Serie A · Italy

IntervMonza

Saturday 22 August at 17:30Stadio Giuseppe Meazzaexpected lineup

Referee

Livio Marinelli

25 matches on record

Fouls per game

25.2

+9% vs league

League average

23.2

fouls per match, both teams

Angles

every priced line builder

opponentMonza concede 15.7 fouls a game — 1st most of 23 in Serie A
Over their last 10. Good news for Inter's foul-drawers.
opponentMonza concede 5.1 shots on target a game — 1st most of 23 in Serie A
Over their last 10. Good news for Inter's shooters.
formMarcus Thuram over 0.5 tackles: 8/10 of his last 10
Last 20: 13/20 · 4/5 at home · strip 0 1 1 1 1 0 1 1 1 2 · market 74% · best 1.36 (Betano) · bet365 1.30
opponentMonza concede 12.8 tackles a game — 22nd most of 23 in Serie A
Over their last 10. A hard place to get tackles.
opponentInter concede 12.9 tackles a game — 21st most of 23 in Serie A
Over their last 10. A hard place to get tackles.

Facts with sample sizes, not predictions. The market % beside a line is the books’ median price with margin in, and the over market runs hot.

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 v20260816-1555 · Shots v20260816-1555 · Shots on target v20260816-1555 · Fouls won v20260816-1555 · Saves v20260816-1229

Model projections

Fouls56 playersHighest: Lautaro Martínez, 1.57 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
Lautaro MartínezFWD	Inter	1.72	79provisional	1.57	46%	61	022% 132% 225% 313% 45% 52% 6+1%
Alessandro BastoniDEF	Inter	1.64	77provisional	1.47	43%	61	024% 133% 224% 312% 45% 51% 6+0%
Marcus ThuramFWD	Inter	1.67	73provisional	1.42	41%	61	026% 133% 223% 312% 44% 51% 6+0%
Armando IzzoDEF	Monza	1.62	69provisional	1.31	37%	30	031% 132% 221% 310% 44% 51% 6+0%
Roberto GagliardiniMID	Monza	1.52	70provisional	1.26	36%	37	030% 134% 221% 310% 43% 51% 6+0%
Hakan ÇalhanoğluMID	Inter	1.46	73provisional	1.25	36%	51	030% 135% 222% 39% 43% 51% 6+0%
Pedro PereiraDEF	Monza	1.37	79provisional	1.24	35%	35	030% 135% 222% 39% 43% 51% 6+0%
Jean-Daniel Akpa AkproMID	Monza	1.64	62provisional	1.23	35%	36	032% 134% 221% 39% 43% 51% 6+0%
Warren BondoMID	Monzanow at AC Milan	1.56	62provisional	1.17	33%	51	034% 134% 220% 39% 43% 51% 6+0%
Ebenezer AkinsanmiroMID	—	1.41	66provisional	1.12	31%	24	034% 135% 220% 38% 42% 51% 6+0%

10 of 56 players shown, ranked by projection. Show all 56 → 2 of the 56 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.

Shots56 playersHighest: Lautaro Martínez, 3.71 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 MartínezFWD	Inter	4.13	79provisional	3.71	85%	61	04% 111% 217% 319% 417% 513% 6+19%
Marcus ThuramFWD	Inter	3.66	73provisional	3.07	77%	61	08% 116% 220% 319% 415% 510% 6+12%
Hakan ÇalhanoğluMID	Inter	2.66	73provisional	2.24	63%	51	013% 124% 225% 318% 411% 55% 6+4%
Pio EspositoFWD	Inter	3.49	46provisional	1.99	52%	35	021% 127% 221% 314% 48% 55% 6+5%
Federico DimarcoDEF	Inter	2.42	64provisional	1.81	51%	68	021% 128% 223% 315% 48% 54% 6+2%
Henrikh MkhitaryanMID	Inter	2.15	59provisional	1.50	42%	62	028% 130% 221% 312% 46% 52% 6+1%
Ange-Yoan BonnyFWD	Inter	2.31	51provisional	1.43	40%	70	029% 131% 220% 311% 45% 52% 6+1%
Kristjan AsllaniMID	Inter	1.82	67provisional	1.41	40%	37	029% 131% 221% 311% 45% 52% 6+1%
Nicolò BarellaMID	Inter	1.61	76provisional	1.41	40%	67	026% 133% 223% 311% 44% 51% 6+1%
Patrick CutroneFWD	—	1.81	57provisional	1.22	34%	48	035% 132% 219% 39% 44% 51% 6+1%

Shots on target52 playersHighest: Lautaro Martínez, 1.39 expectedshow

What drives it — Shot volume first, conversion second — literally: the projection is the shots model thinned by the player’s own on-target rate, so a high-volume low-accuracy shooter and a sniper price differently at the same shot count. See what has actually happened.

Player	Team	Model /90	Exp. mins	Projected	Over 0.5	n	Distribution
Lautaro MartínezFWD	Inter	1.54	79provisional	1.39	73%	61	027% 133% 223% 311% 44% 51% 6+1%
Marcus ThuramFWD	Inter	1.44	73provisional	1.21	68%	61	032% 134% 220% 39% 43% 51% 6+0%
Pio EspositoFWD	Inter	1.35	46provisional	0.77	50%	35	050% 131% 213% 34% 41% 50% 6+0%
Hakan ÇalhanoğluMID	Inter	0.85	73provisional	0.72	50%	51	050% 134% 212% 33% 41% 5+0%
Federico DimarcoDEF	Inter	0.85	64provisional	0.64	45%	68	055% 131% 211% 33% 41% 5+0%
Ange-Yoan BonnyFWD	Inter	0.91	51provisional	0.57	41%	70	059% 129% 29% 32% 40% 5+0%
Patrick CutroneFWD	—	0.66	57provisional	0.45	34%	48	066% 126% 27% 31% 4+0%
Keita BaldéFWD	Monza	0.56	67provisional	0.43	34%	11	066% 127% 26% 31% 4+0%
Kristjan AsllaniMID	Inter	0.53	67provisional	0.41	33%	37	067% 126% 26% 31% 4+0%
Gianluca CaprariFWD	Monza	0.56	60provisional	0.39	31%	34	069% 125% 26% 31% 4+0%

10 of 52 players shown, ranked by projection. Show all 52 → 2 of the 52 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.

Fouls won56 playersHighest: Keita Baldé, 1.79 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
Keita BaldéFWD	Monza	2.32	67provisional	1.79	51%	11	021% 128% 223% 315% 48% 53% 6+2%
Lautaro MartínezFWD	Inter	1.76	79provisional	1.57	46%	61	022% 132% 224% 313% 46% 52% 6+1%
Marcus ThuramFWD	Inter	1.75	73provisional	1.46	42%	61	026% 132% 223% 312% 45% 52% 6+1%
Alessandro BiancoMID	Monza	1.63	78provisional	1.44	41%	35	026% 132% 223% 312% 45% 52% 6+1%
Pio EspositoFWD	Inter	2.39	46provisional	1.34	36%	35	033% 131% 219% 310% 45% 52% 6+1%
Gianluca CaprariFWD	Monza	1.87	60provisional	1.31	37%	34	032% 131% 220% 310% 44% 52% 6+1%
Armando IzzoDEF	Monza	1.49	69provisional	1.17	33%	30	036% 132% 219% 39% 43% 51% 6+0%
Ebenezer AkinsanmiroMID	—	1.50	66provisional	1.15	32%	24	034% 134% 219% 38% 43% 51% 6+0%
Warren BondoMID	Monzanow at AC Milan	1.57	62provisional	1.13	31%	51	036% 133% 219% 38% 43% 51% 6+0%
Roberto GagliardiniMID	Monza	1.36	70provisional	1.10	30%	37	036% 134% 219% 38% 43% 51% 6+0%

Saves4 playersHighest: Stefano Turati, 3.48 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	3.55	88provisional	3.48	64%	36	05% 113% 219% 320% 417% 512% 6+16%
Ivan ProvedelGK	—	2.34	89provisional	2.32	40%	56	012% 123% 225% 319% 411% 56% 6+4%
Josep MartínezGK	Inter	2.13	86provisional	2.05	34%	10	015% 126% 225% 317% 49% 54% 6+3%
Yann SommerGK	Inter	1.97	89provisional	1.96	31%	66	016% 127% 225% 316% 49% 54% 6+2%

2 of 7 markets have no projection for this fixture.