Championship · England

WrexhamvBirmingham City

Friday 28 August at 20:00Racecourse Groundlineups not announced

Referee

Not appointed

0 matches on record

Fouls per game

—

League average

22.6

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

Fouls43 playersHighest: Jay Stansfield, 1.40 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
Jay StansfieldFWD	Birmingham City	1.67	75provisional	1.40	40%	42	026% 133% 223% 311% 44% 51% 6+0%
August PriskeFWD	Birmingham City	2.00	54provisional	1.19	33%	12	038% 130% 218% 39% 44% 51% 6+1%
Tomoki IwataMID	Birmingham City	1.26	84provisional	1.18	33%	45	032% 135% 221% 39% 43% 51% 6+0%
Kieffer MooreFWD	Wrexham	1.50	69provisional	1.15	32%	30	036% 131% 219% 39% 43% 51% 6+0%
Jhon Elmer Solis RomeroMID	Birmingham City	1.42	65provisional	1.03	28%	15	039% 134% 218% 37% 42% 51% 6+0%
Seung-Ho PaikMID	Birmingham City	1.12	78provisional	0.98	26%	39	039% 135% 218% 36% 42% 5+0%
James McCleanMID	Wrexham	1.48	57provisional	0.94	26%	10	047% 127% 215% 37% 42% 51% 6+0%
George DobsonMID	Wrexham	1.15	73provisional	0.93	24%	34	042% 134% 216% 36% 42% 5+0%
Phil NeumannDEF	Birmingham City	0.94	88provisional	0.92	23%	32	040% 136% 217% 35% 41% 5+0%
Christoph KlarerDEF	Birmingham City	0.85	90provisional	0.85	21%	43	043% 136% 215% 34% 41% 5+0%

10 of 43 players shown, ranked by projection. Show all 43 →

Tackles43 playersHighest: George Dobson, 2.14 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
George DobsonMID	Wrexham	2.64	73provisional	2.14	57%	34	019% 124% 221% 315% 410% 55% 6+5%
Kai WagnerDEF	Birmingham City	1.83	87provisional	1.77	51%	16	020% 129% 224% 315% 47% 53% 6+2%
George ThomasonMID	Wrexham	2.04	76provisional	1.72	48%	30	025% 127% 221% 314% 47% 53% 6+2%
Marc LeonardMID	Birmingham City	3.38	46provisional	1.71	44%	10	028% 129% 219% 311% 46% 54% 6+4%
Max CleworthDEF	Wrexham	1.67	88provisional	1.63	47%	40	024% 130% 223% 313% 46% 53% 6+1%
Lewis O'BrienMID	Wrexham	2.06	71provisional	1.63	45%	30	028% 127% 220% 313% 47% 53% 6+2%
James McCleanMID	Wrexham	2.45	57provisional	1.55	41%	10	036% 123% 216% 311% 47% 54% 6+3%
Tomoki IwataMID	Birmingham City	1.65	84provisional	1.54	44%	45	025% 131% 223% 312% 46% 52% 6+1%
Ben SheafMID	Wrexham	2.00	66provisional	1.47	41%	23	028% 131% 221% 312% 45% 52% 6+1%
Matty JamesMID	Wrexham	2.16	60provisional	1.44	39%	24	033% 128% 218% 311% 46% 53% 6+2%

10 of 43 players shown, ranked by projection. Show all 43 →

Shots43 playersHighest: Kieffer Moore, 1.99 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
Kieffer MooreFWD	Wrexham	2.58	69provisional	1.99	54%	30	022% 124% 221% 315% 49% 55% 6+4%
Jay StansfieldFWD	Birmingham City	2.34	75provisional	1.96	56%	42	017% 127% 225% 317% 49% 54% 6+2%
Josh WindassMID	Wrexham	2.75	59provisional	1.79	49%	31	024% 127% 221% 314% 48% 54% 6+3%
Marvin DuckschFWD	Birmingham City	2.47	60provisional	1.64	46%	26	026% 127% 221% 313% 47% 53% 6+2%
Demarai GrayFWD	Birmingham City	2.29	62provisional	1.58	45%	29	025% 130% 222% 313% 46% 53% 6+1%
Nathan BroadheadFWD	Wrexham	2.42	51provisional	1.38	38%	21	033% 129% 219% 310% 45% 52% 6+1%
Patrick RobertsFWD	Birmingham City	1.87	63provisional	1.31	37%	28	033% 130% 220% 310% 44% 52% 6+1%
August PriskeFWD	Birmingham City	2.09	54provisional	1.24	34%	12	037% 129% 217% 39% 44% 52% 6+1%
Carlos Vicente RoblesFWD	Birmingham City	1.69	58provisional	1.10	30%	13	040% 131% 217% 38% 43% 51% 6+0%
Samuel Toby SmithFWD	Wrexham	2.38	39provisional	1.02	26%	18	050% 124% 212% 37% 44% 52% 6+1%

10 of 43 players shown, ranked by projection. Show all 43 →

Fouls won43 playersHighest: Lewis O'Brien, 1.26 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
Lewis O'BrienMID	Wrexham	1.60	71provisional	1.26	35%	30	034% 130% 220% 310% 44% 51% 6+1%
Kieffer MooreFWD	Wrexham	1.43	69provisional	1.10	30%	30	038% 131% 218% 38% 43% 51% 6+0%
Demarai GrayFWD	Birmingham City	1.51	62provisional	1.04	28%	29	039% 133% 218% 37% 42% 51% 6+0%
Ben SheafMID	Wrexham	1.38	66provisional	1.01	27%	23	039% 134% 218% 37% 42% 51% 6+0%
Patrick RobertsFWD	Birmingham City	1.41	63provisional	0.99	27%	28	042% 132% 217% 37% 42% 51% 6+0%
Keshi AndersonFWD	Birmingham City	2.30	38provisional	0.97	25%	11	045% 130% 215% 36% 43% 51% 6+0%
Bright Osayi-SamuelDEF	Birmingham City	1.50	58provisional	0.96	26%	20	044% 130% 216% 37% 42% 51% 6+0%
Ethan LairdDEF	Birmingham City	1.96	43provisional	0.94	24%	12	046% 130% 214% 36% 43% 51% 6+0%
Liberato Gianpaolo CacaceDEF	Wrexham	1.37	60provisional	0.91	24%	8	046% 129% 215% 36% 42% 51% 6+0%
Carlos Vicente RoblesFWD	Birmingham City	1.38	58provisional	0.90	23%	13	046% 131% 215% 36% 42% 51% 6+0%

10 of 43 players shown, ranked by projection. Show all 43 →

Cards43 playersHighest: Christoph Klarer, 0.18 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
Christoph KlarerDEF	Birmingham City	0.18	90provisional	0.18	16%	43	084% 115% 2+1%
James BeadleGK	Birmingham City	0.17	90provisional	0.17	16%	35	084% 114% 2+1%
Jhon Elmer Solis RomeroMID	Birmingham City	0.23	65provisional	0.16	15%	15	085% 114% 2+1%
Callum DoyleDEF	Wrexham	0.17	85provisional	0.16	15%	34	085% 114% 2+1%
Phil NeumannDEF	Birmingham City	0.17	88provisional	0.16	15%	32	085% 114% 2+1%
Tomoki IwataMID	Birmingham City	0.17	84provisional	0.16	15%	45	085% 114% 2+1%
Jay StansfieldFWD	Birmingham City	0.19	75provisional	0.16	14%	42	086% 113% 2+1%
George DobsonMID	Wrexham	0.18	73provisional	0.15	14%	34	086% 113% 2+1%
Seung-Ho PaikMID	Birmingham City	0.16	78provisional	0.14	13%	39	087% 112% 2+1%
Bright Osayi-SamuelDEF	Birmingham City	0.22	58provisional	0.14	13%	20	087% 112% 2+1%

10 of 43 players shown, ranked by projection. Show all 43 →

Saves3 playersHighest: James Beadle, 2.80 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
James BeadleGK	Birmingham City	2.80	90provisional	2.80	51%	35	08% 118% 223% 320% 414% 59% 6+8%
Ryan AllsopGK	Birmingham City	2.62	90provisional	2.62	47%	11	09% 120% 224% 320% 413% 58% 6+7%
Arthur OkonkwoGK	Wrexham	2.53	88provisional	2.47	44%	38	012% 121% 223% 319% 412% 57% 6+6%

1 of 7 markets have no projection for this fixture.