Serie A · Italy

MonzavUdinese

Saturday 29 August at 17:30U-Power Stadiumlineups 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

Fouls45 playersHighest: Nicolò Zaniolo, 1.98 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
Nicolò ZanioloFWD	Udinese	2.51	71provisional	1.98	57%	29	017% 126% 224% 317% 49% 54% 6+2%
Armando IzzoDEF	Monza	1.98	82provisional	1.80	53%	28	020% 128% 225% 316% 48% 53% 6+1%
Sandi LovricMID	Udinese	2.33	59provisional	1.53	44%	35	026% 130% 222% 313% 46% 52% 6+1%
Warren BondoMID	Monzanow at AC Milan	1.57	81provisional	1.41	41%	19	026% 132% 223% 312% 44% 51% 6+0%
Keinan DavisFWD	Udinese	2.09	59provisional	1.38	39%	40	031% 130% 220% 311% 45% 52% 6+1%
Jean-Daniel Akpa AkproMID	Monza	1.73	69provisional	1.33	38%	12	030% 132% 222% 311% 44% 51% 6+0%
Christian KabaseleDEF	Udinese	1.56	74provisional	1.29	37%	42	029% 134% 222% 310% 44% 51% 6+0%
Jesper KarlströmMID	Udinese	1.33	87provisional	1.28	37%	73	028% 135% 222% 310% 43% 51% 6+0%
Pablo Marí VillarDEF	Monza	1.30	86provisional	1.24	35%	19	029% 136% 222% 39% 43% 51% 6+0%
Thomas KristensenDEF	Udinese	1.35	82provisional	1.22	35%	49	031% 134% 221% 39% 43% 51% 6+0%

10 of 45 players shown, ranked by projection. Show all 45 → 2 of the 45 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.

Tackles45 playersHighest: Andrea Carboni, 2.09 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	2.23	84provisional	2.09	59%	26	016% 126% 224% 317% 410% 55% 6+3%
Oumar Solet BomawokoDEF	Udinese	1.96	86provisional	1.87	53%	52	019% 128% 224% 315% 48% 54% 6+2%
Jean-Daniel Akpa AkproMID	Monza	2.42	69provisional	1.86	52%	12	022% 126% 222% 315% 48% 54% 6+3%
Warren BondoMID	Monzanow at AC Milan	1.77	81provisional	1.59	46%	19	025% 130% 223% 313% 46% 52% 6+1%
Jesper KarlströmMID	Udinese	1.62	87provisional	1.57	45%	73	024% 131% 223% 313% 46% 52% 6+1%
Pedro Miguel Almeida Lopes PereiraDEF	Monza	1.81	77provisional	1.55	44%	32	026% 129% 222% 312% 46% 52% 6+1%
Alessandro BiancoMID	Monza	1.77	77provisional	1.51	42%	32	027% 131% 222% 312% 46% 52% 6+1%
Matteo PessinaMID	Monza	1.66	78provisional	1.44	41%	11	027% 132% 222% 311% 45% 52% 6+1%
Kingsley EhizibueDEF	Udinese	1.93	66provisional	1.42	40%	52	032% 128% 219% 311% 45% 52% 6+1%
Pablo Marí VillarDEF	Monza	1.46	86provisional	1.39	39%	19	028% 133% 222% 311% 44% 52% 6+1%

Shots45 playersHighest: Nicolò Zaniolo, 2.17 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
Nicolò ZanioloFWD	Udinese	2.75	71provisional	2.17	60%	29	016% 124% 223% 317% 410% 55% 6+4%
Daniel MaldiniMID	Monzanow at Cagliari	2.94	66provisional	2.14	59%	17	018% 124% 222% 317% 410% 55% 6+4%
Milan ĐurićFWD	Monza	2.31	71provisional	1.81	51%	17	021% 128% 223% 315% 48% 54% 6+2%
Dany Mota CarvalhoFWD	Monza	2.30	68provisional	1.74	49%	27	022% 129% 223% 314% 47% 53% 6+2%
Keita Baldé DiaoFWD	Monza	2.54	59provisional	1.67	47%	7	026% 127% 221% 313% 47% 53% 6+2%
Keinan DavisFWD	Udinese	2.27	59provisional	1.50	42%	40	030% 128% 220% 312% 46% 53% 6+1%
Gianluca CaprariFWD	Monza	2.42	49provisional	1.31	35%	20	036% 129% 217% 310% 45% 52% 6+1%
Sandi LovricMID	Udinese	1.70	59provisional	1.12	31%	35	037% 132% 218% 38% 43% 51% 6+0%
Jakub PiotrowskiMID	Udinese	1.84	52provisional	1.05	28%	22	040% 131% 217% 37% 43% 51% 6+0%
Alessandro BiancoMID	Monza	1.11	77provisional	0.94	25%	32	041% 134% 217% 36% 42% 50% 6+0%

Fouls won45 playersHighest: Nicolò Zaniolo, 2.02 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
Nicolò ZanioloFWD	Udinese	2.56	71provisional	2.02	57%	29	018% 125% 223% 317% 49% 55% 6+3%
Daniel MaldiniMID	Monzanow at Cagliari	2.66	66provisional	1.94	54%	17	020% 126% 222% 316% 49% 54% 6+3%
Warren BondoMID	Monzanow at AC Milan	2.00	81provisional	1.80	52%	19	020% 128% 224% 315% 48% 53% 6+2%
Keita Baldé DiaoFWD	Monza	2.67	59provisional	1.76	49%	7	025% 127% 221% 314% 48% 54% 6+2%
Alessandro BiancoMID	Monza	2.02	77provisional	1.72	49%	32	022% 129% 223% 314% 47% 53% 6+2%
Armando IzzoDEF	Monza	1.68	82provisional	1.52	44%	28	025% 131% 223% 313% 45% 52% 6+1%
Gianluca CaprariFWD	Monza	2.53	49provisional	1.37	37%	20	035% 128% 218% 310% 45% 52% 6+1%
Jean-Daniel Akpa AkproMID	Monza	1.76	69provisional	1.36	39%	12	030% 131% 221% 311% 44% 52% 6+1%
Dany Mota CarvalhoFWD	Monza	1.74	68provisional	1.31	37%	27	031% 132% 221% 310% 44% 51% 6+1%
Matteo PessinaMID	Monza	1.48	78provisional	1.28	36%	11	030% 134% 221% 310% 44% 51% 6+0%

Cards45 playersHighest: Nicolò Zaniolo, 0.26 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
Nicolò ZanioloFWD	Udinese	0.33	71provisional	0.26	23%	29	077% 120% 23% 3+0%
Armando IzzoDEF	Monza	0.28	82provisional	0.25	22%	28	078% 119% 23% 3+0%
Pedro Miguel Almeida Lopes PereiraDEF	Monza	0.25	77provisional	0.21	19%	32	081% 117% 22% 3+0%
Jesper KarlströmMID	Udinese	0.22	87provisional	0.21	19%	73	081% 117% 22% 3+0%
Luca CaldirolaDEF	Monza	0.28	63provisional	0.19	17%	12	083% 115% 22% 3+0%
Jean-Daniel Akpa AkproMID	Monza	0.24	69provisional	0.19	17%	12	083% 115% 22% 3+0%
Thomas KristensenDEF	Udinese	0.20	82provisional	0.18	17%	49	083% 115% 21% 3+0%
Sandi LovricMID	Udinese	0.28	59provisional	0.18	17%	35	083% 115% 22% 3+0%
Alessandro BiancoMID	Monza	0.21	77provisional	0.18	16%	32	084% 115% 21% 3+0%
Christian KabaseleDEF	Udinese	0.22	74provisional	0.18	16%	42	084% 115% 2+1%

Saves3 playersHighest: Razvan Sava, 2.87 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
Razvan SavaGK	Udinese	2.88	89provisional	2.87	53%	19	08% 118% 222% 320% 415% 59% 6+9%
Maduka OkoyeGK	Udinese	2.91	88provisional	2.86	53%	54	08% 118% 222% 320% 415% 59% 6+9%
Stefano TuratiGK	Monza	2.61	89provisional	2.59	47%	30	09% 120% 224% 320% 413% 57% 6+6%

1 of 7 markets have no projection for this fixture.