 | 4.76 | 2.33 | +3.2 |
| Owen Shortt | Marquis Eaton | ARST | 12 | 4.76 | 3.22 | +2.0 | 3.67 | 2.31 | +3.1 |
| Adam Vandenburgh | Quinton Mincey | UML | 13 | 3.69 | 2.83 | +1.2 | 2.60 | 1.48 | +3.1 |
| Jeffrey Anderson | Aaron Thompson | BTLR | 13 | 2.96 | 2.53 | +0.7 | 4.25 | 2.93 | +3.1 |
| Official | Player | Team | Games | FTA/30 | Own rate | z | PF/30 | Own rate | z |
|---|---|---|---|---|---|---|---|---|---|
| Todd Von Sossan | Daeqwon Plowden | BGSU | 21 | 3.47 | 3.10 | +0.8 | 1.53 | 2.81 | -3.0 |
| Owen Shortt | Cameron Matthews | MSST | 17 | 2.82 | 2.50 | +0.5 | 1.91 | 3.30 | -3.0 |
| Randy Heimerman | Ore Arogundade | DRKE | 12 | 1.28 | 1.66 | -0.6 | 0.99 | 2.38 | -2.9 |
| Paul Szelc | Jordan Bohannon | IOWA | 27 | 2.01 | 2.27 | -0.5 | 0.97 | 1.56 | -2.9 |
| Lamar Simpson | Alpha Diallo | PROV | 16 | 5.30 | 4.41 | +1.3 | 1.62 | 2.66 | -2.9 |
| Lamont Simpson | Duncan Robinson | MICH | 14 | 1.54 | 1.57 | -0.0 | 1.34 | 2.91 | -2.9 |
| Ron Tyburski | David Dixon | DUQ | 12 | 3.26 | 3.70 | -0.4 | 2.35 | 4.93 | -2.8 |
| Deldre Carr | Pelle Larsson | ARIZ | 16 | 3.25 | 3.45 | -0.3 | 1.74 | 2.93 | -2.7 |
| Doug Sirmons | Mamadi Diakite | UVA | 12 | 2.66 | 2.31 | +0.5 | 1.77 | 3.82 | -2.7 |
| Lamar Simpson | Billy Garrett Jr. | DEP | 16 | 4.35 | 5.44 | -1.2 | 1.97 | 2.99 | -2.7 |
The same question asked of the bench instead of the program: how does a coach's team do with each official on the floor, across every team the coach has had. Expected wins come from each game’s matchup, so a rebuilding year isn't pinned on a referee and drawing the big games is not held against anyone. Pairings need 10+ games. 762 coaches, 7,014 coach×referee pairings; 27 sit beyond three standard deviations against about 16.1 by chance, 373 beyond two against about 312.9 (chance simulated by replaying every game from its expected odds). ESPN lists one head coach per team-season, so a mid-season change is credited to the coach on the record.
| Coach | Teams | Official | G | W | Win % | Expected | z |
|---|---|---|---|---|---|---|---|
| Bill Coen | NE | Vasili Mallios | 14 | 1 | 7.1 | 50.6 | -3.52 |
| Sydney Johnson | FAIR | D.J. Carstensen | 14 | 1 | 7.1 | 47.4 | -3.44 |
| Zach Spiker | DREX | Tim Smith | 17 | 5 | 29.4 | 65.4 | -3.42 |
| Patrick Sellers | CCSU | Tom Deneen | 12 | 4 | 33.3 | 70.7 | -3.41 |
| Geno Ford | STBK | Nathan Hall | 12 | 1 | 8.3 | 50.6 | -3.38 |
| Corey Williams | STET | Rick Crawford | 11 | 1 | 9.1 | 51.1 | -3.18 |
| Brad Korn | SEMO | Kevin Mathis | 13 | 2 | 15.4 | 56.6 | -3.17 |
| Kevin Broadus | MORG | Jose Carrion | 10 | 2 | 20.0 | 63.9 | -3.13 |
| Ed Conroy | CIT | Ryan Christian | 11 | 1 | 9.1 | 46.2 | -3.09 |
| Andrew Toole | RMU | King Whetstone | 14 | 4 | 28.6 | 64.6 | -3.06 |
| Coach | Teams | Official | G | W | Win % | Expected | z |
|---|---|---|---|---|---|---|---|
| King Rice | MONM | Tim Kelly | 13 | 11 | 84.6 | 38.5 | 3.78 |
| Matt Painter | PUR | D.J. Carstensen | 84 | 69 | 82.1 | 65.0 | 3.60 |
| Rick Byrd | BEL | Bret Smith | 21 | 19 | 90.5 | 58.2 | 3.33 |
| John Becker | UVM | Carl Luciano | 42 | 40 | 95.2 | 75.3 | 3.28 |
| Ed Cooley | PROV, GTWN | Tony Henderson | 13 | 13 | 100.0 | 58.4 | 3.28 |
| Kevin Willard | HALL, VILL | Tim Clougherty | 24 | 20 | 83.3 | 53.2 | 3.27 |
| Leon Rice | BOIS | Kipp Kissinger | 11 | 11 | 100.0 | 56.8 | 3.26 |
| Billy Kennedy | TA&M | Patrick Evans | 16 | 14 | 87.5 | 51.1 | 3.25 |
| Mitch Henderson | PRIN | Michael McCloskey | 11 | 10 | 90.9 | 49.1 | 3.23 |
| Chris Mooney | RICH | Matt Potter | 17 | 15 | 88.2 | 54.8 | 3.17 |
Foul margin against the team's own norm that season, for pairings with 20+ games. Negative means fewer whistles on the coach's team than usual with that official.
| Coach | Teams | Official | G | Fouls vs norm | FTA margin |
|---|---|---|---|---|---|
| Eran Ganot | HAW | Matthew Rukasin | 27 | -4.05 | 7.41 |
| David Richman | NDSU | Rhett Lamoreaux | 31 | -3.50 | 8.16 |
| David Richman | NDSU | Todd Von Sossan | 33 | -3.31 | 7.18 |
| Steve Alford | UCLA, NEV | D.G. Nelson | 24 | -3.27 | 6.92 |
| Jeff Jones | ODU | Doug Sirmons | 22 | -3.11 | 6.32 |
| Johnny Dawkins | UCF | Rick Crawford | 25 | -3.08 | 6.56 |
| Danny Manning | TLSA, WAKE | Roger Ayers | 23 | 3.16 | -4.57 |
| Brad Underwood | ILL | Jeffrey Anderson | 22 | 2.80 | 0.91 |
| Buzz Williams | MARQ, VT, TA&M, MD | James Breeding | 20 | 2.53 | 0.25 |
| Mike White | LT, FLA, UGA | Jeb Hartness | 20 | 2.52 | 0.40 |
| Craig Neal | UNM | David Hall | 21 | 2.50 | -0.19 |
| Justin Hutson | FRES | David Hall | 22 | 2.48 | -3.23 |
Every league has its own game, and the whistle follows it. This section looks only at conference games (both teams in the same league, regular season), so a Big East night is compared with other Big East nights. 34 conferences with 150+ games on record, 51,794 conference games. Membership is taken season by season, so a team that moved is counted in the league it was in at the time. Conferences shown below are the ones that exist this season.
Fouls per game, against the Division I average in the same seasons, and how the home team does. Home foul lean is home fouls minus away fouls; negative means the home team gets the friendlier whistle. Most fouls first.
| Conference | G | Fouls/G | vs D-I | Home win % | Home foul lean | Home FTA lean | Techs/G | Close % | Lopsided/100 | Officials |
|---|---|---|---|---|---|---|---|---|---|---|
| Mid-Eastern Athletic Conference | 1316 | 38.30 | +2.20 | 59.7% | -0.94 | +1.36 | 0.53 | 21.0% | 4.64 | 106 |
| Southland Conference | 1628 | 37.80 | +2.08 | 58.2% | -0.86 | +1.54 | 0.51 | 16.2% | 3.81 | 126 |
| Southwestern Athletic Conference | 1176 | 37.60 | +1.84 | 60.2% | -0.76 | +1.22 | 0.46 | 19.9% | 3.40 | 140 |
| Southeastern Conference | 2148 | 36.90 | +1.22 | 61.9% | -1.89 | +3.14 | 0.37 | 17.5% | 4.52 | 72 |
| Big Sky Conference | 1679 | 36.90 | +1.14 | 60.5% | -0.80 | +1.53 | 0.39 | 20.1% | 2.74 | 140 |
| Big West Conference | 1431 | 36.50 | +0.97 | 57.7% | -0.67 | +1.22 | 0.36 | 17.8% | 3.63 | 138 |
| West Coast Conference | 1413 | 36.10 | +0.40 | 56.6% | -0.76 | +1.16 | 0.32 | 14.2% | 3.26 | 130 |
| Ohio Valley Conference | 1670 | 36.10 | +0.42 | 58.1% | -1.12 | +1.77 | 0.48 | 16.9% | 3.41 | 145 |
| Mid-American Conference | 1806 | 36.10 | +0.36 | 59.8% | -1.29 | +2.09 | 0.53 | 18.4% | 4.26 | 130 |
| Mountain West Conference | 1649 | 35.80 | +0.10 | 61.7% | -1.52 | +2.43 | 0.38 | 16.6% | 3.58 | 121 |
| Sun Belt Conference | 1879 | 35.70 | +0.04 | 59.8% | -1.11 | +1.88 | 0.42 | 19.1% | 3.46 | 195 |
| Northeast Conference | 1455 | 35.70 | -0.15 | 58.4% | -0.43 | +0.72 | 0.36 | 19.0% | 4.05 | 124 |
| Big 12 Conference | 1732 | 35.60 | +0.00 | 64.5% | -2.21 | +3.55 | 0.39 | 17.8% | 3.41 | 89 |
| MAAC | 1794 | 35.60 | -0.07 | 57.7% | -0.49 | +0.79 | 0.41 | 19.6% | 3.40 | 128 |
| Big South Conference | 1487 | 35.60 | -0.23 | 59.0% | -0.75 | +1.31 | 0.38 | 19.8% | 3.09 | 136 |
| Horizon League | 1627 | 35.50 | -0.04 | 58.9% | -1.36 | +2.11 | 0.37 | 17.7% | 4.00 | 177 |
| Summit League | 1281 | 35.20 | -0.48 | 61.0% | -1.13 | +1.77 | 0.30 | 15.9% | 3.36 | 137 |
| Southern Conference | 1583 | 34.90 | -0.89 | 57.9% | -0.96 | +1.51 | 0.25 | 16.9% | 2.91 | 175 |
| Atlantic 10 Conference | 2121 | 34.70 | -0.92 | 60.4% | -1.74 | +2.74 | 0.35 | 16.9% | 3.30 | 160 |
| Big East Conference | 1386 | 34.70 | -0.78 | 59.2% | -1.62 | +2.62 | 0.41 | 17.7% | 3.75 | 51 |
| Conference USA | 1911 | 34.70 | -1.19 | 62.3% | -1.59 | +2.44 | 0.46 | 19.0% | 3.92 | 173 |
| American Athletic Conference | 1452 | 34.60 | -0.95 | 58.9% | -1.26 | +2.20 | 0.41 | 18.5% | 3.65 | 111 |
| Ivy League | 841 | 34.50 | -1.31 | 58.0% | -0.25 | +0.51 | 0.19 | 19.5% | 3.21 | 125 |
| Atlantic Sun Conference | 1407 | 34.40 | -1.18 | 59.8% | -0.91 | +1.59 | 0.30 | 18.0% | 2.70 | 206 |
| Big Ten Conference | 2284 | 34.30 | -1.25 | 62.4% | -1.62 | +2.81 | 0.30 | 16.9% | 4.07 | 78 |
| Missouri Valley Conference | 1659 | 33.90 | -1.70 | 62.7% | -1.88 | +2.95 | 0.26 | 17.8% | 2.89 | 118 |
| America East Conference | 1244 | 33.80 | -1.87 | 57.2% | -0.86 | +1.17 | 0.32 | 14.6% | 3.38 | 115 |
| Patriot League | 1416 | 33.80 | -1.94 | 58.1% | -0.63 | +1.07 | 0.16 | 20.7% | 2.47 | 145 |
| Atlantic Coast Conference | 2341 | 33.20 | -2.43 | 61.8% | -1.66 | +2.70 | 0.25 | 18.0% | 2.65 | 92 |
| Coastal Athletic Association | 404 | 32.40 | -1.67 | 63.0% | -0.83 | +1.38 | 0.34 | 17.6% | 1.98 | 110 |
Officials who work one conference more than any other: the share of their conference games that league gets, and how the home team does with them there.
| Official | Conference | G | Share | Fouls/G | vs own avg | Home win % | Expected | z |
|---|---|---|---|---|---|---|---|---|
| Jackie Sanders | Mid-Eastern Athletic Conference | 166 | 72.2% | 38.50 | -0.30 | 61.5% | 59.1% | 0.66 |
| Everett Summers | Mid-Eastern Athletic Conference | 146 | 68.9% | 40.50 | -0.18 | 61.7% | 58.1% | 0.96 |
| Doug George | Southland Conference | 110 | 58.8% | 37.80 | +0.28 | 60.6% | 60.7% | -0.03 |
| Kevin Driver | Ohio Valley Conference | 119 | 53.1% | 36.80 | +0.94 | 50.0% | 57.6% | -1.74 |
| Danny Chance | Southland Conference | 149 | 53.0% | 36.40 | +0.36 | 52.6% | 60.8% | -2.15 |
| Doug Sturdivan | Big Sky Conference | 105 | 52.0% | 36.70 | +0.51 | 52.1% | 59.4% | -1.58 |
| Billy Dunlap | Southern Conference | 185 | 51.0% | 33.60 | -0.99 | 57.9% | 60.2% | -0.66 |
| Henry Howard | Southland Conference | 115 | 50.4% | 38.30 | -0.04 | 56.6% | 59.3% | -0.66 |
| Freddie Williams | Atlantic Sun Conference | 104 | 49.8% | 35.10 | -0.60 | 50.0% | 59.1% | -2.14 |
| Ervin Wilson | Missouri Valley Conference | 100 | 49.3% | 33.30 | -1.60 | 57.9% | 59.7% | -0.39 |
| Haywood Bostic | Mid-Eastern Athletic Conference | 135 | 46.6% | 38.40 | +1.23 | 57.1% | 60.0% | -0.71 |
| James Hicks | Ohio Valley Conference | 166 | 46.1% | 36.50 | -0.39 | 69.9% | 64.9% | 1.43 |
Officials with 60+ games in a conference whose foul count there sits furthest from their own average everywhere. Looser there first, then tighter.
| Official | Conference | G | Fouls/G | vs own avg | z |
|---|---|---|---|---|---|
| Jeb Hartness | Missouri Valley Conference | 62 | 30.60 | -4.49 | -6.36 |
| Jeff Malham | Missouri Valley Conference | 135 | 34.80 | -3.76 | -6.24 |
| Randy Heimerman | Missouri Valley Conference | 188 | 32.80 | -2.76 | -5.80 |
| Ron Groover | Atlantic Coast Conference | 232 | 32.50 | -2.15 | -5.16 |
| Jeffrey Anderson | Atlantic Coast Conference | 121 | 33.60 | -2.95 | -4.99 |
| Les Jones | Atlantic 10 Conference | 108 | 32.90 | -3.17 | -4.97 |
| Pat Driscoll | Atlantic Coast Conference | 149 | 31.10 | -2.73 | -4.88 |
| D.G. Nelson | Pac-12 Conference | 80 | 35.00 | -3.15 | -4.72 |
| Ron Groover | Southeastern Conference | 146 | 38.40 | +3.71 | 6.00 |
| Matt Potter | Southwestern Athletic Conference | 60 | 37.40 | +3.51 | 4.64 |
| Lee Cassell | Southeastern Conference | 127 | 38.00 | +3.18 | 4.64 |
| Vladimir Voyard-Tadal | Southeastern Conference | 143 | 37.90 | +2.36 | 4.49 |
| Gary Maxwell | Big 12 Conference | 171 | 35.00 | +2.17 | 4.28 |
| Doug Sirmons | Southeastern Conference | 98 | 37.10 | +3.10 | 4.22 |
| Tony Henderson | Southeastern Conference | 70 | 38.10 | +3.35 | 3.96 |
| Robert Riley | MAAC | 153 | 38.60 | +2.46 | 3.93 |
Home wins against expected, for official and conference pairings with 60+ games. 36 pairings sit beyond two standard deviations against about 22.8 by chance. Worst for the home side first.
| Official | Conference | G | Home win % | Expected | z |
|---|---|---|---|---|---|
| Lionel Butler | Patriot League | 71 | 43.7% | 62.3% | -3.49 |
| Tommy Morrissey | Big South Conference | 63 | 45.6% | 62.5% | -2.98 |
| Tommy Morrissey | Atlantic Coast Conference | 121 | 50.5% | 61.1% | -2.45 |
| Tim Clougherty | Atlantic 10 Conference | 106 | 50.0% | 60.3% | -2.27 |
| Zelton Steed | Southern Conference | 66 | 39.7% | 52.4% | -2.18 |
| Sean Casady | Big South Conference | 82 | 52.6% | 63.7% | -2.16 |
| Clarence Armstrong | MAAC | 69 | 77.9% | 57.3% | 3.75 |
| Doug Sirmons | Big 12 Conference | 290 | 71.3% | 62.2% | 3.23 |
| Mike Reed | Pac-12 Conference | 241 | 67.5% | 57.6% | 3.19 |
| Keith Kimble | Big 12 Conference | 182 | 72.0% | 61.1% | 3.06 |
| Randy McCall | Pac-12 Conference | 208 | 69.1% | 60.1% | 2.78 |
| Eric Curry | Mountain West Conference | 179 | 68.9% | 59.2% | 2.73 |
| Conference | Most games (conference games on record) |
|---|---|
| Atlantic Coast Conference | Roger Ayers (377), Bill Covington Jr. (258), Ron Groover (232) |
| Big Ten Conference | D.J. Carstensen (397), Larry Scirotto (374), Paul Szelc (338) |
| Southeastern Conference | Doug Shows (311), Joe Lindsay (302), Pat Adams (299) |
| Atlantic 10 Conference | Sean Hull (158), Tony Crisp (137), Earl Walton (128) |
| Conference USA | Kevin Mathis (172), Rick Crawford (150), Gary Maxwell (142) |
| Sun Belt Conference | Steve Orkus (101), Glenn Tuitt (98), Vladimir Voyard-Tadal (88) |
| Mid-American Conference | Todd Von Sossan (216), Tim Stewart (173), Chris Beaver (171) |
| MAAC | Robert Riley (153), Ron Tyburski (145), Adam Vandenburgh (128) |
| Big 12 Conference | Doug Sirmons (290), Kipp Kissinger (255), Ray Natili (193) |
| Big Sky Conference | Ryan Holmes (144), Mark Cook (112), Doug Sturdivan (105) |
| Ohio Valley Conference | James Hicks (166), Craig Murley (127), Kevin Driver (119) |
| Missouri Valley Conference | Randy Heimerman (188), Kipp Kissinger (153), Jeff Campbell (150) |
| Mountain West Conference | Verne Harris (246), Randy McCall (229), David Hall (207) |
| Southland Conference | Danny Chance (149), Brent Dugas (123), Henry Howard (115) |
| Horizon League | Chris Beaver (122), Steve McJunkins (105), John Floyd (103) |
| Southern Conference | Billy Dunlap (185), Jeremy Mosier (105), Ryan Christian (99) |
| Big South Conference | J.W. Lucas (134), A.J. Desai (117), Michael Luckie (106) |
| Northeast Conference | Guy Pagano (128), Brandon Cruz (103), Steve Ruppenthal (94) |
| American Athletic Conference | Byron Jarrett (113), Joe Lindsay (103), Mike Nance (100) |
| Big West Conference | Daryl Gelinas (147), Bill Vinovich (138), Deron White (115) |
| Patriot League | Tony Crisp (87), Tim Kelly (74), Lionel Butler (71) |
| West Coast Conference | Michael Greenstein (105), Gregory Nixon (102), Glen Mayberry (101) |
| Atlantic Sun Conference | Freddie Williams (104), Art Vaulk (77), Jon Broska (73) |
| Big East Conference | Pat Driscoll (246), James Breeding (233), Brian O'Connell (226) |
| Mid-Eastern Athletic Conference | Jackie Sanders (166), Everett Summers (146), Haywood Bostic (135) |
| Summit League | Bryan Anslinger (137), Tim Stewart (122), Todd Von Sossan (115) |
| America East Conference | Carl Luciano (75), William Flowers Jr. (69), Adam Vandenburgh (68) |
| Southwestern Athletic Conference | Byron Evans (78), Byron Jarrett (78), Ross Cullins (69) |
| Ivy League | Evon Burroughs (41), Tim Kelly (39), Carl Luciano (37) |
| Coastal Athletic Association | Andrew Barksdale (30), Matt Oblas (27), Tim Clougherty (26) |
Officials are not employed by a league. They are independent contractors who take assignments from several conferences at once and get themselves there. This section rebuilds every official's itinerary from the venues on record: each hop between consecutive games is measured city to city, a hop of 300 miles or less counts as a drive, anything longer counts as a flight. 99.6% of games could be placed on the map (91,194). Miles are straight-line, so real road miles run higher. Two games in two cities on the same day cannot both be right, so those hops are dropped. 51 names do it three or more times, which almost always means two officials share the name, and they are left out of the rankings.
Across everyone on record that is 84,617,206 miles, 73,847,896 of them in the air over 100,513 flights. The typical official with 150+ games covers 10,760 miles a season, 12.8 flights, about 436 miles per trip between games.
Per season, straight-line, every hop between games counted.
| Official | Seasons | Miles/season | Air miles | Ground miles | Flights/season | Hard trips | Longest hop | Most common city |
|---|---|---|---|---|---|---|---|---|
| Kipp Kissinger | 16 | 54,669 | 826,356 | 48,239 | 63.50 | 653 | 4,276 | St. Louis, MO |
| Eric Curry | 16 | 52,490 | 807,843 | 31,948 | 51.70 | 400 | 6,872 | Las Vegas, NV |
| Randy McCall | 16 | 50,670 | 784,726 | 25,730 | 59.90 | 528 | 6,872 | Las Vegas, NV |
| Amy Bonner | 6 | 47,122 | 262,810 | 19,910 | 48.50 | 146 | 4,185 | Des Moines, IA |
| Verne Harris | 16 | 46,606 | 714,373 | 31,006 | 56.90 | 486 | 6,484 | Las Vegas, NV |
| Doug Sirmons | 16 | 45,781 | 663,167 | 69,260 | 56.80 | 548 | 4,848 | Kansas City, MO |
| Tommy Johnson | 5 | 45,135 | 221,037 | 4,637 | 42.00 | 67 | 4,153 | Las Vegas, NV |
| Rick Crawford | 16 | 42,240 | 650,903 | 24,799 | 49.60 | 332 | 2,597 | Orlando, FL |
| Mike Reed | 16 | 41,744 | 635,813 | 31,721 | 49.80 | 378 | 6,676 | Las Vegas, NV |
| Ron Groover | 16 | 41,662 | 596,020 | 70,472 | 51.80 | 490 | 4,678 | Atlanta, GA |
| Don Daily | 16 | 41,007 | 614,824 | 41,259 | 49.60 | 363 | 4,547 | Nashville, TN |
| Gregory Nixon | 16 | 40,931 | 631,148 | 23,512 | 49.30 | 351 | 5,709 | Las Vegas, NV |
Fewest miles per game among the busiest officials. These are the ones a conference can call on a day's notice.
| Official | G | Miles/game | Miles/season | Flights/season | Most common city |
|---|---|---|---|---|---|
| Phil Sallustio Jr. | 314 | 143.6 | 2,862 | 2.00 | Brooklyn, NY |
| Ed Corbett Jr. | 340 | 163.8 | 4,898 | 2.30 | Philadelphia, PA |
| Mikerlange Fleury | 314 | 167.0 | 4,570 | 3.10 | Fairfield, CT |
| Michael Nardone | 373 | 191.6 | 7,728 | 7.20 | Fairfield, CT |
| Michael McCloskey | 372 | 197.7 | 4,387 | 3.90 | Stony Brook, NY |
| Lionel Butler | 470 | 198.9 | 5,645 | 6.10 | Baltimore, MD |
| Carl Luciano | 350 | 204.1 | 4,261 | 3.40 | Burlington, VT |
| Brian Zink | 314 | 208.7 | 4,833 | 4.20 | Boston, MA |
A hard trip is a flight on no rest: more than 300 miles between a game one day and a game the next. On 21,026 occasions an official worked a game straight off one (14.85% of all assignments on the map). The officials who take the most of them, then the single worst ones.
| Official | G | Hard trips | 3 cities in 3 days | Flights/season | Heaviest week (mi) |
|---|---|---|---|---|---|
| Kipp Kissinger | 1419 | 653 | 561 | 63.50 | 8,408 |
| Doug Sirmons | 1429 | 548 | 564 | 56.80 | 9,165 |
| Randy McCall | 1256 | 528 | 355 | 59.90 | 9,193 |
| Ron Groover | 1326 | 490 | 478 | 51.80 | 9,122 |
| Verne Harris | 1258 | 486 | 343 | 56.90 | 9,946 |
| Eric Curry | 1134 | 400 | 265 | 51.70 | 10,260 |
| Doug Shows | 1189 | 386 | 257 | 50.00 | 9,329 |
| Mike Reed | 1117 | 378 | 242 | 49.80 | 10,576 |
| Don Daily | 1135 | 363 | 182 | 49.60 | 9,353 |
| Lee Cassell | 1154 | 361 | 309 | 46.00 | 8,922 |
| Official | Date | From | To | Miles | Game | Fouls |
|---|---|---|---|---|---|---|
| Mike Roberts | 2015-12-08 | Pearl Harbor, HI | Philadelphia, PA | 4,917 | Princeton Tigers at Saint Joseph's Hawks | 30 |
| Mike Kitts | 2013-11-28 | Maui, HI | Orlando, FL | 4,662 | LSU Tigers at Saint Joseph's Hawks | 36 |
| Tommy Johnson | 2024-11-17 | Peoria, IL | Honolulu, HI | 4,153 | Weber State Wildcats at Hawai'i Rainbow Warriors | 48 |
| Eric Curry | 2026-01-22 | Arlington, TX | Honolulu, HI | 3,772 | Cal State Bakersfield Roadrunners at Hawai'i Rainbow Warriors | 50 |
| Ryan McCarty | 2018-12-17 | Honolulu, HI | San Antonio, TX | 3,710 | Bethany (KS) BETHANY (KS) at UTSA Roadrunners | 31 |
| Jimmy Casas | 2011-12-31 | Honolulu, HI | Greeley, CO | 3,360 | Northern Arizona Lumberjacks at Northern Colorado Bears | 45 |
| Gregory Nixon | 2019-11-30 | Honolulu, HI | Boulder, CO | 3,327 | Sacramento State Hornets at Colorado Buffaloes | 40 |
| Chris Beaver | 2015-11-23 | San Juan, Puerto Rico | Las Vegas, NV | 3,216 | Massachusetts Minutemen at Clemson Tigers | 41 |
| Rick Batsell | 2024-11-26 | Missoula, MT | Honolulu, HI | 3,011 | Hawaiʻi Pacific Sea Warriors at Hawai'i Rainbow Warriors | 30 |
| Johnny Harrington | 2023-12-10 | Logan, UT | Honolulu, HI | 3,010 | Hawaiʻi Pacific Sea Warriors at Hawai'i Rainbow Warriors | 27 |
The heaviest three-day stretches on record: three games, three different cities, the miles across both hops.
| Official | Date | From | To | Miles | Game | Fouls |
|---|---|---|---|---|---|---|
| Mike Roberts | 2015-12-09 | Philadelphia, PA | Lexington, KY | 5,435 | Eastern Kentucky Colonels at Kentucky Wildcats | 36 |
| Mike Kitts | 2013-11-29 | Orlando, FL | Lake Buena Vista, FL | 4,676 | Oklahoma State Cowboys at Butler Bulldogs | 31 |
| Tommy Johnson | 2024-11-17 | Peoria, IL | Honolulu, HI | 4,443 | Weber State Wildcats at Hawai'i Rainbow Warriors | 48 |
| Chris Beaver | 2015-11-24 | Las Vegas, NV | Lincoln, NE | 4,261 | Arkansas-Pine Bluff Golden Lions at Nebraska Cornhuskers | 48 |
| Tommy Johnson | 2025-12-21 | Newark, NJ | Stillwater, OK | 3,694 | Cal State Fullerton Titans at Oklahoma State Cowboys | 41 |
| Joe Lindsay | 2026-02-07 | Richmond, VA | College Station, TX | 3,569 | Florida Gators at Texas A&M Aggies | 39 |
| Chuck Jones | 2026-02-05 | Berkeley, CA | Birmingham, AL | 3,494 | Memphis Tigers at UAB Blazers | 39 |
| Kipp Kissinger | 2023-12-05 | Corvallis, OR | New York, NY | 3,458 | North Carolina Tar Heels at UConn Huskies | 31 |
Each official compared with themselves: the games they worked straight off a flight on no rest against their other games. Fouls per game the whistle moves -0.17 fouls after a hard trip against the same official's other games (266 officials), inside what chance would give (p = 0.11). Home foul lean the lean moves -0.21 fouls after a hard trip against the same official's other games (266 officials), unlikely to be chance on its own (p = 0.009), though it is one of four tests here, so treat it as a lead rather than a finding. Home results the home side moves +0.03 wins per game after a hard trip against the same official's other games (266 officials), unlikely to be chance on its own (p = 0.000), though it is one of four tests here, so treat it as a lead rather than a finding. Technicals technicals moves +0.01 per game after a hard trip against the same official's other games (266 officials), inside what chance would give (p = 0.70).
League-wide, by rest and by how the official got there (stay means the same city as the last game):
| Rest | Got there by | G | Fouls/G | vs D-I | Techs/G |
|---|---|---|---|---|---|
| 2-3 days | drive | 15031 | 35.03 | -0.10 | 0.38 |
| 2-3 days | fly | 27594 | 35.40 | +0.11 | 0.36 |
| 2-3 days | stay | 2010 | 35.23 | +0.11 | 0.38 |
| 4+ days | drive | 21875 | 34.86 | -0.24 | 0.36 |
| 4+ days | fly | 25866 | 35.44 | +0.11 | 0.36 |
| 4+ days | stay | 2064 | 34.93 | -0.11 | 0.40 |
| no rest | drive | 14091 | 34.74 | -0.44 | 0.37 |
| no rest | fly | 21026 | 35.15 | -0.21 | 0.39 |
| no rest | stay | 5739 | 35.52 | +0.41 | 0.40 |
Nobody was flagged in advance. These are the officials whose whistle, home lean and home results sit furthest from the field, added together. Far from the field is not the same as wrong. A tight whistle is a style, and a home-results tilt over a few hundred games is usually luck. The cards say which is which.
1,525 games over 16 seasons (2010-11-08 to 2026-03-29), 49 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2010-11 | 96 | 72.3% | 70.0% | 34.10 | 37.60 |
| 2011-12 | 87 | 65.1% | 69.7% | 32.50 | 36.10 |
| 2012-13 | 82 | 67.5% | 67.1% | 31.60 | 35.30 |
| 2013-14 | 99 | 69.9% | 68.3% | 33.80 | 38.40 |
| 2014-15 | 95 | 53.7% | 63.9% | 33.00 | 36.60 |
| 2015-16 | 91 | 68.0% | 66.1% | 33.60 | 38.80 |
| 2016-17 | 90 | 71.2% | 67.9% | 33.30 | 37.80 |
| 2017-18 | 97 | 65.1% | 66.7% | 31.20 | 36.30 |
| 2018-19 | 96 | 73.8% | 68.4% | 32.40 | 35.70 |
| 2019-20 | 93 | 67.5% | 70.0% | 30.50 | 35.10 |
| 2020-21 | 73 | 63.9% | 59.8% | 31.70 | 34.60 |
| 2021-22 | 98 | 66.7% | 68.1% | 30.30 | 33.30 |
| 2022-23 | 97 | 67.5% | 63.1% | 31.40 | 33.80 |
| 2023-24 | 109 | 71.0% | 67.3% | 30.30 | 33.90 |
| 2024-25 | 110 | 70.5% | 68.4% | 29.40 | 33.70 |
| 2025-26 | 112 | 76.3% | 71.8% | 30.80 | 34.90 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| DePaul Blue Demons | 33 | 10 | 30.3% | 46.0% | -2.1 |
| Western Kentucky Hilltoppers | 12 | 3 | 25.0% | 51.7% | -2.1 |
| TCU Horned Frogs | 15 | 4 | 26.7% | 49.0% | -1.9 |
| James Madison Dukes | 13 | 4 | 30.8% | 48.0% | -1.5 |
| NC State Wolfpack | 79 | 38 | 48.1% | 55.4% | -1.5 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| North Carolina Tar Heels | 105 | 79 | 75.2% | 60.7% | +3.5 |
| Duke Blue Devils | 107 | 86 | 80.4% | 69.8% | +2.6 |
| Kansas Jayhawks | 34 | 28 | 82.4% | 64.0% | +2.5 |
| South Carolina Gamecocks | 14 | 11 | 78.6% | 54.0% | +2.3 |
| West Virginia Mountaineers | 36 | 26 | 72.2% | 57.1% | +2.1 |
1,053 games over 16 seasons (2010-11-12 to 2026-03-07), 6 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2010-11 | 78 | 67.2% | 69.8% | 34.60 | 37.60 |
| 2011-12 | 65 | 68.8% | 69.3% | 32.50 | 36.10 |
| 2012-13 | 56 | 65.4% | 66.9% | 32.60 | 35.30 |
| 2013-14 | 79 | 65.7% | 68.0% | 35.00 | 38.40 |
| 2014-15 | 74 | 79.4% | 67.1% | 34.90 | 36.50 |
| 2015-16 | 69 | 63.5% | 66.9% | 35.10 | 38.70 |
| 2016-17 | 74 | 64.2% | 66.1% | 34.20 | 37.80 |
| 2017-18 | 74 | 70.5% | 70.0% | 33.10 | 36.30 |
| 2018-19 | 66 | 75.9% | 68.2% | 30.90 | 35.70 |
| 2019-20 | 68 | 66.1% | 64.2% | 30.90 | 35.10 |
| 2020-21 | 58 | 64.0% | 67.2% | 31.50 | 34.60 |
| 2021-22 | 71 | 80.3% | 68.7% | 30.00 | 33.30 |
| 2022-23 | 68 | 72.4% | 66.7% | 31.50 | 33.80 |
| 2023-24 | 81 | 71.0% | 67.3% | 33.10 | 33.80 |
| 2024-25 | 42 | 74.4% | 71.2% | 31.80 | 33.60 |
| 2025-26 | 30 | 70.4% | 67.7% | 31.40 | 34.80 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| UTEP Miners | 23 | 5 | 21.7% | 44.7% | -2.4 |
| UTSA Roadrunners | 24 | 7 | 29.2% | 46.4% | -2.1 |
| Wofford Terriers | 21 | 7 | 33.3% | 51.2% | -1.9 |
| Charlotte 49ers | 19 | 5 | 26.3% | 45.5% | -1.8 |
| South Alabama Jaguars | 12 | 5 | 41.7% | 54.2% | -1.0 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| West Virginia Mountaineers | 62 | 47 | 75.8% | 59.9% | +2.9 |
| Louisiana Ragin' Cajuns | 15 | 10 | 66.7% | 46.0% | +1.8 |
| Tulsa Golden Hurricane | 15 | 11 | 73.3% | 56.8% | +1.4 |
| Northern Iowa Panthers | 15 | 11 | 73.3% | 57.4% | +1.4 |
| Missouri State Bears | 23 | 14 | 60.9% | 49.7% | +1.2 |
584 games over 13 seasons (2013-11-15 to 2026-03-22), 19 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2013-14 | 3 | 0.0% | 78.7% | 39.00 | 38.30 |
| 2014-15 | 4 | 75.0% | 51.3% | 37.00 | 36.50 |
| 2015-16 | 5 | 60.0% | 73.5% | 40.00 | 38.70 |
| 2016-17 | 27 | 73.1% | 61.9% | 36.70 | 37.80 |
| 2017-18 | 39 | 75.0% | 72.2% | 35.20 | 36.30 |
| 2018-19 | 49 | 72.1% | 67.1% | 31.70 | 35.60 |
| 2019-20 | 48 | 83.3% | 75.1% | 31.00 | 35.10 |
| 2020-21 | 46 | 75.6% | 65.1% | 30.40 | 34.60 |
| 2021-22 | 59 | 70.4% | 73.9% | 28.00 | 33.30 |
| 2022-23 | 58 | 77.4% | 69.5% | 30.40 | 33.80 |
| 2023-24 | 76 | 75.8% | 69.9% | 32.90 | 33.80 |
| 2024-25 | 83 | 77.1% | 68.4% | 30.40 | 33.70 |
| 2025-26 | 87 | 70.4% | 70.0% | 32.70 | 34.80 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| North Texas Mean Green | 15 | 7 | 46.7% | 60.6% | -1.4 |
| Valparaiso Beacons | 12 | 2 | 16.7% | 28.7% | -1.0 |
| Cincinnati Bearcats | 14 | 5 | 35.7% | 48.0% | -1.0 |
| UAB Blazers | 13 | 8 | 61.5% | 70.0% | -0.8 |
| Saint Louis Billikens | 17 | 11 | 64.7% | 71.3% | -0.7 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Kansas Jayhawks | 33 | 31 | 93.9% | 73.3% | +3.0 |
| Texas Tech Red Raiders | 38 | 30 | 78.9% | 66.3% | +2.0 |
| LSU New Orleans Privateers | 13 | 8 | 61.5% | 44.9% | +1.4 |
| Oklahoma State Cowboys | 24 | 15 | 62.5% | 51.8% | +1.2 |
| Iowa State Cyclones | 33 | 19 | 57.6% | 51.7% | +0.8 |
1,421 games over 16 seasons (2010-11-13 to 2026-04-04), 61 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2010-11 | 70 | 67.8% | 69.6% | 36.90 | 37.50 |
| 2011-12 | 69 | 73.8% | 66.9% | 33.50 | 36.10 |
| 2012-13 | 72 | 75.8% | 66.6% | 34.50 | 35.30 |
| 2013-14 | 91 | 75.3% | 68.3% | 38.70 | 38.30 |
| 2014-15 | 90 | 69.2% | 69.5% | 35.00 | 36.50 |
| 2015-16 | 97 | 71.8% | 67.5% | 37.30 | 38.70 |
| 2016-17 | 93 | 60.3% | 64.8% | 36.00 | 37.80 |
| 2017-18 | 92 | 71.4% | 71.2% | 33.80 | 36.30 |
| 2018-19 | 100 | 75.9% | 65.4% | 33.00 | 35.60 |
| 2019-20 | 88 | 71.8% | 70.6% | 33.80 | 35.10 |
| 2020-21 | 63 | 72.0% | 65.8% | 32.00 | 34.60 |
| 2021-22 | 81 | 68.7% | 67.2% | 32.50 | 33.30 |
| 2022-23 | 91 | 73.0% | 69.2% | 32.30 | 33.80 |
| 2023-24 | 103 | 69.7% | 67.3% | 32.50 | 33.80 |
| 2024-25 | 109 | 73.1% | 70.0% | 31.10 | 33.70 |
| 2025-26 | 112 | 79.2% | 71.1% | 31.80 | 34.80 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Texas A&M Aggies | 25 | 7 | 28.0% | 50.1% | -2.5 |
| Mississippi State Bulldogs | 22 | 5 | 22.7% | 41.6% | -2.0 |
| Charlotte 49ers | 13 | 3 | 23.1% | 41.3% | -1.5 |
| South Carolina Gamecocks | 24 | 9 | 37.5% | 50.4% | -1.4 |
| Georgia Tech Yellow Jackets | 12 | 3 | 25.0% | 41.9% | -1.3 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Tennessee Volunteers | 29 | 20 | 69.0% | 48.7% | +2.5 |
| LSU Tigers | 26 | 17 | 65.4% | 47.0% | +2.2 |
| Tulsa Golden Hurricane | 22 | 16 | 72.7% | 56.0% | +1.8 |
| West Virginia Mountaineers | 70 | 38 | 54.3% | 45.9% | +1.6 |
| NC State Wolfpack | 22 | 14 | 63.6% | 48.5% | +1.6 |
1,397 games over 16 seasons (2010-11-12 to 2026-04-06), 47 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2010-11 | 62 | 58.6% | 70.7% | 37.40 | 37.50 |
| 2011-12 | 52 | 78.4% | 71.9% | 37.70 | 36.00 |
| 2012-13 | 52 | 64.0% | 68.0% | 34.70 | 35.30 |
| 2013-14 | 79 | 74.2% | 66.9% | 38.10 | 38.30 |
| 2014-15 | 92 | 71.8% | 65.4% | 33.90 | 36.50 |
| 2015-16 | 89 | 63.5% | 63.4% | 36.50 | 38.70 |
| 2016-17 | 89 | 66.7% | 60.9% | 37.70 | 37.70 |
| 2017-18 | 90 | 67.1% | 65.3% | 34.30 | 36.30 |
| 2018-19 | 99 | 78.8% | 68.7% | 32.90 | 35.60 |
| 2019-20 | 88 | 64.5% | 67.8% | 32.10 | 35.10 |
| 2020-21 | 70 | 59.3% | 61.6% | 30.20 | 34.60 |
| 2021-22 | 84 | 66.2% | 65.7% | 31.30 | 33.30 |
| 2022-23 | 100 | 80.5% | 67.2% | 32.00 | 33.80 |
| 2023-24 | 114 | 72.6% | 67.4% | 32.90 | 33.80 |
| 2024-25 | 117 | 68.4% | 71.6% | 32.50 | 33.60 |
| 2025-26 | 120 | 79.4% | 68.5% | 32.30 | 34.80 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| UNLV Rebels | 17 | 6 | 35.3% | 57.3% | -2.3 |
| Indiana State Sycamores | 29 | 9 | 31.0% | 47.0% | -1.9 |
| UTEP Miners | 17 | 6 | 35.3% | 51.8% | -1.6 |
| Charlotte 49ers | 14 | 4 | 28.6% | 46.3% | -1.5 |
| Clemson Tigers | 13 | 6 | 46.2% | 64.6% | -1.5 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Arizona Wildcats | 28 | 25 | 89.3% | 66.7% | +2.8 |
| Boise State Broncos | 13 | 11 | 84.6% | 53.8% | +2.5 |
| Northern Iowa Panthers | 51 | 36 | 70.6% | 56.0% | +2.4 |
| BYU Cougars | 19 | 15 | 78.9% | 58.9% | +2.1 |
| Milwaukee Panthers | 16 | 11 | 68.8% | 47.9% | +2.0 |
498 games over 13 seasons (2013-11-20 to 2026-03-20), 3 in the postseason.
| Season | Games | Home win % | Expected | Fouls/game | League |
|---|---|---|---|---|---|
| 2013-14 | 8 | 37.5% | 55.0% | 34.50 | 38.30 |
| 2014-15 | 7 | 71.4% | 71.1% | 34.70 | 36.50 |
| 2015-16 | 6 | 50.0% | 55.5% | 39.20 | 38.70 |
| 2016-17 | 14 | 50.0% | 61.2% | 37.30 | 37.70 |
| 2017-18 | 20 | 60.0% | 68.6% | 34.20 | 36.30 |
| 2018-19 | 30 | 57.1% | 64.6% | 35.10 | 35.60 |
| 2019-20 | 39 | 51.4% | 65.6% | 33.10 | 35.10 |
| 2020-21 | 35 | 55.9% | 62.8% | 32.50 | 34.50 |
| 2021-22 | 66 | 61.9% | 65.3% | 30.70 | 33.30 |
| 2022-23 | 59 | 59.6% | 65.3% | 31.80 | 33.80 |
| 2023-24 | 73 | 56.1% | 64.1% | 30.40 | 33.90 |
| 2024-25 | 67 | 67.2% | 66.8% | 30.50 | 33.60 |
| 2025-26 | 74 | 64.2% | 72.1% | 32.10 | 34.80 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Lehigh Mountain Hawks | 12 | 1 | 8.3% | 41.5% | -2.5 |
| Stony Brook Seawolves | 17 | 5 | 29.4% | 54.2% | -2.4 |
| Long Island University Sharks | 14 | 5 | 35.7% | 59.6% | -2.1 |
| Niagara Purple Eagles | 15 | 6 | 40.0% | 62.7% | -1.9 |
| Binghamton Bearcats | 13 | 2 | 15.4% | 35.8% | -1.8 |
| Team | Games | Wins | Win % | Expected | z |
|---|---|---|---|---|---|
| Providence Friars | 19 | 16 | 84.2% | 57.6% | +2.9 |
| Hofstra Pride | 23 | 18 | 78.3% | 59.3% | +2.0 |
| Saint Peter's Peacocks | 22 | 16 | 72.7% | 56.3% | +1.7 |
| Yale Bulldogs | 18 | 14 | 77.8% | 64.5% | +1.3 |
| Pennsylvania Quakers | 16 | 9 | 56.2% | 43.9% | +1.2 |
4,265 games had a free-throw gap of 20 or more, 4.73 per 100 games. Officials with 150+ games, most frequent first, with the count you'd expect from their share of games.
| Official | Games | Lopsided games | Expected | Per 100 |
|---|---|---|---|---|
| John Hendricks | 170 | 16 | 8.00 | 9.41 |
| David Cronin | 162 | 15 | 7.70 | 9.26 |
| James Hicks | 360 | 28 | 17.00 | 7.78 |
| Joe Wilbert III | 297 | 23 | 14.00 | 7.74 |
| Kelly Davis | 224 | 17 | 10.60 | 7.59 |
| John Dillon | 304 | 23 | 14.40 | 7.57 |
| Chad Barlow | 510 | 38 | 24.10 | 7.45 |
| Rick O'Neill | 423 | 31 | 20.00 | 7.33 |
| Date | Away | Home | Away pts | Home pts | Home FTA − away | Home fouls − away | Official | Official | Official |
|---|---|---|---|---|---|---|---|---|---|
| 2015-11-22 | Arkansas-Pine Bluff Golden Lions | Cincinnati Bearcats | 50 | 99 | +48 | -21 | Alfred Smith | Bill Jacobson | |
| 2016-11-18 | Bethesda Flames | Southern Utah Thunderbirds | 92 | 108 | +46 | -30 | D.G. Nelson | ||
| 2016-11-11 | IU Kokomo Cougars | Central Michigan Chippewas | 53 | 117 | +43 | -20 | Greg Langsdorf | ||
| 2011-11-12 | St. Thomas (FL) Bobcats | UCF Knights | 61 | 74 | +43 | -22 | Doug Sirmons | Scott Smith | |
| 2023-12-02 | Old Dominion Monarchs | Northeastern Huskies | 68 | 81 | +42 | -14 | Tom Deneen | David Powell | Ryan Corbett |
| 2013-01-17 | Southern Utah Thunderbirds | Montana Grizzlies | 67 | 73 | +42 | -20 | Rob Kueneman | K.B. Burdett | |
| 2015-11-25 | Texas Southern Tigers | UTSA Roadrunners | 91 | 80 | -41 | +20 | |||
| 2017-12-20 | UTSA Roadrunners | Nebraska Cornhuskers | 94 | 104 | +40 | -19 | Lamont Simpson | ||
| 2015-11-24 | North Carolina A&T Aggies | Virginia Tech Hokies | 60 | 90 | +40 | -17 | Brian Dorsey | ||
| 2011-11-11 | Eastern Washington Eagles | Gonzaga Bulldogs | 69 | 77 | +40 | -22 |
Fouls, free throws and home-floor advantage season by season, with the number of officials who worked at least one game on the feed.
| Season | Games | Officials | Fouls/game | FTA/game | Home win (true home) | OT |
|---|---|---|---|---|---|---|
| 2010-11 | 5534 | 950 | 37.5 | 41.6 | 68.1% | 6.1% |
| 2011-12 | 4915 | 860 | 36.1 | 39.6 | 67.7% | 5.4% |
| 2012-13 | 4859 | 712 | 35.3 | 39.1 | 67.2% | 5.5% |
| 2013-14 | 5879 | 929 | 38.3 | 44.5 | 67.6% | 6.2% |
| 2014-15 | 5863 | 938 | 36.5 | 40.0 | 67.7% | 6.1% |
| 2015-16 | 5874 | 950 | 38.7 | 42.1 | 68.1% | 5.7% |
| 2016-17 | 5876 | 942 | 37.7 | 40.7 | 67.2% | 5.8% |
| 2017-18 | 5923 | 907 | 36.3 | 38.7 | 69.1% | 6.6% |
| 2018-19 | 5816 | 901 | 35.6 | 37.9 | 67.3% | 5.8% |
| 2019-20 | 5591 | 862 | 35.1 | 37.0 | 68.4% | 5.2% |
| 2020-21 | 4148 | 716 | 34.5 | 36.0 | 64.6% | 5.1% |
| 2021-22 | 5671 | 804 | 33.3 | 34.5 | 66.7% | 5.2% |
| 2022-23 | 5991 | 842 | 33.8 | 35.8 | 67.9% | 5.8% |
| 2023-24 | 6062 | 786 | 33.8 | 38.0 | 68.2% | 5.6% |
| 2024-25 | 6095 | 782 | 33.6 | 37.9 | 68.0% | 5.2% |
| 2025-26 | 6165 | 772 | 34.8 | 40.4 | 67.8% | 5.2% |
Source: ESPN's public game summaries, pulled game by game for every Division I men's game on the feed from 2010-11 to 2025-26 (90,262 games with at least two officials listed; games with no crew on record are left out of referee tables). Officials come from the game info block; team totals and every player line come from the box score. Where ESPN's team foul field is blank, team fouls are the sum of the player lines.
Baselines: home-win rate is compared with a matchup expectation (both teams' records that season without that game, log5, shifted to the national home rate in the same season), true home games only; neutral-floor games are excluded from every home-floor number. Team and coach pairings use the same matchup expectation, game by game. Player pairings are compared with the player's own per-30-minute rates that season.
What the method can't see: which crew member made which call, foul type, the score when a call came, or whether a call was right. It measures tendencies and outcomes. College crews are also less stable than NBA crews and ESPN's college officials list is incomplete in the early seasons, so the early years carry fewer games per official.
They're formulas, not opinions, so treat them as a starting point.
Timeouts. By rule, a period's two mandatory timeouts are simply the first two taken, whoever takes them. The league only imposes one, at the next dead ball, when nobody has called one by 6:59 or by 2:59. Those imposed ones are the TV timeouts and don't count for or against the coach. Every other timeout was the coach's decision, including early ones that end up charged as mandatory, and timeouts taken to challenge a call are listed as challenges.
Timeouts. In college the media timeouts are the ones taken at the first dead ball at or under 16, 12, 8 and 4 minutes of each half. Those are imposed by the broadcast and don't count for or against the coach. Every other timeout was the coach's decision. College has no coach's challenge, so that line does not apply.
Timeouts. In the WNBA the league stops play for TV at the first dead ball under 5:00 of each quarter. A team timeout that lands in that window, first in it, is counted as the TV stop and does not count for or against the coach.
Coaching starts at 72 and moves on: whether timeouts called during an opponent run stopped it (next two minutes), whether the first possession out of a timeout scored, the best lineup's plus-minus, bench points against the other bench, and the final margin. Challenges add or subtract when the feed logs them. Small samples are weighted down, so one timeout doesn't swing a grade much.
Officiating has two parts. Consistency asks whether the whistle changed: a change point (the foul rate before and after some moment differs by 1.5+ per six-minute window) and stretches way out of the ordinary (a window at more than double or under a quarter of the game's rate) are what cost the grade; ordinary window-to-window noise barely does. Evenness is the foul differential plus half the free-throw-attempt differential, but a lopsided count only counts against the crew when the box score does not explain it: the team living in the paint draws the fouls, so the paint-points edge sets an expected lean (about a free throw for every two points of edge) and only the leftover is graded. Uneven and unexplained is a hit; uneven and explained is not. Offensive fouls, technicals and deliberate late fouls by the trailing team are left out of both. A streak is four or more straight calls on the same team, and two streaks on the side already getting the leftover lean cost one more letter.
"Lean" means the numbers leaned that way. It doesn't separate a slanted whistle from a team that simply drove more, so read it next to paint points and free-throw rate.
The write-up is assembled from the same numbers by fixed rules, so it can't invent anything and it costs nothing to run. Every sentence traces back to a play in the feed.