Championship · England

Birmingham CityvSouthampton

Tuesday 1 September at 19:45St. Andrew's Stadiumlineups not announced

Referee

Not appointed

0 matches on record

Fouls per game

—

League average

23.2

fouls per match, both teams

Value

No edges published on this fixture. An edge is published only when a bookmaker's price beats the model's fair price by that market's full bar — most fixtures never produce one. The value board explains every gate a candidate has to clear.

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 v20260815-1512 · Tackles v20260815-1512 · Shots v20260815-1512 · Fouls won v20260815-1512 · Cards v20260815-1512 · Saves v20260815-1512

Model projections

Fouls52 playersHighest: Flynn Downes, 1.50 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
Flynn DownesMID	Southampton	1.79	76provisional	1.50	44%	59	025% 131% 223% 313% 45% 52% 6+1%
Jay StansfieldFWD	Birmingham City	1.72	75provisional	1.44	42%	42	025% 133% 224% 312% 45% 51% 6+0%
Caspar JanderMID	Southampton	1.43	78provisional	1.23	35%	34	032% 133% 221% 39% 43% 51% 6+0%
August PriskeFWD	Birmingham City	2.06	54provisional	1.23	34%	12	037% 130% 218% 39% 44% 52% 6+1%
Tomoki IwataMID	Birmingham City	1.30	84provisional	1.21	34%	45	031% 135% 221% 39% 43% 51% 6+0%
Cyle LarinFWD	Southampton	1.86	53provisional	1.09	30%	12	039% 132% 217% 38% 43% 51% 6+0%
Jhon SolísMID	Birmingham City	1.46	65provisional	1.06	29%	15	038% 134% 218% 37% 42% 51% 6+0%
Seung-ho PaikMID	Birmingham City	1.16	78provisional	1.01	27%	39	038% 135% 218% 36% 42% 5+1%
Phil NeumannDEF	Birmingham City	0.97	88provisional	0.95	24%	32	039% 136% 217% 36% 41% 5+0%
Christoph KlarerDEF	Birmingham City	0.88	90provisional	0.88	22%	43	042% 136% 216% 35% 41% 5+0%

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

Tackles52 playersHighest: Caspar Jander, 1.63 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
Caspar JanderMID	Southampton	1.89	78provisional	1.63	46%	34	026% 129% 222% 313% 46% 53% 6+2%
Kai WagnerDEF	Birmingham City	1.38	87provisional	1.34	38%	16	029% 133% 221% 310% 44% 51% 6+1%
Flynn DownesMID	Southampton	1.26	76provisional	1.06	29%	59	039% 133% 218% 37% 43% 51% 6+0%
Seung-ho PaikMID	Birmingham City	1.10	78provisional	0.96	25%	39	041% 134% 216% 36% 42% 51% 6+0%
Marc LeonardMID	Birmingham City	1.88	46provisional	0.95	24%	10	046% 130% 214% 36% 43% 51% 6+1%
Tomoki IwataMID	Birmingham City	1.00	84provisional	0.93	24%	45	042% 134% 216% 36% 42% 50% 6+0%
Joe AriboMID	Southampton	1.51	55provisional	0.92	24%	27	046% 130% 214% 36% 42% 51% 6+0%
WelingtonDEF	Southampton	1.57	51provisional	0.90	24%	17	048% 128% 214% 36% 42% 51% 6+0%
Ryan ManningDEF	Southampton	1.12	71provisional	0.89	23%	59	046% 131% 215% 36% 42% 50% 6+0%
Jack StephensDEF	Southampton	0.96	80provisional	0.86	22%	47	046% 132% 215% 35% 41% 50% 6+0%

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

Shots52 playersHighest: Jay Stansfield, 2.55 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
Jay StansfieldFWD	Birmingham City	3.04	75provisional	2.55	69%	42	010% 121% 223% 319% 413% 57% 6+6%
Marvin DuckschFWD	Birmingham City	3.21	60provisional	2.13	57%	26	020% 123% 221% 316% 410% 56% 6+5%
Demarai GrayFWD	Birmingham City	2.98	62provisional	2.05	56%	29	018% 125% 223% 316% 49% 55% 6+4%
Adam ArmstrongFWD	Southampton	2.40	67provisional	1.79	51%	42	022% 127% 223% 315% 48% 54% 6+2%
Patrick RobertsFWD	Birmingham City	2.43	63provisional	1.71	48%	28	025% 127% 221% 314% 47% 53% 6+2%
Léo ScienzaMID	Southampton	2.31	66provisional	1.71	49%	35	023% 129% 223% 314% 47% 53% 6+2%
Finn AzazMID	Southampton	2.04	74provisional	1.69	49%	39	023% 129% 223% 314% 47% 53% 6+1%
August PriskeFWD	Birmingham City	2.72	54provisional	1.62	43%	12	029% 127% 219% 312% 47% 54% 6+3%
Carlos VicenteFWD	Birmingham City	2.20	58provisional	1.43	39%	13	032% 129% 219% 311% 46% 52% 6+1%
Kyogo FuruhashiFWD	Birmingham City	3.25	36provisional	1.30	33%	14	036% 130% 216% 38% 44% 52% 6+2%
Tomoki IwataMID	Birmingham City	1.38	84provisional	1.29	37%	45	030% 134% 222% 310% 44% 51% 6+0%
Cyle LarinFWD	Southampton	2.02	53provisional	1.18	32%	12	037% 131% 218% 39% 44% 51% 6+1%
Seung-ho PaikMID	Birmingham City	1.31	78provisional	1.14	31%	39	034% 134% 220% 38% 43% 51% 6+0%
Willum Thór WillumssonMID	Birmingham City	2.45	39provisional	1.07	29%	5	049% 122% 214% 38% 44% 52% 6+1%
Jhon SolísMID	Birmingham City	1.41	65provisional	1.02	27%	15	039% 133% 217% 37% 42% 51% 6+0%
Keshi AndersonFWD	Birmingham City	2.39	38provisional	1.01	26%	11	043% 131% 215% 37% 43% 51% 6+1%
Ross StewartFWD	Southampton	2.37	38provisional	1.00	26%	21	043% 131% 215% 37% 43% 51% 6+0%
Lewis KoumasFWD	Birmingham City	2.52	32provisional	0.91	23%	10	048% 129% 213% 36% 43% 51% 6+1%
Cameron ArcherFWD	Southampton	2.11	38provisional	0.88	22%	30	049% 129% 213% 36% 42% 51% 6+0%
Kuryu MatsukiMID	Southampton	1.48	51provisional	0.83	21%	13	049% 130% 214% 35% 42% 51% 6+0%
Marc LeonardMID	Birmingham City	1.59	46provisional	0.81	20%	10	050% 131% 213% 35% 42% 51% 6+0%
Tom FellowsMID	Southampton	1.18	60provisional	0.78	19%	31	048% 133% 213% 34% 41% 5+0%
Ethan LairdDEF	Birmingham City	1.58	43provisional	0.76	19%	12	052% 129% 212% 35% 41% 50% 6+0%
Tommy DoyleMID	Birmingham City	1.35	50provisional	0.75	19%	23	052% 129% 212% 34% 41% 50% 6+0%
Damion DownsFWD	Southampton	2.47	25provisional	0.68	15%	4	053% 132% 211% 33% 41% 50% 6+0%
Ryan ManningDEF	Southampton	0.86	71provisional	0.68	16%	59	053% 131% 212% 33% 41% 5+0%
Jack RobinsonDEF	Birmingham City	0.81	75provisional	0.67	15%	15	052% 133% 211% 33% 41% 5+0%
Taylor Harwood-BellisDEF	Southampton	0.64	87provisional	0.62	13%	76	054% 132% 210% 32% 4+0%
Christoph KlarerDEF	Birmingham City	0.60	90provisional	0.60	12%	43	056% 132% 210% 32% 4+0%
Alex CochraneDEF	Birmingham City	0.70	73provisional	0.57	12%	19	058% 130% 29% 32% 4+0%
Caspar JanderMID	Southampton	0.65	78provisional	0.56	12%	34	058% 130% 29% 32% 4+0%
Ryan FraserFWD	Southampton	1.02	50provisional	0.56	12%	15	060% 128% 29% 32% 41% 5+0%
Samuel EdozieFWD	Southampton	1.91	25provisional	0.52	11%	5	062% 127% 28% 32% 41% 5+0%
Kai WagnerDEF	Birmingham City	0.54	87provisional	0.52	10%	16	060% 130% 28% 32% 4+0%
Phil NeumannDEF	Birmingham City	0.49	88provisional	0.48	8%	32	063% 129% 27% 31% 4+0%
Joe AriboMID	Southampton	0.78	55provisional	0.47	9%	27	064% 126% 27% 32% 4+0%
James BreeDEF	Southampton	0.55	74provisional	0.45	8%	32	065% 127% 27% 31% 4+0%
Joshua QuarshieDEF	Southampton	0.47	73provisional	0.38	7%	11	070% 124% 25% 31% 4+0%
Yukinari SugawaraDEF	Southampton	0.62	51provisional	0.35	6%	22	072% 122% 25% 31% 4+0%
Flynn DownesMID	Southampton	0.40	76provisional	0.33	5%	59	072% 123% 24% 3+1%
Nathan WoodDEF	Southampton	0.37	72provisional	0.30	4%	40	075% 121% 24% 3+1%
Jack StephensDEF	Southampton	0.33	80provisional	0.30	4%	47	075% 121% 23% 3+0%
Cameron BraggMID	Southampton	0.66	40provisional	0.30	5%	7	076% 119% 24% 31% 4+0%
Ronnie EdwardsDEF	Southampton	0.44	57provisional	0.28	4%	8	077% 118% 24% 3+1%
Bright Osayi-SamuelDEF	Birmingham City	0.41	58provisional	0.26	4%	20	078% 119% 23% 3+0%
WelingtonDEF	Southampton	0.40	51provisional	0.23	3%	17	080% 117% 23% 3+0%
James BeadleGK	Birmingham City	0.02	90provisional	0.02	—	35	098% 1+2%
Ryan AllsopGK	Birmingham City	0.00	90provisional	0.00	—	11	0100% 1+0%
Alex McCarthyGK	Southampton	0.00	90provisional	0.00	—	12	0100% 1+0%
Daniel PeretzGK	Southampton	0.00	91provisional	0.00	—	22	0100% 1+0%
Gavin BazunuGK	Southampton	0.00	90provisional	0.00	—	19	0100% 1+0%
Aaron RamsdaleGK	Southampton	0.00	90provisional	0.00	—	30	0100% 1+0%

All 52 players shown, ranked by projection. Show fewer ↥

Fouls won52 playersHighest: Léo Scienza, 2.36 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	3.19	66provisional	2.36	63%	35	014% 122% 222% 318% 412% 57% 6+6%
Demarai GrayFWD	Birmingham City	1.65	62provisional	1.14	31%	29	036% 133% 219% 38% 43% 51% 6+0%
Ryan ManningDEF	Southampton	1.39	71provisional	1.10	30%	59	038% 132% 218% 38% 43% 51% 6+0%
Patrick RobertsFWD	Birmingham City	1.54	63provisional	1.08	30%	28	039% 131% 218% 38% 43% 51% 6+0%
Keshi AndersonFWD	Birmingham City	2.51	38provisional	1.06	28%	11	042% 131% 215% 37% 43% 51% 6+1%
Bright Osayi-SamuelDEF	Birmingham City	1.64	58provisional	1.05	29%	20	041% 130% 217% 38% 43% 51% 6+0%
Ethan LairdDEF	Birmingham City	2.14	43provisional	1.03	27%	12	043% 130% 215% 37% 43% 51% 6+1%
Finn AzazMID	Southampton	1.24	74provisional	1.02	27%	39	039% 134% 218% 37% 42% 51% 6+0%
Kuryu MatsukiMID	Southampton	1.81	51provisional	1.02	27%	13	043% 130% 216% 37% 43% 51% 6+0%
Caspar JanderMID	Southampton	1.15	78provisional	0.99	26%	34	040% 134% 217% 36% 42% 50% 6+0%

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

Cards52 playersHighest: Flynn Downes, 0.29 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
Flynn DownesMID	Southampton	0.34	76provisional	0.29	25%	59	075% 121% 23% 3+0%
Gavin BazunuGK	Southampton	0.27	90provisional	0.27	24%	19	076% 121% 23% 3+0%
Aaron RamsdaleGK	Southampton	0.20	90provisional	0.20	18%	30	082% 116% 22% 3+0%
Taylor Harwood-BellisDEF	Southampton	0.20	87provisional	0.19	17%	76	083% 116% 22% 3+0%
Christoph KlarerDEF	Birmingham City	0.18	90provisional	0.18	17%	43	083% 115% 2+1%
James BeadleGK	Birmingham City	0.18	90provisional	0.18	16%	35	084% 115% 2+1%
Jhon SolísMID	Birmingham City	0.23	65provisional	0.17	15%	15	085% 114% 2+1%
Caspar JanderMID	Southampton	0.19	78provisional	0.17	15%	34	085% 114% 2+1%
Phil NeumannDEF	Birmingham City	0.17	88provisional	0.17	15%	32	085% 114% 2+1%
Tomoki IwataMID	Birmingham City	0.18	84provisional	0.17	15%	45	085% 114% 2+1%

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

Saves6 playersHighest: Aaron Ramsdale, 3.66 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
Aaron RamsdaleGK	Southampton	3.66	90provisional	3.66	67%	30	04% 111% 217% 319% 417% 513% 6+19%
Alex McCarthyGK	Southampton	3.07	90provisional	3.07	57%	12	06% 116% 221% 320% 415% 510% 6+11%
Daniel PeretzGK	Southampton	3.00	91provisional	3.05	56%	22	06% 116% 221% 320% 415% 510% 6+11%
Gavin BazunuGK	Southampton	2.82	90provisional	2.82	52%	19	08% 118% 223% 320% 414% 59% 6+9%
James BeadleGK	Birmingham City	2.36	90provisional	2.36	41%	35	011% 123% 225% 319% 412% 56% 6+5%
Ryan AllsopGK	Birmingham City	2.22	90provisional	2.22	38%	11	013% 124% 225% 318% 411% 55% 6+4%

1 of 7 markets have no projection for this fixture.