PER90

Premier League · England

BrentfordvTottenham Hotspur

Gtech Community Stadiumexpected lineup

Referee

Michael Oliver

55 matches on record

Fouls per game

23.1

-0% vs league

League average

23.2

fouls per match, both teams

Facts with sample sizes, not predictions. The market % beside a line is the books’ median price with margin in, and the over market runs hot.

Value — 18 live calls

Published at the price shown and scored against the closing line whatever happens next — the same rows, with staking context, are on the value board and in the track record.

What these numbers are

Not tips and not prices. Each row is the model's expected count for one player in this match — how many fouls, tackles or shots it thinks he records — plus the full spread of outcomes around it. Nothing here has been compared to a bookmaker.

  1. Step 1

    His own record

    Every completed match in the dataset, as a rate per 90 minutes. The n column is how many matches that rests on.

  2. Step 2

    Pulled toward his position

    A thin record gets pulled hard toward other players in his position, in his league — a goalkeeper toward goalkeepers, not toward the outfielders around him. How hard is fitted per market, not chosen: tackles pull about four times harder than shots on target.

  3. Step 3

    This fixture's conditions

    How much of the stat the opponent concedes, what the referee's matches look like, and home or away. This is where a projection stops being a season average.

  4. Step 4

    Scaled to expected minutes

    A rate becomes a count only once you say how long he plays. 60 minutes carries two-thirds the exposure of 90, and the distribution is built at that exposure.

How to read each column
Model /90
The model's rate for this match with the minutes taken back out — steps 1 to 3 already applied. It moves with the opponent and the referee, so it is not his raw career rate; the builder shows career and model rates side by side.
Exp. mins
Minutes the model expects. Confirmed means the lineup is out; provisional means it is guessing from his start rate, and is the widest of the three.
Projected
The expected count in this match, at those minutes. This is the number a line is set against.
Over 1.5, etc.
Read straight off the distribution beside it, not off how often he has beaten that line before. It answers what the model thinks, not what has happened.
n
Matches behind his own rate. Under 30 it turns amber: the projection is mostly his position group speaking, and §6.3 will not publish an edge on it.
Distribution
The whole spread, not just the average. The dashed amber cut is the line; jade bars beat it. An expected 1.8 fouls made of 2 every week and an expected 1.8 made of a 0 and a 5 are different bets, and this is where you see which one you have.

Counts are modelled as negative binomial rather than Poisson, because fouls and tackles are overdispersed and a Poisson would systematically underprice the tails — which is exactly where over bets live. Every market on this page cleared its calibration gate before it was allowed to produce a projection: when the model says 60%, it lands within a point or so of 60% out of sample.

Fouls v20260816-1555 · Shots v20260816-1555 · Shots on target v20260816-1555 · Fouls won v20260816-1555 · Saves v20260816-1229

Model projections

Fouls50 playersHighest: Igor Thiago, 1.42 expectedshow

What drives it Who referees the match. A strict official lifts every player on the pitch, which is the part most books price off a season average. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Igor ThiagoFWDBrentford1.5779provisional1.4241%46
026%
133%
223%
312%
45%
51%
6+1%
Dejan KulusevskiMIDTottenham Hotspur1.3577provisional1.2134%32
031%
135%
221%
39%
43%
51%
6+0%
Mateus FernandesMID1.1482provisional1.0729%75
035%
136%
219%
37%
42%
50%
6+0%
Conor GallagherMIDTottenham Hotspur1.4261provisional1.0629%67
037%
135%
219%
37%
42%
51%
6+0%
Kevin SchadeFWDBrentford1.2262provisional0.9124%73
043%
134%
216%
36%
42%
5+0%
Vitaly JaneltMIDBrentford1.0971provisional0.9123%57
042%
135%
216%
35%
41%
5+0%
Mohammed KudusFWDTottenham Hotspur0.9781provisional0.9023%51
041%
136%
216%
35%
41%
5+0%
Dominic SolankeFWDTottenham Hotspur0.9579provisional0.8621%42
043%
135%
215%
35%
41%
5+0%
Kevin DansoDEFTottenham Hotspur0.9975provisional0.8522%34
044%
134%
215%
35%
41%
5+0%
Yegor YarmolyukMIDBrentford1.0569provisional0.8521%68
045%
134%
215%
35%
41%
5+0%

10 of 50 players shown, ranked by projection. Show all 50 →

Shots50 playersHighest: Igor Thiago, 2.12 expectedshow

What drives it The player’s own shot rate, shrunk toward others in his position, then adjusted for how many shots the opponent concedes. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Igor ThiagoFWDBrentford2.3779provisional2.1260%46
016%
124%
224%
317%
410%
55%
6+3%
Dango OuattaraFWDBrentford2.5273provisional2.1159%64
016%
125%
223%
317%
410%
55%
6+4%
RicharlisonFWDTottenham Hotspur2.7264provisional2.0255%47
019%
125%
222%
315%
49%
55%
6+4%
Jaidon AnthonyFWD2.1682provisional1.9957%80
016%
127%
225%
317%
49%
54%
6+2%
Xavi SimonsMIDTottenham Hotspur2.0877provisional1.8353%54
019%
128%
224%
315%
48%
53%
6+2%
Dominic SolankeFWDTottenham Hotspur2.0179provisional1.8152%42
019%
128%
224%
315%
48%
53%
6+2%
Mohammed KudusFWDTottenham Hotspur1.8681provisional1.7250%51
020%
130%
225%
315%
47%
53%
6+1%
Dejan KulusevskiMIDTottenham Hotspur1.8577provisional1.6347%32
022%
130%
224%
314%
46%
52%
6+1%
Mathys TelFWDTottenham Hotspur2.1661provisional1.5443%52
028%
129%
221%
312%
46%
53%
6+1%
Kevin SchadeFWDBrentford2.0462provisional1.4942%73
028%
130%
221%
312%
46%
52%
6+1%

10 of 50 players shown, ranked by projection. Show all 50 →

Shots on target47 playersHighest: Igor Thiago, 1.01 expectedshow

What drives it Shot volume first, conversion second — literally: the projection is the shots model thinned by the player’s own on-target rate, so a high-volume low-accuracy shooter and a sniper price differently at the same shot count. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 0.5nDistribution
Igor ThiagoFWDBrentford1.1379provisional1.0161%46
039%
134%
218%
37%
42%
51%
6+0%
RicharlisonFWDTottenham Hotspur1.1464provisional0.8554%47
046%
133%
215%
35%
41%
5+0%
Dominic SolankeFWDTottenham Hotspur0.9179provisional0.8154%42
046%
134%
214%
34%
41%
5+0%
Dango OuattaraFWDBrentford0.9373provisional0.7852%64
048%
133%
214%
34%
41%
5+0%
Jaidon AnthonyFWD0.8082provisional0.7451%80
049%
134%
213%
33%
41%
5+0%
Kevin SchadeFWDBrentford0.8962provisional0.6545%73
055%
131%
211%
33%
41%
5+0%
Mohammed KudusFWDTottenham Hotspur0.6981provisional0.6446%51
054%
133%
211%
32%
4+1%
Dejan KulusevskiMIDTottenham Hotspur0.6877provisional0.6044%32
056%
131%
210%
32%
4+0%
Callum WilsonFWD1.1738provisional0.5740%50
060%
128%
29%
32%
41%
5+0%
Xavi SimonsMIDTottenham Hotspur0.6177provisional0.5340%54
060%
130%
28%
32%
4+0%

10 of 47 players shown, ranked by projection. Show all 47 →

Fouls won50 playersHighest: Xavi Simons, 1.49 expectedshow

What drives it How much a player runs at defenders. Dribble volume drives this far more than the referee does. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Xavi SimonsMIDTottenham Hotspur1.7177provisional1.4943%54
025%
132%
223%
312%
45%
52%
6+1%
Jaidon AnthonyFWD1.2782provisional1.1733%80
033%
135%
220%
38%
43%
51%
6+0%
Michael KayodeDEFBrentford1.2778provisional1.1131%54
036%
133%
219%
38%
43%
51%
6+0%
Keane Lewis-PotterMIDBrentford1.3274provisional1.1131%74
036%
133%
219%
38%
43%
51%
6+0%
Dango OuattaraFWDBrentford1.3073provisional1.0829%64
037%
134%
219%
38%
42%
51%
6+0%
Mateus FernandesMID1.1682provisional1.0829%75
035%
135%
219%
37%
42%
51%
6+0%
James MaddisonMIDTottenham Hotspur2.1141provisional1.0628%34
041%
131%
216%
37%
43%
51%
6+1%
Sandro TonaliMID1.1577provisional1.0027%71
039%
135%
218%
36%
42%
50%
6+0%
Destiny UdogieDEFTottenham Hotspur1.1576provisional0.9926%45
039%
135%
218%
36%
42%
50%
6+0%
Mohammed KudusFWDTottenham Hotspur1.0781provisional0.9926%51
038%
136%
218%
36%
42%
5+0%

10 of 50 players shown, ranked by projection. Show all 50 →

Saves3 playersHighest: Martin Dúbravka, 3.61 expectedshow

What drives it Opponent shot volume. A keeper’s own record carries so little that this market shrinks harder than any other on the board. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 2.5nDistribution
Martin DúbravkaGK3.6589provisional3.6166%45
04%
112%
218%
319%
417%
512%
6+18%
Antonín KinskýGKTottenham Hotspur3.3287provisional3.2360%13
06%
114%
220%
320%
416%
511%
6+13%
Caoimhín KelleherGKBrentford2.7689provisional2.7350%47
08%
119%
223%
320%
414%
58%
6+8%

2 of 7 markets have no projection for this fixture.