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

23.1

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 v20260813-1451 · Tackles v20260813-1451 · Shots v20260813-1451 · Fouls won v20260813-1451 · Cards v20260813-1451 · Saves v20260813-1451

Model projections

Fouls52 playersHighest: Flynn Downes, 1.52 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	1.81	76provisional	1.52	44%	59	025% 131% 223% 313% 45% 52% 6+1%
Jay StansfieldFWD	Birmingham City	1.73	75provisional	1.45	42%	42	025% 133% 224% 312% 45% 51% 6+1%
Caspar JanderMID	Southampton	1.45	78provisional	1.25	35%	34	031% 133% 221% 310% 43% 51% 6+0%
August PriskeFWD	Birmingham City	2.07	54provisional	1.23	34%	12	036% 130% 218% 39% 44% 52% 6+1%
Tomoki IwataMID	Birmingham City	1.30	84provisional	1.22	34%	45	031% 135% 221% 39% 43% 51% 6+0%
Cyle LarinFWD	Southampton	1.88	53provisional	1.10	30%	12	038% 132% 218% 38% 43% 51% 6+0%
Jhon SolísMID	Birmingham City	1.47	65provisional	1.07	29%	15	037% 134% 218% 37% 42% 51% 6+0%
Seung-ho PaikMID	Birmingham City	1.16	78provisional	1.01	27%	39	038% 135% 218% 37% 42% 50% 6+0%
Phil NeumannDEF	Birmingham City	0.97	88provisional	0.95	25%	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%
Adam ArmstrongFWD	Southampton	1.17	67provisional	0.87	22%	42	044% 134% 215% 35% 41% 5+0%
Marc LeonardMID	Birmingham City	1.67	46provisional	0.85	21%	10	048% 131% 213% 35% 42% 51% 6+0%
Kuryu MatsukiMID	Southampton	1.50	51provisional	0.84	22%	13	048% 130% 214% 35% 42% 50% 6+0%
WelingtonDEF	Southampton	1.46	51provisional	0.83	22%	17	049% 129% 214% 35% 42% 50% 6+0%
Taylor Harwood-BellisDEF	Southampton	0.85	87provisional	0.83	20%	76	044% 135% 215% 34% 41% 5+0%
Bright Osayi-SamuelDEF	Birmingham City	1.23	58provisional	0.79	20%	20	049% 131% 214% 35% 41% 5+0%
Kyogo FuruhashiFWD	Birmingham City	1.95	36provisional	0.78	19%	14	051% 130% 212% 35% 42% 51% 6+0%
Finn AzazMID	Southampton	0.94	74provisional	0.77	19%	39	048% 134% 214% 34% 41% 5+0%
Joshua QuarshieDEF	Southampton	0.93	73provisional	0.75	19%	11	050% 131% 213% 34% 41% 5+0%
Kai WagnerDEF	Birmingham City	0.74	87provisional	0.72	16%	16	049% 135% 213% 33% 41% 5+0%
Lewis KoumasFWD	Birmingham City	1.99	32provisional	0.72	17%	10	054% 129% 211% 34% 41% 50% 6+0%
Ryan ManningDEF	Southampton	0.89	71provisional	0.71	17%	59	051% 132% 212% 33% 41% 5+0%
James BreeDEF	Southampton	0.85	74provisional	0.70	16%	32	051% 132% 212% 33% 41% 5+0%
Carlos VicenteFWD	Birmingham City	1.07	58provisional	0.69	17%	13	053% 130% 212% 34% 41% 5+0%
Tom FellowsMID	Southampton	1.00	60provisional	0.66	15%	31	053% 132% 211% 33% 41% 5+0%
Willum Thór WillumssonMID	Birmingham City	1.50	39provisional	0.66	17%	5	060% 123% 211% 34% 41% 5+0%
Joe AriboMID	Southampton	1.07	55provisional	0.65	15%	27	055% 130% 211% 33% 41% 5+0%
Demarai GrayFWD	Birmingham City	0.92	62provisional	0.64	14%	29	054% 132% 211% 33% 4+1%
Marvin DuckschFWD	Birmingham City	0.95	60provisional	0.63	14%	26	055% 130% 211% 33% 41% 5+0%
Nathan WoodDEF	Southampton	0.78	72provisional	0.63	14%	40	056% 130% 211% 33% 41% 5+0%
Ross StewartFWD	Southampton	1.47	38provisional	0.62	14%	21	057% 129% 210% 33% 41% 5+0%
Cameron BraggMID	Southampton	1.37	40provisional	0.61	15%	7	060% 125% 210% 33% 41% 5+0%
Patrick RobertsFWD	Birmingham City	0.85	63provisional	0.59	13%	28	057% 130% 210% 32% 4+1%
Tommy DoyleMID	Birmingham City	1.05	50provisional	0.58	13%	23	059% 128% 210% 33% 41% 5+0%
Alex CochraneDEF	Birmingham City	0.70	73provisional	0.57	12%	19	058% 130% 29% 32% 4+0%
Cameron ArcherFWD	Southampton	1.28	38provisional	0.53	12%	30	062% 126% 28% 32% 41% 5+0%
Yukinari SugawaraDEF	Southampton	0.93	51provisional	0.52	11%	22	062% 127% 28% 32% 4+0%
Jack StephensDEF	Southampton	0.59	80provisional	0.52	10%	47	060% 130% 28% 32% 4+0%
Jack RobinsonDEF	Birmingham City	0.63	75provisional	0.52	10%	15	060% 130% 28% 31% 4+0%
Ethan LairdDEF	Birmingham City	1.01	43provisional	0.49	10%	12	064% 126% 28% 32% 4+0%
Ronnie EdwardsDEF	Southampton	0.77	57provisional	0.49	11%	8	065% 124% 28% 32% 4+0%
Léo ScienzaMID	Southampton	0.65	66provisional	0.48	9%	35	063% 128% 27% 31% 4+0%
Damion DownsFWD	Southampton	1.52	25provisional	0.42	7%	4	067% 126% 26% 31% 4+0%
Samuel EdozieFWD	Southampton	1.50	25provisional	0.41	7%	5	068% 124% 26% 31% 4+0%
Keshi AndersonFWD	Birmingham City	0.88	38provisional	0.37	6%	11	071% 123% 25% 31% 4+0%
Ryan FraserFWD	Southampton	0.56	50provisional	0.31	5%	15	074% 121% 24% 3+1%
Gavin BazunuGK	Southampton	0.05	90provisional	0.05	0%	19	095% 14% 2+0%
Aaron RamsdaleGK	Southampton	0.03	90provisional	0.03	—	30	097% 1+3%
James BeadleGK	Birmingham City	0.03	90provisional	0.03	—	35	097% 1+3%
Ryan AllsopGK	Birmingham City	0.01	90provisional	0.01	—	11	099% 1+1%
Alex McCarthyGK	Southampton	0.01	90provisional	0.01	—	12	099% 1+1%
Daniel PeretzGK	Southampton	0.00	91provisional	0.00	—	22	0100% 1+0%

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

Tackles52 playersHighest: Caspar Jander, 2.63 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	3.05	78provisional	2.63	67%	34	014% 119% 220% 317% 413% 58% 6+9%
Kai WagnerDEF	Birmingham City	1.93	87provisional	1.87	53%	16	018% 128% 224% 315% 48% 54% 6+2%
Marc LeonardMID	Birmingham City	3.57	46provisional	1.81	46%	10	026% 128% 219% 311% 47% 54% 6+5%
WelingtonDEF	Southampton	2.87	51provisional	1.63	43%	17	032% 125% 217% 312% 47% 54% 6+3%
Tomoki IwataMID	Birmingham City	1.74	84provisional	1.63	46%	45	023% 130% 223% 313% 46% 53% 6+1%
Flynn DownesMID	Southampton	1.93	76provisional	1.62	46%	59	025% 129% 222% 313% 46% 53% 6+2%
Seung-ho PaikMID	Birmingham City	1.67	78provisional	1.46	41%	39	027% 131% 222% 311% 45% 52% 6+1%
Finn AzazMID	Southampton	1.73	74provisional	1.43	40%	39	029% 131% 221% 311% 45% 52% 6+1%
Jhon SolísMID	Birmingham City	1.92	65provisional	1.40	39%	15	030% 131% 220% 311% 45% 52% 6+1%
James BreeDEF	Southampton	1.67	74provisional	1.37	39%	32	031% 130% 220% 311% 45% 52% 6+1%

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

Shots52 playersHighest: Jay Stansfield, 2.54 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	2.54	69%	42	011% 121% 223% 319% 413% 57% 6+6%
Marvin DuckschFWD	Birmingham City	3.20	60provisional	2.12	57%	26	020% 123% 221% 316% 410% 56% 6+5%
Demarai GrayFWD	Birmingham City	2.97	62provisional	2.04	56%	29	018% 125% 223% 316% 49% 55% 6+4%
Adam ArmstrongFWD	Southampton	2.41	67provisional	1.80	51%	42	022% 127% 223% 315% 48% 54% 6+2%
Léo ScienzaMID	Southampton	2.33	66provisional	1.72	49%	35	022% 129% 223% 314% 47% 53% 6+2%
Patrick RobertsFWD	Birmingham City	2.42	63provisional	1.70	48%	28	025% 127% 221% 314% 47% 53% 6+2%
Finn AzazMID	Southampton	2.06	74provisional	1.70	49%	39	022% 129% 223% 314% 47% 53% 6+2%
August PriskeFWD	Birmingham City	2.71	54provisional	1.61	43%	12	029% 127% 219% 312% 47% 53% 6+3%
Carlos VicenteFWD	Birmingham City	2.20	58provisional	1.42	39%	13	032% 129% 219% 311% 45% 52% 6+1%
Kyogo FuruhashiFWD	Birmingham City	3.23	36provisional	1.29	33%	14	037% 130% 216% 38% 44% 52% 6+2%

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

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	3.20	66provisional	2.36	64%	35	014% 122% 222% 318% 412% 57% 6+6%
Demarai GrayFWD	Birmingham City	1.66	62provisional	1.14	31%	29	036% 133% 219% 38% 43% 51% 6+0%
Ryan ManningDEF	Southampton	1.40	71provisional	1.11	30%	59	037% 132% 218% 38% 43% 51% 6+0%
Patrick RobertsFWD	Birmingham City	1.55	63provisional	1.09	30%	28	039% 131% 218% 38% 43% 51% 6+0%
Keshi AndersonFWD	Birmingham City	2.53	38provisional	1.07	28%	11	042% 131% 215% 37% 43% 51% 6+1%
Bright Osayi-SamuelDEF	Birmingham City	1.65	58provisional	1.05	29%	20	041% 130% 217% 38% 43% 51% 6+0%
Ethan LairdDEF	Birmingham City	2.16	43provisional	1.03	27%	12	043% 130% 215% 37% 43% 51% 6+1%
Finn AzazMID	Southampton	1.24	74provisional	1.03	28%	39	038% 134% 218% 37% 42% 51% 6+0%
Kuryu MatsukiMID	Southampton	1.82	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% 37% 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	0.35	76provisional	0.29	25%	59	075% 121% 23% 3+0%
Gavin BazunuGK	Southampton	0.27	90provisional	0.27	24%	19	076% 120% 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	0.17	90provisional	0.17	16%	35	084% 115% 2+1%
Jhon SolísMID	Birmingham City	0.24	65provisional	0.17	16%	15	084% 114% 2+1%
Caspar JanderMID	Southampton	0.20	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, 3.61 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	3.61	90provisional	3.61	66%	30	04% 112% 218% 319% 417% 512% 6+18%
Alex McCarthyGK	Southampton	3.03	90provisional	3.03	56%	12	07% 116% 221% 320% 415% 510% 6+11%
Daniel PeretzGK	Southampton	2.96	91provisional	3.00	55%	22	07% 116% 222% 320% 415% 510% 6+10%
Gavin BazunuGK	Southampton	2.78	90provisional	2.78	51%	19	08% 118% 223% 320% 414% 58% 6+8%
James BeadleGK	Birmingham City	2.35	90provisional	2.35	41%	35	012% 123% 225% 319% 412% 56% 6+5%
Ryan AllsopGK	Birmingham City	2.20	90provisional	2.20	37%	11	013% 125% 225% 318% 410% 55% 6+4%

1 of 7 markets have no projection for this fixture.