Premier League · England

Hull CityvManchester United

Saturday 22 August at 12:30MKM 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

Angles

every priced line builder

formMohamed Belloumi over 0.5 shots on target: 8/10 of his last 10, 3 in a row
Last 20: 12/20 · 4/5 at home · strip 1 1 1 0 1 0 1 1 1 1 · market 51% · best 2.20 (BetMGM) · bet365 2.10
formYouri Tielemans over 1.5 tackles: 9/10 of his last 10, 8 in a row
Last 20: 18/20 · 4/5 away · strip 2 2 2 2 4 3 3 2 1 2 · market 74% · best 1.55 (SportingIndex) · bet365 1.53
formLuke Shaw over 0.5 fouls won: 9/10 of his last 10
Last 20: 13/20 · 4/5 away · strip 0 1 2 1 1 3 1 1 1 1 · market 61% · best 1.91 (Bet365) · bet365 1.91
formMatheus Cunha over 0.5 shots on target: 9/10 of his last 10, 9 in a row
Last 20: 15/20 · 4/5 away · strip 1 1 1 1 2 1 1 1 2 0 · market 80% · best 1.33 (Bet365) · bet365 1.33
formOliver McBurnie over 0.5 shots on target: 8/10 of his last 10
Last 20: 13/20 · 4/5 at home · strip 1 2 0 5 1 1 1 2 1 0 · market 58% · best 1.90 (32Red) · bet365 1.83
formMatheus Cunha over 0.5 fouls: 8/10 of his last 10, 6 in a row
Last 20: 10/20 · 4/5 away · strip 1 2 3 2 2 1 0 1 3 0 · market 75% · best 1.53 (PaddyPower) · bet365 1.36
formMatheus Cunha over 0.5 tackles: 9/10 of his last 10, 3 in a row
Last 20: 17/20 · 4/5 away · strip 1 2 1 0 2 1 1 1 3 1 · market 79% · best 1.33 (Bet365) · bet365 1.33
formMason Mount over 0.5 tackles: 8/10 of his last 10, 5 in a row
Last 20: 12/20 · 4/6 away · strip 4 1 3 1 1 0 1 0 1 3 · market 78% · best 1.33 (Bet365) · bet365 1.33

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.

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.
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.
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.
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.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Matt CrooksMID	Hull City	1.54	74provisional	1.32	38%	52	029% 133% 222% 310% 44% 51% 6+0%
CasemiroMID	Manchester United	1.25	73provisional	1.07	29%	58	035% 136% 219% 37% 42% 50% 6+0%
Oliver McBurnieFWD	Hull City	1.18	74provisional	1.01	27%	74	038% 135% 218% 37% 42% 50% 6+0%
Manuel UgarteMID	Manchester United	1.57	51provisional	1.01	27%	51	041% 132% 217% 37% 42% 51% 6+0%
Luke ShawDEF	Manchester United	1.09	80provisional	1.00	27%	45	037% 136% 218% 36% 42% 5+0%
Nobel MendyDEF	—	1.14	76provisional	1.00	26%	26	038% 136% 218% 36% 42% 5+0%
Darko GyabiMID	Hull City	1.20	65provisional	0.94	25%	63	041% 134% 217% 36% 42% 5+0%
Matheus CunhaFWD	Manchester United	1.02	80provisional	0.93	24%	66	040% 136% 217% 35% 41% 5+0%
Babajide David AkintolaFWD	Hull City	1.89	34provisional	0.90	23%	20	045% 132% 214% 36% 42% 51% 6+0%
Charlie HughesDEF	Hull City	0.92	86provisional	0.88	22%	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.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Matheus CunhaFWD	Manchester United	3.46	80provisional	3.12	78%	66	07% 115% 220% 320% 416% 510% 6+12%
Benjamin SeskoFWD	Manchester United	3.24	70provisional	2.62	69%	63	011% 120% 222% 319% 413% 58% 6+8%
Bruno FernandesMID	Manchester United	2.68	86provisional	2.57	70%	71	09% 120% 224% 320% 413% 57% 6+6%
Marcus RashfordFWD	Manchester United	3.36	63provisional	2.51	67%	57	012% 121% 222% 318% 412% 57% 6+7%
Bryan MbeumoFWD	Manchester United	2.60	85provisional	2.49	69%	71	010% 121% 224% 320% 413% 57% 6+5%
Amad DialloFWD	Manchester United	2.32	77provisional	2.04	58%	58	016% 126% 224% 317% 49% 54% 6+3%
Mohamed BelloumiFWD	Hull City	2.90	53provisional	1.86	51%	35	022% 127% 222% 314% 48% 54% 6+3%
Oliver McBurnieFWD	Hull City	2.05	74provisional	1.73	50%	74	022% 129% 223% 314% 47% 53% 6+2%
Liam MillarFWD	Hull City	2.05	63provisional	1.54	44%	47	025% 131% 223% 312% 46% 52% 6+1%
CasemiroMID	Manchester United	1.79	73provisional	1.52	44%	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.

Player	Team	Model /90	Exp. mins	Projected	Over 0.5	n	Distribution
Benjamin SeskoFWD	Manchester United	1.64	70provisional	1.32	70%	63	030% 133% 221% 310% 44% 51% 6+1%
Matheus CunhaFWD	Manchester United	1.38	80provisional	1.25	69%	66	031% 134% 221% 39% 43% 51% 6+0%
Marcus RashfordFWD	Manchester United	1.43	63provisional	1.07	63%	57	037% 134% 218% 37% 42% 51% 6+0%
Bryan MbeumoFWD	Manchester United	1.09	85provisional	1.05	64%	71	036% 135% 219% 37% 42% 50% 6+0%
Oliver McBurnieFWD	Hull City	1.02	74provisional	0.86	55%	74	045% 134% 215% 35% 41% 5+0%
Amad DialloFWD	Manchester United	0.92	77provisional	0.81	54%	58	046% 134% 214% 34% 41% 5+0%
Bruno FernandesMID	Manchester United	0.81	86provisional	0.78	53%	71	047% 135% 214% 34% 41% 5+0%
Mohamed BelloumiFWD	Hull City	1.19	53provisional	0.76	50%	35	050% 132% 213% 34% 41% 5+0%
Joe GelhardtFWD	Hull City	0.90	50provisional	0.55	39%	64	061% 128% 29% 32% 41% 5+0%
CasemiroMID	Manchester United	0.59	73provisional	0.50	38%	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.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Patrick DorguDEF	Manchester United	2.24	63provisional	1.62	45%	59	027% 128% 221% 313% 47% 53% 6+2%
Matheus CunhaFWD	Manchester United	1.73	80provisional	1.55	45%	66	023% 132% 224% 313% 46% 52% 6+1%
Youri TielemansMID	—	1.59	69provisional	1.26	35%	61	033% 132% 220% 310% 44% 51% 6+0%
Harry AmassDEF	Manchester United	1.45	70provisional	1.15	32%	27	036% 132% 219% 39% 43% 51% 6+0%
Darko GyabiMID	Hull City	1.50	65provisional	1.13	31%	63	036% 132% 219% 38% 43% 51% 6+0%
Amad DialloFWD	Manchester United	1.22	77provisional	1.06	29%	58	037% 135% 219% 37% 42% 51% 6+0%
Lewie CoyleDEF	Hull City	1.03	84provisional	0.97	25%	86	039% 136% 217% 36% 42% 5+0%
CasemiroMID	Manchester United	1.15	73provisional	0.97	25%	58	040% 135% 217% 36% 42% 5+0%
Andrey SantosMID	—	1.35	58provisional	0.92	24%	27	043% 133% 216% 36% 42% 50% 6+0%
Oliver McBurnieFWD	Hull City	1.05	74provisional	0.89	23%	74	043% 134% 216% 35% 41% 5+0%

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

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.

Player	Team	Model /90	Exp. mins	Projected	Over 2.5	n	Distribution
Karl DarlowGK	—	3.14	88provisional	3.09	57%	29	06% 116% 221% 320% 416% 510% 6+11%
Senne LammensGK	Manchester United	3.06	88provisional	3.01	56%	32	07% 116% 221% 320% 415% 510% 6+11%
Ivor PandurGK	Hull City	2.97	89provisional	2.96	55%	92	07% 117% 222% 320% 415% 59% 6+10%

2 of 7 markets have no projection for this fixture.