Serie A · Italy

AC MilanvVenezia

Friday 28 August at 19:45Stadio Giuseppe Meazzalineups 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

Fouls48 playersHighest: Alessio Zerbin, 1.66 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
Alessio ZerbinMID	Venezianow at Frosinone	1.77	84provisional	1.66	49%	17	021% 130% 225% 314% 46% 52% 6+1%
Enrique Pérez MuñozMID	Venezia	2.11	70provisional	1.64	48%	13	023% 129% 223% 314% 47% 53% 6+1%
Hans Nicolussi CavigliaMID	Venezianow at Parma	1.52	82provisional	1.39	40%	33	027% 133% 223% 311% 44% 51% 6+0%
Jay IdzesDEF	Venezia	1.38	89provisional	1.37	39%	35	026% 135% 223% 311% 44% 51% 6+0%
Gaetano Pio OristanioMID	Venezianow at Torino	1.91	63provisional	1.34	38%	32	029% 133% 222% 311% 44% 51% 6+0%
Fali CandéDEF	Venezia	1.32	90provisional	1.32	38%	17	027% 135% 223% 310% 43% 51% 6+0%
Joseph Alfred DuncanMID	Venezia	1.91	60provisional	1.27	36%	12	035% 128% 220% 310% 44% 51% 6+1%
Yunus MusahMID	AC Milan	2.05	56provisional	1.27	36%	21	034% 131% 220% 310% 44% 51% 6+1%
Adrien RabiotMID	AC Milan	1.29	88provisional	1.26	36%	29	029% 135% 222% 39% 43% 51% 6+0%
D. FilaFWD	Venezia	2.11	50provisional	1.16	32%	7	037% 131% 218% 39% 43% 51% 6+0%

10 of 48 players shown, ranked by projection. Show all 48 → 5 of the 48 have since left the club — the squad is built from who has played for these teams, not from who is registered today, so they are shown rather than quietly dropped.

Tackles48 playersHighest: Alessio Zerbin, 2.25 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
Alessio ZerbinMID	Venezianow at Frosinone	2.40	84provisional	2.25	61%	17	016% 123% 223% 317% 411% 56% 6+5%
Enrique Pérez MuñozMID	Venezia	2.72	70provisional	2.12	57%	13	019% 124% 222% 316% 410% 55% 6+5%
Alexis SaelemaekersMID	AC Milan	2.23	80provisional	1.99	56%	35	018% 127% 224% 316% 49% 54% 6+3%
Adrien RabiotMID	AC Milan	2.03	88provisional	1.98	56%	29	017% 127% 224% 316% 49% 54% 6+3%
Fikayo TomoriDEF	AC Milan	2.28	76provisional	1.93	54%	48	021% 125% 222% 315% 49% 54% 6+3%
Strahinja PavlovićDEF	AC Milan	2.05	82provisional	1.86	53%	54	021% 127% 223% 315% 48% 54% 6+3%
Antonio CandelaDEF	Venezia	2.28	72provisional	1.83	51%	13	022% 127% 222% 314% 48% 54% 6+3%
Davide BartesaghiDEF	AC Milan	2.02	73provisional	1.64	46%	32	026% 128% 221% 313% 47% 53% 6+2%
Mikael Egill EllertssonMID	Venezia	2.07	69provisional	1.59	44%	30	027% 129% 221% 313% 46% 53% 6+2%
Youssouf FofanaMID	AC Milan	2.05	70provisional	1.58	45%	61	026% 130% 222% 313% 46% 53% 6+2%

Shots48 playersHighest: Rafael Alexandre Conceição Leão, 2.44 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
Rafael Alexandre Conceição LeãoFWD	AC Milan	3.29	67provisional	2.44	65%	54	013% 122% 222% 318% 412% 57% 6+6%
Santiago Tomás GiménezFWD	AC Milan	3.66	52provisional	2.12	56%	21	019% 125% 221% 315% 410% 55% 6+5%
Christian PulisicFWD	AC Milan	2.97	64provisional	2.12	57%	50	019% 124% 221% 316% 410% 56% 6+5%
Adrien RabiotMID	AC Milan	1.81	88provisional	1.77	51%	29	019% 129% 225% 315% 47% 53% 6+1%
Joel PohjanpaloFWD	Venezia	1.95	81provisional	1.75	51%	19	020% 129% 224% 315% 47% 53% 6+1%
Youssouf FofanaMID	AC Milan	1.88	70provisional	1.45	42%	61	027% 131% 222% 312% 45% 52% 6+1%
Samuel Chimerenka ChukwuezeFWD	AC Milan	3.66	35provisional	1.43	35%	12	037% 128% 215% 39% 45% 53% 6+3%
Christopher NkunkuFWD	AC Milan	3.00	42provisional	1.41	37%	17	036% 127% 216% 310% 46% 53% 6+2%
Alexis SaelemaekersMID	AC Milan	1.49	80provisional	1.33	38%	35	028% 134% 222% 310% 44% 51% 6+0%
Hans Nicolussi CavigliaMID	Venezianow at Parma	1.26	82provisional	1.15	32%	33	034% 135% 220% 38% 43% 51% 6+0%

Fouls won48 playersHighest: Adrien Rabiot, 1.90 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
Adrien RabiotMID	AC Milan	1.95	88provisional	1.90	55%	29	017% 128% 225% 316% 48% 54% 6+2%
Alexis SaelemaekersMID	AC Milan	2.05	80provisional	1.83	53%	35	018% 129% 225% 316% 48% 53% 6+2%
Gaetano Pio OristanioMID	Venezianow at Torino	2.54	63provisional	1.78	51%	32	021% 128% 223% 315% 48% 53% 6+2%
Rafael Alexandre Conceição LeãoFWD	AC Milan	2.11	67provisional	1.56	45%	54	025% 130% 222% 313% 46% 52% 6+1%
Alessio ZerbinMID	Venezianow at Frosinone	1.59	84provisional	1.49	43%	17	025% 131% 223% 312% 45% 52% 6+1%
Gianluca BusioMID	Venezia	1.45	77provisional	1.23	35%	31	032% 133% 220% 39% 43% 51% 6+0%
Mikael Egill EllertssonMID	Venezia	1.46	69provisional	1.12	31%	30	036% 133% 219% 38% 43% 51% 6+0%
Luka ModrićMID	AC Milan	1.17	83provisional	1.08	29%	32	036% 134% 219% 37% 42% 51% 6+0%
Christian PulisicFWD	AC Milan	1.45	64provisional	1.03	28%	50	040% 132% 217% 37% 42% 51% 6+0%
Ruben Loftus-CheekMID	AC Milan	2.01	46provisional	1.03	27%	26	044% 129% 215% 37% 43% 51% 6+1%

Cards48 playersHighest: Enrique Pérez Muñoz, 0.22 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
Enrique Pérez MuñozMID	Venezia	0.28	70provisional	0.22	19%	13	081% 117% 22% 3+0%
Alessio ZerbinMID	Venezianow at Frosinone	0.23	84provisional	0.22	19%	17	081% 117% 22% 3+0%
Fali CandéDEF	Venezia	0.21	90provisional	0.21	19%	17	081% 117% 22% 3+0%
Jay IdzesDEF	Venezia	0.18	89provisional	0.18	17%	35	083% 115% 2+2%
Adrien RabiotMID	AC Milan	0.19	88provisional	0.18	16%	29	084% 115% 2+1%
Hans Nicolussi CavigliaMID	Venezianow at Parma	0.20	82provisional	0.18	16%	33	084% 115% 2+1%
Yunus MusahMID	AC Milan	0.27	56provisional	0.17	15%	21	085% 114% 21% 3+0%
Pervis Josué Estupiñán TenorioDEF	AC Milan	0.26	55provisional	0.16	14%	14	086% 113% 21% 3+0%
Antonio CandelaDEF	Venezia	0.19	72provisional	0.16	14%	13	086% 113% 2+1%
Fikayo TomoriDEF	AC Milan	0.18	76provisional	0.16	14%	48	086% 113% 2+1%

Saves3 playersHighest: Filip Stankovic, 3.37 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
Filip StankovicGK	Venezia	3.55	86provisional	3.37	62%	15	05% 113% 219% 320% 416% 511% 6+15%
Ionuț Andrei RaduGK	Venezianow at Celta de Vigo	3.18	90provisional	3.18	59%	15	06% 115% 220% 320% 416% 511% 6+12%
Mike MaignanGK	AC Milan	2.45	89provisional	2.43	43%	74	011% 122% 224% 319% 412% 56% 6+5%

1 of the 3 have since left the club — the squad is built from who has played for these teams, not from who is registered today, so they are shown rather than quietly dropped.

1 of 7 markets have no projection for this fixture.