Premier League · England

Nottingham ForestvLeeds United

Saturday 22 August at 15:00The City 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

Fouls41 playersHighest: Ibrahim Sangaré, 1.33 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
Ibrahim SangaréMID	Nottingham Forest	1.83	65provisional	1.33	38%	32	033% 129% 220% 311% 45% 52% 6+1%
Ethan AmpaduMID	Leeds United	1.41	85provisional	1.32	38%	62	028% 134% 223% 310% 44% 51% 6+0%
Anton StachMID	Leeds United	1.34	82provisional	1.22	34%	28	030% 135% 222% 39% 43% 51% 6+0%
Dominic Calvert-LewinFWD	Leeds United	1.40	78provisional	1.21	34%	31	033% 133% 221% 39% 43% 51% 6+0%
Ryan YatesMID	Nottingham Forest	2.34	45provisional	1.16	30%	29	039% 130% 216% 38% 44% 52% 6+1%
Jayden BogleDEF	Leeds United	1.18	85provisional	1.12	31%	77	033% 136% 220% 38% 42% 51% 6+0%
Igor Jesus Maciel da CruzFWD	Nottingham Forest	1.60	63provisional	1.11	31%	31	036% 133% 219% 38% 43% 51% 6+0%
Ao TanakaMID	Leeds United	1.34	66provisional	0.97	26%	55	042% 131% 217% 37% 42% 51% 6+0%
Gabriel GudmundssonDEF	Leeds United	1.03	83provisional	0.95	25%	31	040% 136% 217% 36% 41% 5+0%
Ilia GruevMID	Leeds United	1.28	66provisional	0.93	25%	36	043% 132% 216% 36% 42% 50% 6+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Tackles41 playersHighest: Neco Williams, 2.48 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
Neco WilliamsDEF	Nottingham Forest	2.77	80provisional	2.48	65%	66	014% 121% 221% 317% 412% 57% 6+7%
Nicolás Martín DomínguezMID	Nottingham Forest	3.83	52provisional	2.20	56%	44	021% 123% 219% 314% 410% 56% 6+7%
Anton StachMID	Leeds United	2.39	82provisional	2.18	60%	28	015% 125% 224% 317% 410% 55% 6+4%
Ibrahim SangaréMID	Nottingham Forest	2.95	65provisional	2.14	56%	32	023% 122% 219% 315% 410% 56% 6+6%
Jayden BogleDEF	Leeds United	2.17	85provisional	2.05	58%	77	016% 126% 224% 316% 49% 55% 6+3%
Ethan AmpaduMID	Leeds United	2.11	85provisional	1.99	56%	62	018% 126% 224% 316% 49% 54% 6+3%
Gabriel GudmundssonDEF	Leeds United	2.12	83provisional	1.95	55%	31	018% 127% 224% 316% 49% 54% 6+3%
James JustinDEF	Leeds United	2.62	65provisional	1.91	50%	22	028% 122% 218% 314% 49% 55% 6+5%
Ao TanakaMID	Leeds United	2.44	66provisional	1.78	48%	55	027% 125% 219% 313% 48% 54% 6+3%
Sean LongstaffMID	Leeds United	3.48	44provisional	1.71	40%	13	037% 123% 213% 39% 47% 55% 6+6%

10 of 41 players shown, ranked by projection. Show all 41 →

Shots41 playersHighest: Morgan Gibbs-White, 1.86 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
Morgan Gibbs-WhiteMID	Nottingham Forest	2.01	84provisional	1.86	54%	70	017% 128% 225% 316% 48% 53% 6+2%
Dominic Calvert-LewinFWD	Leeds United	2.11	78provisional	1.82	52%	31	021% 127% 223% 315% 48% 54% 6+2%
Igor Jesus Maciel da CruzFWD	Nottingham Forest	2.58	63provisional	1.80	50%	31	023% 127% 222% 314% 48% 54% 6+2%
Chris WoodFWD	Nottingham Forest	2.08	76provisional	1.76	51%	46	021% 128% 224% 315% 47% 53% 6+2%
Daniel JamesFWD	Leeds United	2.74	58provisional	1.76	47%	36	028% 124% 219% 313% 48% 54% 6+3%
Joël PiroeFWD	Leeds United	2.67	54provisional	1.60	43%	38	034% 123% 217% 312% 47% 54% 6+3%
Brenden AaronsonMID	Leeds United	1.79	73provisional	1.45	42%	74	027% 131% 222% 312% 45% 52% 6+1%
Anton StachMID	Leeds United	1.50	82provisional	1.36	39%	28	027% 134% 223% 311% 44% 51% 6+0%
Degnand Wilfried GnontoFWD	Leeds United	2.75	43provisional	1.30	34%	32	040% 126% 215% 39% 45% 53% 6+2%
Noah OkaforFWD	Leeds United	1.89	56provisional	1.18	32%	21	036% 131% 218% 39% 44% 51% 6+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Fouls won41 playersHighest: Gabriel Gudmundsson, 1.46 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
Gabriel GudmundssonDEF	Leeds United	1.60	83provisional	1.46	42%	31	025% 133% 223% 312% 45% 52% 6+1%
Brenden AaronsonMID	Leeds United	1.59	73provisional	1.29	37%	74	031% 132% 221% 310% 44% 51% 6+0%
Neco WilliamsDEF	Nottingham Forest	1.38	80provisional	1.24	35%	66	032% 133% 221% 39% 43% 51% 6+0%
Morgan Gibbs-WhiteMID	Nottingham Forest	1.20	84provisional	1.12	31%	70	034% 135% 220% 38% 42% 51% 6+0%
Ryan YatesMID	Nottingham Forest	2.11	45provisional	1.05	27%	29	043% 130% 215% 37% 43% 51% 6+1%
Igor Jesus Maciel da CruzFWD	Nottingham Forest	1.49	63provisional	1.03	28%	31	039% 133% 217% 37% 42% 51% 6+0%
Ethan AmpaduMID	Leeds United	1.10	85provisional	1.03	28%	62	037% 135% 218% 37% 42% 50% 6+0%
Dominic Calvert-LewinFWD	Leeds United	1.18	78provisional	1.03	28%	31	039% 134% 218% 37% 42% 51% 6+0%
Jayden BogleDEF	Leeds United	1.06	85provisional	1.01	27%	77	038% 135% 218% 36% 42% 50% 6+0%
Degnand Wilfried GnontoFWD	Leeds United	2.12	43provisional	1.00	26%	32	047% 127% 214% 37% 43% 51% 6+1%

10 of 41 players shown, ranked by projection. Show all 41 →

Cards41 playersHighest: Ethan Ampadu, 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
Ethan AmpaduMID	Leeds United	0.26	85provisional	0.24	21%	62	079% 119% 22% 3+0%
Ibrahim SangaréMID	Nottingham Forest	0.32	65provisional	0.23	20%	32	080% 117% 22% 3+0%
Ryan YatesMID	Nottingham Forest	0.43	45provisional	0.21	18%	29	082% 116% 22% 3+0%
Jayden BogleDEF	Leeds United	0.22	85provisional	0.21	19%	77	081% 117% 22% 3+0%
Anton StachMID	Leeds United	0.21	82provisional	0.20	18%	28	082% 116% 22% 3+0%
Murillo Santiago Costa dos SantosDEF	Nottingham Forest	0.20	87provisional	0.19	17%	61	083% 116% 22% 3+0%
Jaka BijolDEF	Leeds United	0.22	76provisional	0.19	17%	23	083% 115% 22% 3+0%
Gabriel GudmundssonDEF	Leeds United	0.20	83provisional	0.18	16%	31	084% 115% 2+1%
Felipe RodriguesDEF	Nottingham Forest	0.33	48provisional	0.18	16%	26	084% 114% 22% 3+0%
Nicolò SavonaDEF	Nottingham Forest	0.22	73provisional	0.18	16%	12	084% 114% 21% 3+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Saves2 playersHighest: Lucas Estella Perri, 3.00 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
Lucas Estella PerriGK	Leeds United	3.00	90provisional	3.00	55%	16	07% 116% 222% 320% 415% 510% 6+10%
Matz SelsGK	Nottingham Forest	2.84	88provisional	2.78	51%	68	09% 118% 222% 320% 414% 59% 6+8%

1 of 7 markets have no projection for this fixture.