Premier League · England

Brighton & Hove AlbionvAston Villa

Sunday 23 August at 14:00The American Express Community Stadiumlineups not announced

Referee

Peter Bankes

48 matches on record

Fouls per game

20.8

-10% vs league

League average

23.2

fouls per match, both teams

Angles

every priced line builder

formOllie Watkins over 0.5 fouls: 8/10 of his last 10
Last 20: 15/20 · 4/5 away · strip 0 1 1 1 1 2 1 0 1 2 · market 62% · best 1.73 (Bet365) · bet365 1.73
formYankuba Minteh over 0.5 fouls: 8/10 of his last 10, 5 in a row
Last 20: 10/20 · 4/5 at home · strip 1 1 1 1 2 0 4 2 2 0 · market 77% · best 1.36 (Bet365) · bet365 1.36
opponentBrighton & Hove Albion concede 9.8 shots a game — 18th most of 20 in the Premier League
Over their last 10. A hard place to get shots.
opponentBrighton & Hove Albion concede 12.5 tackles a game — 18th most of 20 in the Premier League
Over their last 10. A hard place to get tackles.

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 — 2 live calls

Matty Cashover 2.50 Tackles2.75 at bet365 · fair 2.40+14.6%
Diego Gómezover 2.50 Fouls3.00 at bet365 · fair 2.70+11.1%

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

Fouls55 playersHighest: João Gomes, 1.57 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
João GomesMID	—	1.67	83provisional	1.57	46%	71	022% 132% 225% 313% 45% 52% 6+1%
Kaoru MitomaFWD	Brighton & Hove Albion	1.50	75provisional	1.31	37%	61	029% 134% 222% 310% 44% 51% 6+0%
Diego GómezMID	Brighton & Hove Albion	1.86	53provisional	1.23	34%	48	033% 133% 220% 39% 44% 51% 6+0%
Jack HinshelwoodMID	Brighton & Hove Albion	1.37	72provisional	1.16	32%	53	033% 135% 220% 38% 43% 51% 6+0%
Diego CoppolaDEF	Brighton & Hove Albion	1.19	83provisional	1.11	31%	39	034% 135% 220% 38% 42% 51% 6+0%
Mats WiefferDEF	Brighton & Hove Albion	1.36	66provisional	1.07	29%	51	037% 134% 219% 37% 42% 51% 6+0%
Luka VuskovicDEF	—	1.07	87provisional	1.04	28%	28	036% 136% 219% 37% 42% 5+1%
James MilnerMID	Brighton & Hove Albion	1.52	54provisional	1.03	28%	24	039% 133% 217% 37% 42% 51% 6+0%
Carlos BalebaMID	Brighton & Hove Albion	1.40	59provisional	1.01	27%	65	039% 133% 217% 37% 42% 51% 6+0%
Emiliano BuendíaFWD	Aston Villa	1.57	50provisional	1.00	27%	59	041% 133% 217% 37% 42% 51% 6+0%

10 of 55 players shown, ranked by projection. Show all 55 →

Shots55 playersHighest: Ibrahim Osman, 2.09 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
Ibrahim OsmanFWD	—	2.77	64provisional	2.09	59%	17	016% 125% 224% 317% 410% 55% 6+3%
Donyell MalenFWD	Aston Villa	2.88	58provisional	1.99	54%	67	021% 125% 221% 315% 49% 55% 6+4%
Ollie WatkinsFWD	Aston Villa	2.14	76provisional	1.85	53%	75	018% 128% 225% 316% 48% 53% 6+2%
Evan FergusonFWD	Brighton & Hove Albion	3.16	46provisional	1.81	49%	37	023% 127% 221% 314% 48% 54% 6+3%
Kaoru MitomaFWD	Brighton & Hove Albion	2.06	75provisional	1.77	51%	61	020% 129% 224% 315% 47% 53% 6+2%
Johan ManzambiMID	—	1.76	70provisional	1.42	41%	38	028% 132% 222% 311% 45% 52% 6+1%
Jack HinshelwoodMID	Brighton & Hove Albion	1.65	72provisional	1.38	39%	53	028% 133% 222% 311% 44% 51% 6+1%
Georginio RutterFWD	Brighton & Hove Albion	2.42	43provisional	1.30	35%	61	034% 131% 218% 39% 44% 52% 6+1%
Yankuba MintehFWD	Brighton & Hove Albion	1.67	59provisional	1.17	32%	66	036% 132% 219% 39% 43% 51% 6+0%
Alejandro GarnachoFWD	—	2.05	45provisional	1.15	31%	60	037% 133% 218% 38% 43% 51% 6+1%

10 of 55 players shown, ranked by projection. Show all 55 →

Shots on target52 playersHighest: Ollie Watkins, 0.86 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
Ollie WatkinsFWD	Aston Villa	0.99	76provisional	0.86	56%	75	044% 135% 215% 35% 41% 5+0%
Donyell MalenFWD	Aston Villa	1.23	58provisional	0.85	53%	67	047% 132% 214% 35% 42% 50% 6+0%
Ibrahim OsmanFWD	—	1.03	64provisional	0.78	52%	17	048% 134% 214% 34% 41% 5+0%
Kaoru MitomaFWD	Brighton & Hove Albion	0.88	75provisional	0.75	51%	61	049% 133% 213% 34% 41% 5+0%
Evan FergusonFWD	Brighton & Hove Albion	1.28	46provisional	0.73	49%	37	051% 131% 212% 34% 41% 5+0%
Jack HinshelwoodMID	Brighton & Hove Albion	0.63	72provisional	0.53	40%	53	060% 130% 28% 32% 4+0%
Johan ManzambiMID	—	0.61	70provisional	0.49	38%	38	062% 129% 28% 31% 4+0%
Yankuba MintehFWD	Brighton & Hove Albion	0.62	59provisional	0.43	34%	66	066% 126% 26% 31% 4+0%
Alejandro GarnachoFWD	—	0.76	45provisional	0.43	33%	60	067% 126% 26% 31% 4+0%
Georginio RutterFWD	Brighton & Hove Albion	0.79	43provisional	0.42	33%	61	067% 125% 26% 31% 4+0%

10 of 52 players shown, ranked by projection. Show all 52 →

Fouls won55 playersHighest: Johan Manzambi, 2.09 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
Johan ManzambiMID	—	2.61	70provisional	2.09	59%	38	017% 125% 224% 317% 410% 55% 6+3%
Boubacar KamaraMID	Aston Villa	1.62	75provisional	1.39	40%	44	028% 132% 222% 311% 44% 51% 6+1%
João GomesMID	—	1.48	83provisional	1.38	40%	71	027% 134% 223% 311% 44% 51% 6+0%
John McGinnMID	Aston Villa	1.90	61provisional	1.35	38%	64	031% 131% 220% 311% 45% 52% 6+1%
Emiliano BuendíaFWD	Aston Villa	2.15	50provisional	1.28	36%	59	034% 130% 219% 310% 44% 52% 6+1%
Ibrahim OsmanFWD	—	1.70	64provisional	1.27	36%	17	030% 134% 221% 310% 44% 51% 6+0%
Malick YalcouyéMID	—	2.74	37provisional	1.26	34%	33	036% 131% 217% 39% 44% 52% 6+1%
Ezri KonsaDEF	Aston Villa	1.15	87provisional	1.11	31%	68	034% 136% 220% 38% 42% 51% 6+0%
Carlos BalebaMID	Brighton & Hove Albion	1.30	59provisional	0.90	23%	65	044% 132% 215% 36% 42% 50% 6+0%
Kaoru MitomaFWD	Brighton & Hove Albion	1.05	75provisional	0.90	23%	61	043% 134% 216% 35% 41% 5+0%

10 of 55 players shown, ranked by projection. Show all 55 →

Saves3 playersHighest: Zion Suzuki, 3.53 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
Zion SuzukiGK	—	3.57	89provisional	3.53	65%	57	04% 112% 218% 320% 417% 512% 6+17%
Emiliano MartínezGK	Aston Villa	3.53	87provisional	3.43	64%	69	05% 113% 219% 320% 416% 512% 6+16%
Bart VerbruggenGK	Brighton & Hove Albion	2.60	89provisional	2.59	46%	74	09% 120% 224% 320% 413% 57% 6+6%

2 of 7 markets have no projection for this fixture.