Championship · England

Stoke CityvSwansea City

Saturday 15 August at 15:00Bet365 Stadiumlineups not announced

Referee

Oliver Langford

51 matches on record

Fouls per game

21.5

-5% vs league

League average

22.6

fouls per match, both teams

Value — 0 live calls · 2 withdrawn

Sorba Thomasover 1.50 Shots on target6.50 at Bet365 · fair 5.19+25.2%withdrawn
Sorba Thomasover 3.50 Shots7.00 at Bet365 · fair 5.89+18.8%withdrawn

A withdrawn call is one Per90 no longer stands behind — the rule that published it was later found wrong. It stays here, keeps its price, and is still scored against its closing line; hover it for the reason. 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 v20260813-1451 · Tackles v20260813-1451 · Shots v20260813-1451 · Fouls won v20260813-1451 · Cards v20260813-1451 · Saves v20260813-1451

Model projections

Fouls41 playersHighest: Róbert Boženík, 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.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Róbert BoženíkFWD	Stoke City	3.20	40provisional	1.42	37%	12	035% 128% 216% 310% 46% 53% 6+2%
Gonçalo Baptista FrancoMID	Swansea City	1.67	76provisional	1.40	40%	81	026% 133% 223% 311% 44% 51% 6+0%
Marko StamenicMID	Swansea City	1.76	69provisional	1.36	39%	32	029% 132% 222% 311% 44% 51% 6+1%
Ben PearsonMID	Stoke City	1.90	56provisional	1.17	33%	31	037% 130% 218% 39% 44% 51% 6+0%
Lewis BakerMID	Stoke City	1.38	76provisional	1.16	33%	31	034% 133% 220% 39% 43% 51% 6+0%
Sam GallagherFWD	Stoke City	2.31	41provisional	1.04	27%	26	040% 133% 216% 37% 43% 51% 6+0%
J. TchamadeuDEF	Stoke City	1.19	77provisional	1.02	27%	60	038% 134% 218% 37% 42% 50% 6+0%
Tatsuki SekoMID	Stoke City	1.47	61provisional	1.00	27%	49	043% 130% 216% 37% 43% 51% 6+0%
Million ManhoefFWD	Stoke City	1.26	68provisional	0.95	25%	69	041% 134% 217% 36% 42% 5+1%
Zeidane InoussaFWD	Swansea City	1.96	43provisional	0.95	25%	15	043% 132% 216% 36% 42% 51% 6+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Tackles41 playersHighest: Gonçalo Baptista Franco, 2.81 expectedshow

What drives it — Possession share. A tackle count depends far more on how much of the match is played at your own end than on a player’s own form. See what has actually happened.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Gonçalo Baptista FrancoMID	Swansea City	3.35	76provisional	2.81	71%	81	010% 119% 221% 318% 413% 59% 6+10%
Maksym TalovierovDEF	Stoke City	2.41	73provisional	1.96	54%	14	021% 126% 222% 315% 49% 55% 6+4%
Benjamin CabangoDEF	Swansea City	1.73	90provisional	1.72	49%	90	021% 130% 224% 314% 47% 53% 6+2%
Ronald Pereira MartinsFWD	Swansea City	1.89	76provisional	1.59	45%	87	025% 130% 222% 313% 46% 53% 6+1%
J. TchamadeuDEF	Stoke City	1.75	77provisional	1.51	43%	60	027% 130% 222% 312% 46% 52% 6+1%
Joon-Ho BaeMID	Stoke City	1.90	71provisional	1.50	42%	78	028% 130% 221% 312% 46% 52% 6+1%
Marko StamenicMID	Swansea City	1.86	69provisional	1.43	40%	32	029% 131% 221% 311% 45% 52% 6+1%
Tatsuki SekoMID	Stoke City	2.05	61provisional	1.39	38%	49	035% 128% 218% 310% 45% 52% 6+2%
Eric Junior BocatDEF	Stoke City	1.81	65provisional	1.32	37%	45	035% 128% 219% 310% 45% 52% 6+1%
Ben PearsonMID	Stoke City	2.12	56provisional	1.31	36%	31	036% 128% 218% 310% 45% 52% 6+1%

10 of 41 players shown, ranked by projection. Show all 41 →

Shots41 playersHighest: Million Manhoef, 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.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Million ManhoefFWD	Stoke City	2.82	68provisional	2.12	58%	69	017% 124% 222% 316% 410% 55% 6+4%
Žan VipotnikFWD	Swansea City	2.47	59provisional	1.61	45%	65	027% 128% 221% 313% 47% 53% 6+2%
Sorba ThomasFWD	Stoke City	1.63	87provisional	1.58	46%	45	022% 132% 224% 313% 46% 52% 6+1%
Róbert BoženíkFWD	Stoke City	3.18	40provisional	1.41	36%	12	036% 128% 216% 310% 46% 53% 6+2%
Ji-Sung EomFWD	Swansea City	2.08	59provisional	1.36	38%	65	031% 131% 220% 311% 45% 52% 6+1%
Liam CullenMID	Swansea City	1.97	58provisional	1.27	35%	61	035% 130% 219% 310% 44% 52% 6+1%
Lewis BakerMID	Stoke City	1.47	76provisional	1.24	35%	31	033% 132% 220% 39% 44% 51% 6+0%
Joon-Ho BaeMID	Stoke City	1.55	71provisional	1.23	34%	78	033% 133% 220% 39% 44% 51% 6+0%
Lamine CisseFWD	Stoke City	2.22	50provisional	1.22	33%	24	038% 128% 217% 39% 44% 52% 6+1%
Ronald Pereira MartinsFWD	Swansea City	1.36	76provisional	1.15	32%	87	034% 134% 220% 38% 43% 51% 6+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Fouls won41 playersHighest: Ronald Pereira Martins, 2.13 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
Ronald Pereira MartinsFWD	Swansea City	2.53	76provisional	2.13	60%	87	015% 125% 224% 317% 410% 55% 6+4%
Gonçalo Baptista FrancoMID	Swansea City	1.66	76provisional	1.40	40%	81	027% 133% 222% 311% 44% 51% 6+1%
Ji-Sung EomFWD	Swansea City	1.84	59provisional	1.20	33%	65	035% 132% 219% 39% 44% 51% 6+0%
Lamine CisseFWD	Stoke City	2.09	50provisional	1.15	31%	24	040% 129% 217% 39% 44% 51% 6+1%
Joon-Ho BaeMID	Stoke City	1.43	71provisional	1.13	31%	78	036% 133% 219% 38% 43% 51% 6+0%
Zeidane InoussaFWD	Swansea City	2.13	43provisional	1.02	27%	15	041% 132% 216% 37% 43% 51% 6+0%
Oliver CooperMID	Swansea City	2.50	36provisional	1.01	26%	12	049% 126% 212% 37% 44% 52% 6+1%
Melker WidellMID	Swansea City	2.06	41provisional	0.93	24%	22	046% 130% 214% 36% 42% 51% 6+0%
J. TchamadeuDEF	Stoke City	1.02	77provisional	0.88	22%	60	044% 134% 215% 35% 41% 5+0%
Sorba ThomasFWD	Stoke City	0.90	87provisional	0.87	22%	45	043% 135% 216% 35% 41% 5+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Cards41 playersHighest: Ben Pearson, 0.23 expectedshow

Known defect — Prices ANY card — yellow, second yellow or straight red — matching how books settle "to be shown a card". The trends column counts the same thing.

What drives it — Fouls first, then how readily the referee reaches for a card. Two officials averaging four cards a match can do it for opposite reasons. See what has actually happened.

Player	Team	Model /90	Exp. mins	Projected	Over 0.5	n	Distribution
Ben PearsonMID	Stoke City	0.36	56provisional	0.23	20%	31	080% 117% 22% 3+0%
Marko StamenicMID	Swansea City	0.28	69provisional	0.22	20%	32	080% 117% 22% 3+0%
J. TchamadeuDEF	Stoke City	0.24	77provisional	0.21	19%	60	081% 117% 22% 3+0%
Maksym TalovierovDEF	Stoke City	0.23	73provisional	0.19	17%	14	083% 115% 22% 3+0%
Lewis BakerMID	Stoke City	0.19	76provisional	0.16	15%	31	085% 114% 2+1%
Gonçalo Baptista FrancoMID	Swansea City	0.17	76provisional	0.14	13%	81	087% 112% 2+1%
Mohammed Bosun LawalDEF	Stoke City	0.19	68provisional	0.14	13%	29	087% 112% 2+1%
Benjamin CabangoDEF	Swansea City	0.13	90provisional	0.13	12%	90	088% 111% 2+1%
Ben WilmotDEF	Stoke City	0.14	85provisional	0.13	12%	72	088% 111% 2+1%
Jay FultonMID	Swansea City	0.28	42provisional	0.13	12%	32	088% 111% 2+1%

10 of 41 players shown, ranked by projection. Show all 41 →

Saves3 playersHighest: Lawrence Vigouroux, 2.96 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
Lawrence VigourouxGK	Swansea City	2.96	90provisional	2.96	55%	91	07% 117% 222% 320% 415% 59% 6+10%
Viktor JohanssonGK	Stoke City	2.57	90provisional	2.57	46%	71	010% 121% 224% 320% 413% 57% 6+6%
Tommy SimkimGK	Stoke City	2.28	90provisional	2.28	39%	15	012% 124% 225% 319% 411% 56% 6+4%

1 of 7 markets have no projection for this fixture.