Championship · England

MillwallvNorwich City

Saturday 22 August at 12:30The Denlineups 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

Fouls56 playersHighest: Pelle Elkjær Mattsson, 1.47 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
Pelle Elkjær MattssonMID	Norwich City	1.60	83provisional	1.47	43%	33	024% 133% 224% 312% 45% 52% 6+1%
Josh CoburnFWD	Millwall	1.82	66provisional	1.34	38%	38	031% 131% 221% 311% 44% 51% 6+1%
Jovon MakamaFWD	Norwich City	1.95	59provisional	1.27	36%	19	036% 129% 219% 310% 45% 52% 6+1%
Femi AzeezMID	Millwall	1.52	69provisional	1.17	33%	58	035% 132% 220% 39% 43% 51% 6+0%
Kellen FisherDEF	Norwich City	1.36	76provisional	1.15	32%	74	034% 134% 220% 38% 43% 51% 6+0%
Ryan LeonardDEF	Millwall	1.37	73provisional	1.12	31%	48	037% 132% 219% 38% 43% 51% 6+0%
Tristan CramaDEF	Millwall	1.10	87provisional	1.06	29%	66	035% 136% 219% 37% 42% 50% 6+0%
Anis Ben SlimaneMID	Norwich City	1.50	61provisional	1.01	27%	46	039% 134% 218% 37% 42% 51% 6+0%
Jake CooperDEF	Millwall	1.01	89provisional	1.00	26%	83	037% 136% 218% 36% 42% 5+0%
Jacob WrightMID	Norwich City	1.72	50provisional	0.95	25%	28	044% 130% 215% 37% 42% 51% 6+0%

10 of 56 players shown, ranked by projection. Show all 56 →

Tackles56 playersHighest: Ryan Leonard, 2.16 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
Ryan LeonardDEF	Millwall	2.67	73provisional	2.16	57%	48	021% 122% 220% 315% 410% 56% 6+6%
Casper De NorreMID	Millwall	2.34	78provisional	2.02	57%	74	017% 126% 224% 316% 49% 54% 6+3%
Pelle Elkjær MattssonMID	Norwich City	2.13	83provisional	1.96	55%	33	018% 127% 224% 316% 49% 54% 6+3%
Kenny McLeanMID	Norwich City	2.00	87provisional	1.93	55%	74	018% 127% 224% 316% 48% 54% 6+3%
Kellen FisherDEF	Norwich City	2.21	76provisional	1.87	52%	74	021% 126% 222% 315% 48% 54% 6+3%
Joe BryanDEF	Millwall	2.38	69provisional	1.83	49%	45	027% 124% 219% 314% 48% 54% 6+4%
Billy MitchellMID	Millwall	2.58	62provisional	1.79	48%	34	027% 125% 219% 313% 48% 54% 6+4%
Tristan CramaDEF	Millwall	1.78	87provisional	1.72	49%	66	021% 130% 224% 314% 47% 53% 6+2%
Ali AhmedMID	Norwich City	2.16	69provisional	1.66	47%	16	025% 129% 222% 313% 47% 53% 6+2%
Jack StaceyDEF	Norwich City	2.60	54provisional	1.56	40%	47	034% 126% 217% 311% 46% 54% 6+3%

10 of 56 players shown, ranked by projection. Show all 56 →

Shots56 playersHighest: Femi Azeez, 2.13 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
Femi AzeezMID	Millwall	2.77	69provisional	2.13	58%	58	019% 123% 221% 316% 410% 56% 6+4%
Joshua SargentFWD	Norwich City	2.10	81provisional	1.90	55%	53	018% 128% 225% 316% 48% 54% 6+2%
Mihailo IvanovićFWD	Millwall	2.45	63provisional	1.70	47%	60	027% 127% 220% 313% 47% 54% 6+2%
Josh CoburnFWD	Millwall	2.27	66provisional	1.66	47%	38	025% 128% 222% 314% 47% 53% 6+2%
Mohamed ToureFWD	Norwich City	2.53	54provisional	1.52	43%	9	025% 132% 223% 312% 45% 52% 6+1%
Thierno Mamadou Lamarana BalloFWD	Millwall	2.12	60provisional	1.40	40%	25	033% 128% 220% 311% 45% 52% 6+1%
Camiel NeghliFWD	Millwall	1.76	71provisional	1.38	39%	43	030% 131% 221% 311% 45% 52% 6+1%
Jovon MakamaFWD	Norwich City	2.04	59provisional	1.32	37%	19	035% 128% 218% 310% 45% 52% 6+1%
Edmond-Paris MaghomaMID	Norwich City	1.82	59provisional	1.20	33%	12	034% 133% 219% 39% 43% 51% 6+0%
Emiliano Marcondes Camargo HansenMID	Norwich City	1.60	60provisional	1.06	29%	38	040% 132% 217% 38% 43% 51% 6+0%

10 of 56 players shown, ranked by projection. Show all 56 →

Fouls won56 playersHighest: Pelle Elkjær Mattsson, 1.78 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
Pelle Elkjær MattssonMID	Norwich City	1.95	83provisional	1.78	52%	33	019% 129% 225% 315% 47% 53% 6+2%
Kenny McLeanMID	Norwich City	1.81	87provisional	1.75	51%	74	019% 130% 225% 315% 47% 53% 6+1%
Ali AhmedMID	Norwich City	1.84	69provisional	1.41	40%	16	028% 132% 222% 311% 45% 52% 6+1%
Emiliano Marcondes Camargo HansenMID	Norwich City	1.98	60provisional	1.31	37%	38	033% 131% 219% 310% 44% 52% 6+1%
Kellen FisherDEF	Norwich City	1.33	76provisional	1.13	31%	74	035% 133% 219% 38% 43% 51% 6+0%
Harry DarlingDEF	Norwich City	1.16	86provisional	1.11	30%	34	035% 134% 219% 38% 42% 51% 6+0%
Ryan LeonardDEF	Millwall	1.33	73provisional	1.08	30%	48	039% 131% 218% 38% 43% 51% 6+0%
Japhet TangangaDEF	Millwall	1.08	89provisional	1.07	29%	42	036% 136% 219% 37% 42% 51% 6+0%
Josh CoburnFWD	Millwall	1.45	66provisional	1.06	29%	38	039% 132% 218% 38% 43% 51% 6+0%
Joshua SargentFWD	Norwich City	1.03	81provisional	0.93	24%	53	041% 135% 216% 36% 41% 5+0%

10 of 56 players shown, ranked by projection. Show all 56 →

Cards56 playersHighest: Pelle Elkjær Mattsson, 0.24 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
Pelle Elkjær MattssonMID	Norwich City	0.26	83provisional	0.24	21%	33	079% 118% 22% 3+0%
Kellen FisherDEF	Norwich City	0.25	76provisional	0.21	19%	74	081% 117% 22% 3+0%
Tristan CramaDEF	Millwall	0.17	87provisional	0.17	15%	66	085% 114% 2+1%
Jake CooperDEF	Millwall	0.16	89provisional	0.16	15%	83	085% 114% 2+1%
Femi AzeezMID	Millwall	0.21	69provisional	0.16	15%	58	085% 113% 2+1%
Ryan LeonardDEF	Millwall	0.20	73provisional	0.16	15%	48	085% 113% 2+1%
Joe BryanDEF	Millwall	0.21	69provisional	0.16	15%	45	085% 113% 2+1%
Anis Ben SlimaneMID	Norwich City	0.21	61provisional	0.14	13%	46	087% 112% 2+1%
Zak Norton SturgeDEF	Millwall	0.21	63provisional	0.14	13%	37	087% 112% 2+1%
Vladan KovačevićGK	Norwich City	0.14	90provisional	0.14	13%	45	087% 112% 2+1%

10 of 56 players shown, ranked by projection. Show all 56 →

Saves2 playersHighest: Vladan Kovačević, 2.89 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
Vladan KovačevićGK	Norwich City	2.89	90provisional	2.89	53%	45	07% 117% 222% 320% 415% 59% 6+9%
Maxime Teremoana CrocombeGK	Millwall	2.63	90provisional	2.63	48%	23	09% 120% 223% 320% 413% 58% 6+7%

1 of 7 markets have no projection for this fixture.