Championship · England

SouthamptonvStoke City

Saturday 22 August at 15:00St. Mary's Stadiumlineups not announced

Referee

Steve Martin

0 matches on record — under 15, so the model falls back toward the league mean

Fouls per game

—

League average

23.2

fouls per match, both teams

Angles

every priced line builder

formEric-Junior Bocat over 0.5 shots: 8/10 of his last 10
Last 20: 10/20 · 3/4 away · strip 2 3 0 1 0 1 1 3 3 1 · market 58% · best 1.95 (Coral) · bet365 1.91

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 — 6 live calls

Taylor Harwood-Bellisover 1.50 Shots3.50 at bet365 · fair 3.10+12.9%
Eric-Junior Bocatover 0.50 Shots1.91 at bet365 · fair 1.73+10.3%
Finn Azazover 2.50 Shots1.80 at bet365 · fair 1.65+9.1%
Léo Scienzaover 3.50 Shots2.20 at bet365 · fair 2.05+7.3%
Jun-ho Baeover 2.50 Shots4.00 at bet365 · fair 3.75+6.7%
Ryan Manningover 0.50 Shots1.18 at bet365 · fair 1.11+6.5%

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

Fouls59 playersHighest: Róbert Boženík, 1.61 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	2.86	43provisional	1.61	44%	23	026% 130% 221% 312% 46% 53% 6+2%
Svante IngelssonMID	Stoke City	1.70	72provisional	1.44	42%	76	026% 133% 223% 312% 45% 52% 6+1%
Sam GallagherFWD	Stoke City	1.92	44provisional	1.14	31%	42	035% 134% 219% 38% 43% 51% 6+0%
Ben PearsonMID	Stoke City	1.47	64provisional	1.13	31%	44	035% 134% 220% 38% 43% 51% 6+0%
Caspar JanderMID	Southampton	1.19	80provisional	1.08	30%	38	035% 136% 219% 37% 42% 51% 6+0%
Lewis BakerMID	Stoke City	1.26	74provisional	1.08	29%	49	036% 135% 219% 37% 42% 51% 6+0%
Million ManhoefFWD	Stoke City	1.24	73provisional	1.05	29%	80	036% 135% 219% 37% 42% 50% 6+0%
Flynn DownesMID	Southampton	1.33	66provisional	1.04	28%	66	038% 134% 218% 37% 42% 51% 6+0%
Lamine CisséFWD	Stoke City	1.38	60provisional	1.01	27%	34	039% 134% 218% 37% 42% 51% 6+0%
Ethan GalbraithMID	Stoke City	1.02	81provisional	0.94	24%	40	040% 136% 217% 36% 41% 5+0%

10 of 59 players shown, ranked by projection. Show all 59 →

Shots59 playersHighest: Finn Azaz, 2.28 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
Finn AzazMID	Southampton	2.98	66provisional	2.28	61%	91	016% 123% 222% 317% 411% 56% 6+6%
Léo ScienzaMID	Southampton	2.89	65provisional	2.22	62%	67	014% 124% 224% 318% 411% 56% 6+4%
Lewis DobbinFWD	Southampton	2.65	62provisional	1.95	55%	67	019% 126% 223% 316% 49% 54% 6+3%
Cyle LarinFWD	Southampton	2.75	52provisional	1.74	48%	51	024% 128% 221% 313% 47% 54% 6+2%
Cameron ArcherFWD	Southampton	2.59	51provisional	1.61	45%	65	026% 129% 221% 313% 47% 53% 6+2%
Million ManhoefFWD	Stoke City	1.91	73provisional	1.59	46%	80	023% 131% 223% 313% 46% 52% 6+1%
Ross StewartFWD	Southampton	2.82	41provisional	1.47	40%	39	029% 131% 220% 311% 45% 52% 6+2%
Ben Brereton DíazFWD	Southampton	2.01	59provisional	1.41	40%	69	030% 131% 221% 311% 45% 52% 6+1%
Kuryu MatsukiMID	Southampton	1.72	62provisional	1.26	35%	23	033% 132% 220% 310% 44% 51% 6+1%
Divin MubamaFWD	Southampton	2.22	43provisional	1.20	32%	28	038% 130% 217% 39% 44% 52% 6+1%

10 of 59 players shown, ranked by projection. Show all 59 →

Shots on target59 playersHighest: Léo Scienza, 0.88 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
Léo ScienzaMID	Southampton	1.14	65provisional	0.88	56%	67	044% 134% 215% 35% 41% 5+0%
Cyle LarinFWD	Southampton	1.29	52provisional	0.82	52%	51	048% 132% 214% 35% 41% 5+0%
Finn AzazMID	Southampton	0.96	66provisional	0.73	49%	91	051% 132% 213% 34% 41% 5+0%
Ross StewartFWD	Southampton	1.24	41provisional	0.65	45%	39	055% 130% 211% 33% 41% 5+0%
Cameron ArcherFWD	Southampton	1.01	51provisional	0.63	44%	65	056% 130% 210% 33% 41% 5+0%
Lewis DobbinFWD	Southampton	0.84	62provisional	0.62	45%	67	055% 131% 210% 33% 41% 5+0%
Million ManhoefFWD	Stoke City	0.71	73provisional	0.59	44%	80	056% 131% 210% 32% 4+0%
Divin MubamaFWD	Southampton	0.87	43provisional	0.47	35%	28	065% 126% 27% 32% 4+0%
Sorba ThomasFWD	Stoke City	0.48	86provisional	0.46	37%	46	063% 129% 27% 31% 4+0%
Ben Brereton DíazFWD	Southampton	0.66	59provisional	0.46	36%	69	064% 127% 27% 31% 4+0%
Lamine CisséFWD	Stoke City	0.62	60provisional	0.45	35%	34	065% 126% 27% 31% 4+0%
Róbert BoženíkFWD	Stoke City	0.77	43provisional	0.41	31%	23	069% 124% 26% 31% 4+0%
Kuryu MatsukiMID	Southampton	0.53	62provisional	0.39	31%	23	069% 125% 25% 31% 4+0%
Tom FellowsMID	Southampton	0.44	67provisional	0.34	29%	86	071% 124% 24% 3+1%
Jamie Paul DonleyMID	Stoke City	0.46	63provisional	0.34	28%	17	072% 123% 24% 3+1%
Ethan GalbraithMID	Stoke City	0.37	81provisional	0.34	29%	40	071% 124% 24% 3+1%
Taylor Harwood-BellisDEF	Southampton	0.35	87provisional	0.34	29%	78	071% 124% 24% 3+1%
Lewis BakerMID	Stoke City	0.39	74provisional	0.33	28%	49	072% 123% 24% 3+1%
Ryan ManningDEF	Southampton	0.35	76provisional	0.31	26%	70	074% 122% 24% 3+0%
Sam GallagherFWD	Stoke City	0.54	44provisional	0.30	25%	42	075% 121% 24% 3+1%
Jun-ho BaeMID	Stoke City	0.39	64provisional	0.29	25%	88	075% 121% 23% 3+0%
Milan SmitFWD	Stoke City	0.63	33provisional	0.27	22%	13	078% 119% 23% 3+1%
Yukinari SugawaraDEF	Southampton	0.32	70provisional	0.26	23%	63	077% 120% 23% 3+0%
Tomas RigoMID	Stoke City	0.35	63provisional	0.26	23%	32	077% 119% 23% 3+0%
Ryan FraserFWD	Southampton	0.42	49provisional	0.25	22%	24	078% 118% 23% 3+0%
Samuel EdozieFWD	Southampton	0.75	23provisional	0.25	21%	17	079% 118% 23% 3+0%
Damion DownsFWD	Southampton	0.61	30provisional	0.24	21%	23	079% 118% 23% 3+0%
James BreeDEF	Southampton	0.26	79provisional	0.23	20%	59	080% 118% 22% 3+0%
Emre TezgelFWD	Stoke City	0.67	24provisional	0.23	20%	12	080% 117% 22% 3+0%
Caspar JanderMID	Southampton	0.25	80provisional	0.22	20%	38	080% 117% 22% 3+0%
Eric-Junior BocatDEF	Stoke City	0.23	72provisional	0.19	17%	60	083% 115% 22% 3+0%
Svante IngelssonMID	Stoke City	0.21	72provisional	0.18	16%	76	084% 115% 21% 3+0%
Djibril SoumaréMID	Stoke City	0.31	44provisional	0.17	15%	26	085% 114% 21% 3+0%
Joe AriboMID	Southampton	0.33	39provisional	0.16	15%	45	085% 113% 21% 3+0%
Bosun LawalDEF	Stoke City	0.19	73provisional	0.16	15%	35	085% 113% 2+1%
Ben WilmotDEF	Stoke City	0.15	85provisional	0.15	13%	74	087% 112% 2+1%
Jack StephensDEF	Southampton	0.19	68provisional	0.15	13%	52	087% 112% 2+1%
Flynn DownesMID	Southampton	0.19	66provisional	0.14	13%	66	087% 112% 2+1%
Steven N'ZonziMID	Stoke City	0.23	46provisional	0.13	12%	30	088% 111% 2+1%
Tatsuki SekoMID	Stoke City	0.18	57provisional	0.12	11%	68	089% 110% 2+1%
Cameron BraggMID	Southampton	0.22	45provisional	0.12	11%	18	089% 110% 2+1%
Nathan WoodDEF	Southampton	0.13	77provisional	0.12	11%	48	089% 110% 2+1%
Joshua QuarshieDEF	Southampton	0.15	63provisional	0.11	11%	13	089% 110% 2+1%
Ben GibsonDEF	Stoke City	0.15	64provisional	0.11	11%	37	089% 110% 2+1%
Maksym TaloverovDEF	Stoke City	0.11	67provisional	0.09	8%	28	092% 18% 2+0%
Aaron CresswellDEF	Stoke City	0.12	60provisional	0.08	8%	43	092% 18% 2+0%
Junior TchamadeuDEF	Stoke City	0.11	68provisional	0.08	8%	67	092% 18% 2+0%
Ben PearsonMID	Stoke City	0.10	64provisional	0.08	7%	44	093% 17% 2+0%
Oriol RomeuMID	Southampton	0.14	35provisional	0.06	6%	31	094% 16% 2+0%
WelingtonDEF	Southampton	0.10	50provisional	0.06	6%	29	094% 15% 2+0%
Mads RoerslevDEF	Southampton	0.11	39provisional	0.06	5%	35	095% 15% 2+0%
Alex McCarthyGK	Southampton	0.00	86provisional	0.00	0%	12	0100% 1+0%
Tommy SimkinGK	Stoke City	0.00	87provisional	0.00	0%	15	0100% 1+0%
Gavin BazunuGK	Stoke City	0.00	88provisional	0.00	0%	25	0100% 1+0%
Daniel PeretzGK	Southampton	0.00	89provisional	0.00	0%	26	0100% 1+0%
Aaron RamsdaleGK	Southampton	0.00	88provisional	0.00	0%	42	0100% 1+0%
Viktor JohanssonGK	Stoke City	0.00	89provisional	0.00	0%	72	0100% 1+0%
Gavin BazunuGK	Stoke City	0.00	88provisional	0.00	0%	25	0100% 1+0%
Josh GriffithsGK	—	0.00	87provisional	0.00	0%	30	0100% 1+0%

All 59 players shown, ranked by projection. Show fewer ↥

Fouls won59 playersHighest: Léo Scienza, 2.26 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
Léo ScienzaMID	Southampton	2.98	65provisional	2.26	63%	67	014% 123% 224% 318% 411% 56% 6+4%
Lamine CisséFWD	Stoke City	1.74	60provisional	1.22	34%	34	034% 132% 220% 39% 44% 51% 6+0%
Kuryu MatsukiMID	Southampton	1.56	62provisional	1.13	31%	23	036% 133% 219% 38% 43% 51% 6+0%
Ryan ManningDEF	Southampton	1.17	76provisional	1.01	27%	70	039% 134% 218% 37% 42% 50% 6+0%
Ethan GalbraithMID	Stoke City	1.10	81provisional	1.00	27%	40	038% 135% 218% 36% 42% 50% 6+0%
Caspar JanderMID	Southampton	1.05	80provisional	0.95	25%	38	040% 135% 217% 36% 42% 5+0%
Divin MubamaFWD	Southampton	1.80	43provisional	0.95	25%	28	045% 130% 214% 36% 42% 51% 6+0%
Sorba ThomasFWD	Stoke City	0.96	86provisional	0.93	24%	46	040% 136% 217% 35% 41% 5+0%
Taylor Harwood-BellisDEF	Southampton	0.94	87provisional	0.91	23%	78	041% 136% 216% 35% 41% 5+0%
Jun-ho BaeMID	Stoke City	1.20	64provisional	0.89	23%	88	044% 133% 215% 35% 42% 50% 6+0%

10 of 59 players shown, ranked by projection. Show all 59 →

Saves8 playersHighest: Viktor Johansson, 3.19 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
Viktor JohanssonGK	Stoke City	3.21	89provisional	3.19	59%	72	06% 115% 220% 320% 416% 511% 6+13%
Tommy SimkinGK	Stoke City	2.87	87provisional	2.79	51%	15	08% 118% 223% 320% 414% 59% 6+8%
Gavin BazunuGK	Stoke City	2.75	88provisional	2.71	49%	25	09% 119% 223% 320% 414% 58% 6+7%
Josh GriffithsGK	—	2.78	87provisional	2.71	49%	30	09% 119% 223% 320% 414% 58% 6+8%
Aaron RamsdaleGK	Southampton	2.45	88provisional	2.39	42%	42	011% 122% 224% 319% 412% 56% 6+5%
Daniel PeretzGK	Southampton	2.34	89provisional	2.32	40%	26	012% 123% 225% 319% 411% 56% 6+4%
Alex McCarthyGK	Southampton	2.38	86provisional	2.31	40%	12	012% 123% 225% 319% 411% 56% 6+4%
Gavin BazunuGK	Stoke City	2.13	88provisional	2.09	35%	25	014% 126% 225% 317% 410% 55% 6+3%

2 of 7 markets have no projection for this fixture.