PER90

Premier League · England

Hull CityvManchester United

MKM Stadiumexpected lineup

Referee

Darren England

44 matches on record

Fouls per game

22.6

-2% 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 — 21 live calls

  • John Eganover 1.50 Tackles3.50 at bet365 · fair 2.15+62.8%
  • Liam Millarover 1.50 Tackles2.50 at bet365 · fair 1.73+44.5%
  • Kobbie Mainooover 1.50 Tackles2.20 at bet365 · fair 1.55+41.9%
  • Amad Dialloover 2.50 Tackles5.50 at bet365 · fair 3.90+41.0%
  • Regan Slaterover 1.50 Tackles1.91 at bet365 · fair 1.50+27.3%
  • Bruno Fernandesover 1.50 Tackles2.20 at bet365 · fair 1.73+27.2%
  • Bryan Mbeumoover 1.50 Tackles3.50 at Sky Bet · fair 2.90+20.7%
  • Matheus Cunhaover 1.50 Tackles2.50 at bet365 · fair 2.10+19.0%
  • John Eganover 0.50 Fouls won1.83 at bet365 · fair 1.57+16.7%
  • Matheus Cunhaover 0.50 Fouls1.53 at Paddy Power · fair 1.33+15.3%
  • Kobbie Mainooover 1.50 Fouls3.50 at Paddy Power · fair 3.10+13.0%
  • Liam Millarover 0.50 Shots on target2.75 at bet365 · fair 2.45+12.2%
  • Oliver McBurnieover 0.50 Fouls won1.50 at Paddy Power · fair 1.36+10.0%
  • Bruno Fernandesover 0.50 Shots on target1.57 at Sky Bet · fair 1.44+9.1%
  • Amad Dialloover 2.50 Fouls won4.20 at Paddy Power · fair 3.87+8.5%
  • Liam Millarover 0.50 Shots1.30 at Paddy Power · fair 1.20+8.3%
  • Bryan Mbeumoover 1.50 Shots1.20 at bet365 · fair 1.11+8.0%
  • Oliver McBurnieover 0.50 Tackles1.73 at bet365 · fair 1.60+7.9%
  • Matt Crooksover 0.50 Shots1.50 at Paddy Power · fair 1.40+7.1%
  • Matheus Cunhaover 0.50 Shots on target1.33 at bet365 · fair 1.25+6.6%
  • Bruno Fernandesover 1.50 Shots1.22 at Sky Bet · fair 1.15+6.6%

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

Fouls54 playersHighest: Matt Crooks, 1.32 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
Matt CrooksMIDHull City1.5474provisional1.3238%52
029%
133%
222%
310%
44%
51%
6+0%
CasemiroMIDManchester United1.2573provisional1.0729%58
035%
136%
219%
37%
42%
50%
6+0%
Oliver McBurnieFWDHull City1.1874provisional1.0127%74
038%
135%
218%
37%
42%
50%
6+0%
Manuel UgarteMIDManchester United1.5751provisional1.0127%51
041%
132%
217%
37%
42%
51%
6+0%
Luke ShawDEFManchester United1.0980provisional1.0027%45
037%
136%
218%
36%
42%
5+0%
Nobel MendyDEF1.1476provisional1.0026%26
038%
136%
218%
36%
42%
5+0%
Darko GyabiMIDHull City1.2065provisional0.9425%63
041%
134%
217%
36%
42%
5+0%
Matheus CunhaFWDManchester United1.0280provisional0.9324%66
040%
136%
217%
35%
41%
5+0%
Babajide David AkintolaFWDHull City1.8934provisional0.9023%20
045%
132%
214%
36%
42%
51%
6+0%
Charlie HughesDEFHull City0.9286provisional0.8822%68
042%
136%
216%
35%
41%
5+0%

10 of 54 players shown, ranked by projection. Show all 54 →

Shots54 playersHighest: Matheus Cunha, 3.12 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
Matheus CunhaFWDManchester United3.4680provisional3.1278%66
07%
115%
220%
320%
416%
510%
6+12%
Benjamin SeskoFWDManchester United3.2470provisional2.6269%63
011%
120%
222%
319%
413%
58%
6+8%
Bruno FernandesMIDManchester United2.6886provisional2.5770%71
09%
120%
224%
320%
413%
57%
6+6%
Marcus RashfordFWDManchester United3.3663provisional2.5167%57
012%
121%
222%
318%
412%
57%
6+7%
Bryan MbeumoFWDManchester United2.6085provisional2.4969%71
010%
121%
224%
320%
413%
57%
6+5%
Amad DialloFWDManchester United2.3277provisional2.0458%58
016%
126%
224%
317%
49%
54%
6+3%
Mohamed BelloumiFWDHull City2.9053provisional1.8651%35
022%
127%
222%
314%
48%
54%
6+3%
Oliver McBurnieFWDHull City2.0574provisional1.7350%74
022%
129%
223%
314%
47%
53%
6+2%
Liam MillarFWDHull City2.0563provisional1.5444%47
025%
131%
223%
312%
46%
52%
6+1%
CasemiroMIDManchester United1.7973provisional1.5244%58
024%
132%
223%
312%
45%
52%
6+1%

10 of 54 players shown, ranked by projection. Show all 54 →

Shots on target51 playersHighest: Benjamin Sesko, 1.32 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
Benjamin SeskoFWDManchester United1.6470provisional1.3270%63
030%
133%
221%
310%
44%
51%
6+1%
Matheus CunhaFWDManchester United1.3880provisional1.2569%66
031%
134%
221%
39%
43%
51%
6+0%
Marcus RashfordFWDManchester United1.4363provisional1.0763%57
037%
134%
218%
37%
42%
51%
6+0%
Bryan MbeumoFWDManchester United1.0985provisional1.0564%71
036%
135%
219%
37%
42%
50%
6+0%
Oliver McBurnieFWDHull City1.0274provisional0.8655%74
045%
134%
215%
35%
41%
5+0%
Amad DialloFWDManchester United0.9277provisional0.8154%58
046%
134%
214%
34%
41%
5+0%
Bruno FernandesMIDManchester United0.8186provisional0.7853%71
047%
135%
214%
34%
41%
5+0%
Mohamed BelloumiFWDHull City1.1953provisional0.7650%35
050%
132%
213%
34%
41%
5+0%
Joe GelhardtFWDHull City0.9050provisional0.5539%64
061%
128%
29%
32%
41%
5+0%
CasemiroMIDManchester United0.5973provisional0.5038%58
062%
129%
28%
31%
4+0%

10 of 51 players shown, ranked by projection. Show all 51 →

Fouls won54 playersHighest: Patrick Dorgu, 1.62 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
Patrick DorguDEFManchester United2.2463provisional1.6245%59
027%
128%
221%
313%
47%
53%
6+2%
Matheus CunhaFWDManchester United1.7380provisional1.5545%66
023%
132%
224%
313%
46%
52%
6+1%
Youri TielemansMID1.5969provisional1.2635%61
033%
132%
220%
310%
44%
51%
6+0%
Harry AmassDEFManchester United1.4570provisional1.1532%27
036%
132%
219%
39%
43%
51%
6+0%
Darko GyabiMIDHull City1.5065provisional1.1331%63
036%
132%
219%
38%
43%
51%
6+0%
Amad DialloFWDManchester United1.2277provisional1.0629%58
037%
135%
219%
37%
42%
51%
6+0%
Lewie CoyleDEFHull City1.0384provisional0.9725%86
039%
136%
217%
36%
42%
5+0%
CasemiroMIDManchester United1.1573provisional0.9725%58
040%
135%
217%
36%
42%
5+0%
Andrey SantosMID1.3558provisional0.9224%27
043%
133%
216%
36%
42%
50%
6+0%
Oliver McBurnieFWDHull City1.0574provisional0.8923%74
043%
134%
216%
35%
41%
5+0%
Kobbie MainooMIDManchester United1.1170provisional0.8823%53
044%
134%
215%
35%
41%
5+0%
Liam MillarFWDHull City1.1963provisional0.8822%47
043%
134%
215%
35%
41%
5+0%
Bryan MbeumoFWDManchester United0.9185provisional0.8722%71
043%
135%
216%
35%
41%
5+0%
Bruno FernandesMIDManchester United0.9086provisional0.8621%71
043%
135%
215%
35%
41%
5+0%
Manuel UgarteMIDManchester United1.3951provisional0.8421%51
048%
131%
214%
35%
42%
50%
6+0%
Joe GelhardtFWDHull City1.3950provisional0.8321%64
049%
131%
214%
35%
42%
50%
6+0%
Marcus RashfordFWDManchester United1.1263provisional0.8321%57
046%
134%
214%
35%
41%
5+0%
Amir HadziahmetovicMIDHull City1.1164provisional0.8320%37
046%
134%
215%
35%
41%
5+0%
Noussair MazraouiDEFManchester United0.9672provisional0.7919%57
047%
134%
214%
34%
41%
5+0%
Abdülkadir ÖmürMIDHull City1.2652provisional0.7819%20
049%
132%
213%
34%
41%
5+0%
Joel NdalaFWDHull City1.6135provisional0.7117%25
054%
130%
211%
34%
41%
50%
6+0%
Semi AjayiDEFHull City0.7781provisional0.7016%39
051%
133%
212%
33%
41%
5+0%
Regan SlaterMIDHull City0.7776provisional0.6715%89
053%
133%
211%
33%
41%
5+0%
Charlie HughesDEFHull City0.6986provisional0.6614%68
052%
133%
211%
33%
4+1%
Luke ShawDEFManchester United0.7080provisional0.6414%45
054%
133%
211%
32%
4+1%
Harry MaguireDEFManchester United0.7574provisional0.6314%50
055%
132%
211%
33%
4+1%
Ayden HeavenDEFManchester United0.8663provisional0.6214%21
056%
130%
211%
33%
41%
5+0%
John EganDEFHull City0.6978provisional0.6113%63
056%
131%
210%
32%
4+1%
Kasey PalmerMIDHull City1.5630provisional0.6013%38
058%
129%
29%
33%
41%
5+0%
Matt TargettDEF0.6180provisional0.5511%47
058%
131%
29%
32%
4+0%
Cody DramehDEFHull City0.8057provisional0.5411%51
060%
128%
29%
32%
4+0%
Matt CrooksMIDHull City0.6374provisional0.5311%52
060%
130%
28%
32%
4+0%
Mason MountMIDManchester United0.9943provisional0.5211%40
062%
127%
28%
32%
4+0%
Nobel MendyDEF0.5876provisional0.509%26
061%
129%
28%
31%
4+0%
Patrick James Coleman McNairDEFHull City0.9940provisional0.4910%16
063%
127%
27%
32%
4+0%
Babajide David AkintolaFWDHull City1.1434provisional0.4910%20
064%
126%
27%
32%
40%
5+0%
Mohamed BelloumiFWDHull City0.7453provisional0.479%35
064%
127%
27%
31%
4+0%
Benjamin SeskoFWDManchester United0.5770provisional0.458%63
064%
128%
27%
31%
4+0%
Diogo DalotDEFManchester United0.4883provisional0.448%67
065%
128%
26%
31%
4+0%
Matty JacobDEFHull City0.7048provisional0.417%13
068%
125%
26%
31%
4+0%
Matthijs de LigtDEFManchester United0.4380provisional0.396%42
068%
126%
25%
3+1%
Toby CollyerMIDHull City0.9233provisional0.387%23
070%
123%
25%
31%
4+0%
Mason BurstowFWDHull City1.0527provisional0.376%31
071%
123%
25%
31%
4+0%
Lewis KoumasFWDHull City0.7636provisional0.345%84
073%
122%
25%
31%
4+0%
Joshua ZirkzeeFWDManchester United0.9029provisional0.346%56
073%
121%
24%
31%
4+0%
Kieran DowellMIDHull City1.1119provisional0.304%14
075%
121%
24%
31%
4+0%
Lisandro MartínezDEFManchester United0.2881provisional0.263%38
078%
119%
23%
3+0%
Karl DarlowGK0.2288provisional0.222%29
080%
117%
22%
3+0%
Enis DestanFWDHull City0.6821provisional0.202%18
083%
115%
22%
3+0%
John LundstramMIDHull City0.4237provisional0.202%32
083%
115%
22%
3+0%
Senne LammensGKManchester United0.1988provisional0.192%32
083%
116%
22%
3+0%
Ryan GilesDEFHull City0.2169provisional0.171%63
085%
114%
2+1%
Leny YoroDEFManchester United0.3241provisional0.161%53
086%
113%
21%
3+0%
Ivor PandurGKHull City0.1289provisional0.121%92
089%
110%
2+1%

All 54 players shown, ranked by projection. Show fewer ↥

Saves3 playersHighest: Karl Darlow, 3.09 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
Karl DarlowGK3.1488provisional3.0957%29
06%
116%
221%
320%
416%
510%
6+11%
Senne LammensGKManchester United3.0688provisional3.0156%32
07%
116%
221%
320%
415%
510%
6+11%
Ivor PandurGKHull City2.9789provisional2.9655%92
07%
117%
222%
320%
415%
59%
6+10%

2 of 7 markets have no projection for this fixture.