Serie A · Italy

GenoavNapoli

Saturday 22 August at 19:45Stadio Comunale Luigi Ferrarisexpected lineup

Referee

Marco Di Bello

28 matches on record

Fouls per game

26.4

+14% vs league

League average

23.2

fouls per match, both teams

Angles

every priced line builder

formRafa Marín over 0.5 tackles: 8/10 of his last 10, 6 in a row
Last 20: 15/20 · 5/6 away · strip 3 1 2 3 1 2 0 1 0 3 · market 80% · best 1.40 (Bet365) · bet365 1.40
formAlessandro Marcandalli over 1.5 tackles: 5/10 of his last 10, 5 in a row
Last 20: 7/20 · 2/4 at home · strip 2 3 2 3 3 1 0 1 1 0 · market 50% · best 2.00 (BetVictor) · bet365 1.33
opponentGenoa concede 15.2 fouls a game — 2nd most of 23 in Serie A
Over their last 10. Good news for Napoli's foul-drawers.
opponentNapoli concede 9.8 fouls a game — 22nd most of 23 in Serie A
Over their last 10. A hard place to get fouls.
opponentNapoli concede 3.0 shots on target a game — 21st most of 23 in Serie A
Over their last 10. A hard place to get shots on target.
opponentNapoli concede 9.0 shots a game — 22nd most of 23 in Serie A
Over their last 10. A hard place to get shots.
refereeMarco Di Bello: 26.6 fouls and 2.5 cards a game (league 25.3 / 3.5)
+1.2 fouls and -1.1 cards against his league's average over his last 20.

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Value — 0 live calls · 1 withdrawn

Johan Vásquezover 1.50 Shots10.00 at Coral · fair 7.94+25.9%withdrawn

A withdrawn call is one Per90 no longer stands behind — the rule that published it was later found wrong. Hover it for the reason. Withdrawn calls are not deleted — each keeps its published price and timestamp, is still scored against its closing line, and is still counted in every figure on the record. Why a withdrawal is not a deletion → Published at the price shown and scored against the closing line whatever happens next — the same rows, with staking context, are on the value board and in the track record.

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

Fouls58 playersHighest: Alessandro Buongiorno, 1.45 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
Alessandro BuongiornoDEF	Napoli	1.55	83provisional	1.45	42%	55	025% 133% 224% 312% 45% 51% 6+0%
Walid CheddiraFWD	Napoli	2.00	49provisional	1.25	35%	56	033% 132% 219% 310% 44% 51% 6+1%
Juan JesusDEF	Napoli	1.66	62provisional	1.24	35%	39	034% 132% 220% 310% 44% 51% 6+0%
Morten ThorsbyMID	Genoa	1.53	68provisional	1.23	35%	54	032% 134% 221% 39% 43% 51% 6+0%
Giovanni Di LorenzoDEF	Napoli	1.25	88provisional	1.23	35%	63	030% 136% 222% 39% 43% 51% 6+0%
Antonio VergaraMID	Napoli	1.93	51provisional	1.23	34%	12	035% 131% 219% 39% 44% 51% 6+1%
Lorenzo LuccaFWD	Napoli	2.16	40provisional	1.16	31%	53	037% 132% 217% 38% 43% 51% 6+1%
Djibril SowMID	—	1.36	72provisional	1.15	32%	63	033% 135% 220% 38% 43% 51% 6+0%
Frank AnguissaMID	Napoli	1.41	68provisional	1.13	31%	53	035% 134% 219% 38% 43% 51% 6+0%
Leo ØstigårdDEF	Genoa	1.15	86provisional	1.11	30%	41	033% 136% 220% 38% 42% 51% 6+0%

10 of 58 players shown, ranked by projection. Show all 58 →

Shots58 playersHighest: Scott McTominay, 2.32 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
Scott McTominayMID	Napoli	2.45	85provisional	2.32	65%	69	012% 123% 224% 319% 412% 56% 6+4%
VitinhaFWD	Genoa	2.26	66provisional	1.74	50%	60	022% 129% 223% 314% 47% 53% 6+2%
Alisson SantosFWD	Napoli	2.23	66provisional	1.72	49%	14	022% 129% 223% 314% 47% 53% 6+2%
Lorenzo ColomboFWD	Genoa	2.14	66provisional	1.66	48%	75	022% 130% 224% 314% 46% 53% 6+1%
Rasmus HøjlundFWD	Napoli	1.75	77provisional	1.53	44%	65	024% 132% 223% 313% 45% 52% 6+1%
GiovaneFWD	Napoli	2.21	54provisional	1.43	40%	33	030% 130% 220% 311% 45% 52% 6+1%
Matteo PolitanoFWD	Napoli	1.66	74provisional	1.42	41%	71	027% 132% 223% 311% 45% 52% 6+1%
Ruslan MalinovskyiMID	Genoa	1.85	56provisional	1.25	35%	43	033% 132% 220% 39% 44% 51% 6+1%
Walid CheddiraFWD	Napoli	2.08	49provisional	1.25	34%	56	035% 130% 218% 39% 44% 52% 6+1%
Lorenzo LuccaFWD	Napoli	2.46	40provisional	1.24	33%	53	037% 130% 217% 39% 44% 52% 6+1%

10 of 58 players shown, ranked by projection. Show all 58 →

Shots on target54 playersHighest: Scott McTominay, 0.84 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
Scott McTominayMID	Napoli	0.89	85provisional	0.84	56%	69	044% 135% 215% 35% 41% 5+0%
Alisson SantosFWD	Napoli	1.03	66provisional	0.80	53%	14	047% 133% 214% 34% 41% 5+0%
Lorenzo ColomboFWD	Genoa	0.84	66provisional	0.65	47%	75	053% 132% 211% 33% 41% 5+0%
Rasmus HøjlundFWD	Napoli	0.71	77provisional	0.62	45%	65	055% 132% 210% 32% 4+1%
VitinhaFWD	Genoa	0.66	66provisional	0.51	39%	60	061% 129% 28% 32% 4+0%
GiovaneFWD	Napoli	0.75	54provisional	0.48	36%	33	064% 127% 27% 32% 4+0%
Walid CheddiraFWD	Napoli	0.76	49provisional	0.46	35%	56	065% 126% 27% 31% 4+0%
Lorenzo LuccaFWD	Napoli	0.88	40provisional	0.44	33%	53	067% 125% 27% 32% 4+0%
Kevin De BruyneMID	Napoli	0.72	46provisional	0.41	32%	46	068% 125% 26% 31% 4+0%
Albert GrønbækMID	Genoa	0.57	61provisional	0.41	32%	15	068% 125% 26% 31% 4+0%

10 of 54 players shown, ranked by projection. Show all 54 →

Fouls won58 playersHighest: Scott McTominay, 1.86 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
Scott McTominayMID	Napoli	1.97	85provisional	1.86	54%	69	018% 128% 225% 316% 48% 53% 6+2%
VitinhaFWD	Genoa	2.04	66provisional	1.55	45%	60	025% 130% 223% 313% 46% 52% 6+1%
AmorimMID	Genoa	1.96	61provisional	1.40	40%	11	030% 131% 221% 311% 45% 52% 6+1%
Walid CheddiraFWD	Napoli	2.29	49provisional	1.35	37%	56	033% 130% 219% 310% 45% 52% 6+1%
Lorenzo ColomboFWD	Genoa	1.74	66provisional	1.33	38%	75	029% 133% 222% 310% 44% 51% 6+0%
Antonio VergaraMID	Napoli	2.15	51provisional	1.29	36%	12	036% 129% 218% 310% 45% 52% 6+1%
Michael FolorunshoMID	Napoli	2.04	54provisional	1.29	35%	47	036% 129% 218% 310% 45% 52% 6+1%
Rasmus HøjlundFWD	Napoli	1.43	77provisional	1.25	35%	65	031% 134% 221% 39% 43% 51% 6+0%
Stanislav LobotkaMID	Napoli	1.30	80provisional	1.18	33%	64	032% 135% 221% 39% 43% 51% 6+0%
Mikael Egill EllertssonMID	Genoa	1.30	77provisional	1.13	31%	72	034% 134% 220% 38% 43% 51% 6+0%

10 of 58 players shown, ranked by projection. Show all 58 →

Saves4 playersHighest: Vanja Milinković-Savić, 2.88 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
Vanja Milinković-SavićGK	Napoli	2.90	89provisional	2.88	53%	64	07% 117% 222% 320% 415% 59% 6+9%
Justin BijlowGK	Genoa	2.63	85provisional	2.52	45%	16	010% 121% 224% 319% 413% 57% 6+6%
Alex MeretGK	Napoli	2.44	88provisional	2.39	42%	45	011% 122% 224% 319% 412% 56% 6+5%
Nicola LealiGK	Genoa	2.71	74provisional	2.30	40%	51	015% 123% 222% 317% 411% 56% 6+5%

2 of 7 markets have no projection for this fixture.