Serie A · Italy

IntervMonza

Saturday 22 August at 17:30Stadio 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

Fouls47 playersHighest: Armando Izzo, 1.90 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
Armando IzzoDEF	Monza	2.09	82provisional	1.90	55%	28	018% 126% 225% 317% 49% 54% 6+2%
Alessandro BastoniDEF	Inter	1.79	77provisional	1.53	45%	59	023% 132% 224% 313% 45% 52% 6+1%
Lautaro Javier MartínezFWD	Inter	1.75	78provisional	1.51	44%	58	024% 132% 224% 313% 45% 52% 6+1%
Warren BondoMID	Monzanow at AC Milan	1.65	81provisional	1.49	44%	19	025% 132% 224% 312% 45% 52% 6+1%
Jean-Daniel Akpa AkproMID	Monza	1.83	69provisional	1.41	41%	12	028% 131% 222% 312% 45% 52% 6+1%
Pablo Marí VillarDEF	Monza	1.37	86provisional	1.31	37%	19	028% 135% 223% 310% 43% 51% 6+0%
Pedro Miguel Almeida Lopes PereiraDEF	Monza	1.49	77provisional	1.28	37%	32	030% 133% 222% 310% 44% 51% 6+0%
Alessandro BiancoMID	Monza	1.48	77provisional	1.26	36%	32	031% 134% 221% 310% 43% 51% 6+0%
Marcus ThuramFWD	Inter	1.64	69provisional	1.26	36%	51	032% 133% 221% 310% 44% 51% 6+0%
Luca CaldirolaDEF	Monza	1.73	63provisional	1.20	34%	12	033% 133% 220% 39% 43% 51% 6+0%

10 of 47 players shown, ranked by projection. Show all 47 → 2 of the 47 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.

Tackles47 playersHighest: Andrea Carboni, 1.90 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
Andrea CarboniDEF	Monza	2.02	84provisional	1.90	54%	26	018% 128% 224% 315% 48% 54% 6+2%
Jean-Daniel Akpa AkproMID	Monza	2.20	69provisional	1.69	47%	12	025% 128% 222% 313% 47% 53% 6+2%
Alessandro BastoniDEF	Inter	1.78	77provisional	1.51	43%	59	026% 131% 223% 312% 45% 52% 6+1%
Hakan ÇalhanoğluMID	Inter	1.89	71provisional	1.49	42%	47	026% 131% 222% 312% 45% 52% 6+1%
Nicolò BarellaMID	Inter	1.79	75provisional	1.48	42%	62	027% 131% 222% 312% 45% 52% 6+1%
Warren BondoMID	Monzanow at AC Milan	1.60	81provisional	1.44	41%	19	028% 131% 222% 312% 45% 52% 6+1%
Pedro Miguel Almeida Lopes PereiraDEF	Monza	1.64	77provisional	1.40	40%	32	029% 131% 221% 311% 45% 52% 6+1%
Alessandro BiancoMID	Monza	1.60	77provisional	1.37	38%	32	030% 132% 221% 311% 45% 52% 6+1%
Manuel Obafemi AkanjiDEF	Inter	1.43	85provisional	1.36	38%	32	029% 132% 221% 311% 44% 52% 6+1%
Matteo PessinaMID	Monza	1.51	78provisional	1.30	37%	11	031% 133% 221% 310% 44% 51% 6+1%

Shots47 playersHighest: Lautaro Javier Martínez, 3.73 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
Lautaro Javier MartínezFWD	Inter	4.31	78provisional	3.73	84%	58	05% 111% 216% 318% 417% 513% 6+20%
Marcus ThuramFWD	Inter	3.50	69provisional	2.69	69%	51	012% 119% 221% 318% 413% 58% 6+9%
Francesco Pio EspositoFWD	Inter	4.25	45provisional	2.10	52%	18	022% 126% 219% 313% 48% 56% 6+7%
Hakan ÇalhanoğluMID	Inter	2.57	71provisional	2.04	58%	47	016% 126% 224% 317% 49% 54% 6+3%
Federico DimarcoDEF	Inter	2.51	72provisional	2.00	57%	61	017% 126% 224% 316% 49% 54% 6+3%
Daniel MaldiniMID	Monzanow at Cagliari	2.00	66provisional	1.46	42%	17	028% 130% 222% 312% 45% 52% 6+1%
Nicolò BarellaMID	Inter	1.71	75provisional	1.42	41%	62	027% 132% 222% 311% 45% 52% 6+1%
Henrikh MkhitaryanMID	Inter	1.82	64provisional	1.30	37%	50	032% 131% 220% 310% 44% 52% 6+1%
Milan ĐurićFWD	Monza	1.57	71provisional	1.23	34%	17	033% 133% 220% 39% 44% 51% 6+0%
Dany Mota CarvalhoFWD	Monza	1.57	68provisional	1.19	33%	27	034% 133% 220% 39% 43% 51% 6+0%

Fouls won47 playersHighest: Daniel Maldini, 1.75 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
Daniel MaldiniMID	Monzanow at Cagliari	2.40	66provisional	1.75	50%	17	023% 128% 222% 314% 48% 53% 6+2%
Warren BondoMID	Monzanow at AC Milan	1.80	81provisional	1.63	47%	19	023% 130% 224% 314% 46% 52% 6+1%
Lautaro Javier MartínezFWD	Inter	1.86	78provisional	1.61	47%	58	022% 131% 224% 313% 46% 52% 6+1%
Keita Baldé DiaoFWD	Monza	2.40	59provisional	1.58	44%	7	028% 128% 221% 313% 46% 53% 6+2%
Alessandro BiancoMID	Monza	1.82	77provisional	1.55	45%	32	025% 131% 223% 313% 46% 52% 6+1%
Marcus ThuramFWD	Inter	1.80	69provisional	1.38	39%	51	029% 131% 221% 311% 45% 52% 6+1%
Armando IzzoDEF	Monza	1.52	82provisional	1.37	40%	28	028% 132% 222% 311% 44% 51% 6+1%
Francesco Pio EspositoFWD	Inter	2.70	45provisional	1.34	36%	18	035% 130% 217% 310% 45% 52% 6+1%
Gianluca CaprariFWD	Monza	2.28	49provisional	1.24	33%	20	038% 129% 217% 39% 44% 52% 6+1%
Jean-Daniel Akpa AkproMID	Monza	1.59	69provisional	1.22	34%	12	033% 132% 220% 39% 43% 51% 6+0%

Cards47 playersHighest: Armando Izzo, 0.27 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
Armando IzzoDEF	Monza	0.29	82provisional	0.27	23%	28	077% 120% 23% 3+0%
Pedro Miguel Almeida Lopes PereiraDEF	Monza	0.26	77provisional	0.22	20%	32	080% 118% 22% 3+0%
Alessandro BastoniDEF	Inter	0.25	77provisional	0.21	19%	59	081% 117% 22% 3+0%
Hakan ÇalhanoğluMID	Inter	0.26	71provisional	0.20	18%	47	082% 116% 22% 3+0%
Luca CaldirolaDEF	Monza	0.29	63provisional	0.20	18%	12	082% 116% 22% 3+0%
Jean-Daniel Akpa AkproMID	Monza	0.26	69provisional	0.20	18%	12	082% 116% 22% 3+0%
Alessandro BiancoMID	Monza	0.22	77provisional	0.19	17%	32	083% 116% 22% 3+0%
Warren BondoMID	Monzanow at AC Milan	0.20	81provisional	0.18	17%	19	083% 115% 21% 3+0%
Pablo Marí VillarDEF	Monza	0.19	86provisional	0.18	16%	19	084% 115% 2+1%
Andrea CarboniDEF	Monza	0.18	84provisional	0.17	15%	26	085% 114% 2+1%

Saves3 playersHighest: Stefano Turati, 3.25 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
Stefano TuratiGK	Monza	3.28	89provisional	3.25	60%	30	05% 114% 220% 320% 416% 511% 6+13%
Josep Martínez RieraGK	Inter	2.24	90provisional	2.24	38%	10	013% 124% 225% 318% 411% 55% 6+4%
Yann SommerGK	Inter	2.07	90provisional	2.06	34%	66	015% 126% 225% 317% 49% 54% 6+3%

1 of 7 markets have no projection for this fixture.