Serie A · Italy

NapolivComo

Sunday 30 August at 17:30Stadio Diego Armando Maradonalineups not announced

Referee

Not appointed

0 matches on record

Fouls per game

—

League average

23.2

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 v20260815-1512 · Tackles v20260815-1512 · Shots v20260815-1512 · Fouls won v20260815-1512 · Cards v20260815-1512 · Saves v20260815-1512

Model projections

Fouls52 playersHighest: Nico Paz, 1.44 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
Nico PazMID	Como	1.63	80provisional	1.44	42%	63	026% 132% 223% 312% 45% 52% 6+1%
Diego CarlosDEF	Como	1.69	72provisional	1.35	39%	24	029% 133% 222% 311% 44% 51% 6+0%
Edoardo GoldanigaDEF	Como	1.57	75provisional	1.31	38%	28	032% 130% 221% 311% 44% 51% 6+1%
Alieu FaderaFWD	Como	1.97	59provisional	1.29	37%	20	035% 128% 219% 311% 45% 52% 6+1%
Juan JesusDEF	Napoli	1.62	71provisional	1.27	36%	32	033% 130% 220% 310% 44% 51% 6+0%
Antonio VergaraMID	Napoli	2.19	51provisional	1.25	36%	8	040% 124% 218% 310% 45% 52% 6+1%
Giovanni Di LorenzoDEF	Napoli	1.25	89provisional	1.23	35%	63	030% 136% 222% 39% 43% 51% 6+0%
Alessandro BuongiornoDEF	Napoli	1.35	81provisional	1.22	34%	50	032% 134% 221% 39% 43% 51% 6+0%
Romelu LukakuFWD	Napoli	1.54	71provisional	1.21	35%	35	034% 132% 220% 39% 43% 51% 6+0%
Rasmus HøjlundFWD	Napoli	1.25	84provisional	1.17	32%	32	032% 136% 221% 38% 43% 51% 6+0%

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

Tackles52 playersHighest: Alieu Fadera, 1.24 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
Alieu FaderaFWD	Como	1.90	59provisional	1.24	34%	20	038% 128% 218% 39% 44% 52% 6+1%
Nico PazMID	Como	1.35	80provisional	1.20	33%	63	034% 133% 219% 39% 43% 51% 6+0%
Giovanni Di LorenzoDEF	Napoli	1.09	89provisional	1.07	29%	63	037% 135% 218% 37% 42% 51% 6+0%
Máximo PerroneMID	Como	1.30	74provisional	1.07	29%	57	038% 133% 218% 37% 43% 51% 6+0%
Ignace Van der BremptDEF	Como	1.72	55provisional	1.04	28%	25	044% 128% 215% 38% 43% 51% 6+1%
Mathías OliveraDEF	Napoli	1.41	66provisional	1.03	28%	46	041% 131% 216% 37% 43% 51% 6+0%
Jacobo RamónDEF	Como	1.03	86provisional	0.98	26%	32	040% 134% 217% 36% 42% 51% 6+0%
Álex ValleDEF	Como	1.20	71provisional	0.95	25%	36	044% 131% 216% 36% 42% 51% 6+0%
Juan JesusDEF	Napoli	1.16	71provisional	0.91	24%	32	045% 131% 215% 36% 42% 51% 6+0%
Maxence CaqueretMID	Como	1.48	54provisional	0.89	23%	38	045% 132% 215% 36% 42% 51% 6+0%

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

Shots52 playersHighest: Nico Paz, 2.34 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
Nico PazMID	Como	2.64	80provisional	2.34	64%	63	014% 122% 223% 318% 412% 56% 6+5%
Scott McTominayMID	Napoli	2.37	86provisional	2.25	63%	65	013% 124% 225% 318% 411% 56% 6+4%
Rasmus HøjlundFWD	Napoli	1.96	84provisional	1.83	53%	32	018% 129% 225% 316% 48% 53% 6+2%
Alisson SantosFWD	Napoli	2.49	60provisional	1.66	47%	11	024% 129% 222% 313% 47% 53% 6+2%
Romelu LukakuFWD	Napoli	1.98	71provisional	1.56	45%	35	027% 128% 222% 313% 46% 52% 6+1%
Kevin De BruyneMID	Napoli	1.96	65provisional	1.42	40%	17	027% 133% 222% 311% 45% 52% 6+1%
Assane DiaoFWD	Como	1.72	73provisional	1.39	40%	31	027% 133% 222% 311% 44% 51% 6+1%
Matteo PolitanoFWD	Napoli	1.74	71provisional	1.38	39%	62	029% 132% 221% 311% 45% 52% 6+1%
Anastasios DouvikasFWD	Como	2.07	56provisional	1.29	36%	37	034% 131% 219% 310% 44% 52% 6+1%
Jesús RodríguezFWD	Como	1.91	56provisional	1.18	32%	26	036% 132% 218% 39% 44% 51% 6+1%

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

Fouls won52 playersHighest: Assane Diao, 2.49 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
Assane DiaoFWD	Como	3.07	73provisional	2.49	68%	31	011% 121% 223% 319% 412% 57% 6+6%
Scott McTominayMID	Napoli	2.19	86provisional	2.08	59%	65	015% 126% 225% 317% 410% 55% 6+3%
Rasmus HøjlundFWD	Napoli	1.86	84provisional	1.73	50%	32	020% 130% 225% 315% 47% 53% 6+1%
Alieu FaderaFWD	Como	2.57	59provisional	1.68	46%	20	029% 125% 220% 313% 48% 54% 6+2%
Antonio VergaraMID	Napoli	2.68	51provisional	1.52	42%	8	036% 122% 218% 312% 47% 53% 6+2%
Martin BaturinaMID	Como	2.26	55provisional	1.38	38%	21	034% 128% 218% 311% 45% 52% 6+1%
Stanislav LobotkaMID	Napoli	1.43	79provisional	1.26	36%	61	031% 134% 221% 310% 43% 51% 6+0%
Giovanni Di LorenzoDEF	Napoli	1.25	89provisional	1.23	35%	63	030% 135% 221% 39% 43% 51% 6+0%
Anastasios DouvikasFWD	Como	1.85	56provisional	1.15	31%	37	037% 131% 218% 38% 43% 51% 6+0%
Jayden AddaiFWD	Como	1.78	57provisional	1.14	31%	10	036% 133% 218% 38% 43% 51% 6+0%

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

Cards52 playersHighest: Edoardo Goldaniga, 0.25 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
Edoardo GoldanigaDEF	Como	0.30	75provisional	0.25	22%	28	078% 119% 23% 3+0%
Jacobo RamónDEF	Como	0.25	86provisional	0.24	21%	32	079% 119% 22% 3+0%
Diego CarlosDEF	Como	0.29	72provisional	0.23	21%	24	079% 118% 22% 3+0%
Juan JesusDEF	Napoli	0.29	71provisional	0.22	20%	32	080% 117% 22% 3+0%
Ivan SmolcicDEF	Como	0.30	62provisional	0.20	18%	29	082% 116% 22% 3+0%
Máximo PerroneMID	Como	0.24	74provisional	0.20	18%	57	082% 116% 22% 3+0%
Jayden AddaiFWD	Como	0.26	57provisional	0.17	15%	10	085% 114% 2+1%
Álex ValleDEF	Como	0.20	71provisional	0.16	14%	36	086% 113% 2+1%
Alieu FaderaFWD	Como	0.23	59provisional	0.15	14%	20	086% 112% 2+1%
Assane DiaoFWD	Como	0.18	73provisional	0.15	14%	31	086% 113% 2+1%
Maxence CaqueretMID	Como	0.24	54provisional	0.15	13%	38	087% 112% 2+1%
Nico PazMID	Como	0.16	80provisional	0.14	13%	63	087% 112% 2+1%
Alessandro BuongiornoDEF	Napoli	0.16	81provisional	0.14	13%	50	087% 112% 2+1%
Giovanni Di LorenzoDEF	Napoli	0.14	89provisional	0.14	13%	63	087% 112% 2+1%
Ignace Van der BremptDEF	Como	0.23	55provisional	0.14	13%	25	087% 112% 2+1%
Jesús RodríguezFWD	Como	0.22	56provisional	0.13	12%	26	088% 111% 2+1%
Marc Oliver KempfDEF	Como	0.16	76provisional	0.13	12%	58	088% 111% 2+1%
Frank AnguissaMID	Napoli	0.15	78provisional	0.13	12%	50	088% 111% 2+1%
Alberto DossenaDEF	Como	0.14	79provisional	0.13	12%	21	088% 111% 2+1%
Sam BeukemaDEF	Napoli	0.16	70provisional	0.13	12%	21	088% 111% 2+1%
Alberto MorenoDEF	Como	0.16	69provisional	0.13	12%	36	088% 111% 2+1%
Amir RrahmaniDEF	Napoli	0.13	88provisional	0.12	12%	59	088% 111% 2+1%
Mathías OliveraDEF	Napoli	0.17	66provisional	0.12	11%	46	089% 111% 2+1%
Lucas Da CunhaMID	Como	0.15	75provisional	0.12	11%	66	089% 111% 2+1%
Antonio VergaraMID	Napoli	0.21	51provisional	0.12	11%	8	089% 110% 2+1%
Sergi RobertoMID	Como	0.26	37provisional	0.11	10%	15	090% 19% 2+1%
Scott McTominayMID	Napoli	0.11	86provisional	0.11	10%	65	090% 110% 2+1%
Romelu LukakuFWD	Napoli	0.13	71provisional	0.10	10%	35	090% 19% 2+1%
David NeresFWD	Napoli	0.18	50provisional	0.10	9%	25	091% 19% 2+1%
Stefan PoschDEF	Como	0.31	27provisional	0.09	9%	5	091% 18% 2+1%
Stanislav LobotkaMID	Napoli	0.11	79provisional	0.09	9%	61	091% 18% 2+0%
Álvaro MorataFWD	Como	0.23	36provisional	0.09	9%	14	091% 18% 2+1%
Luca MazzitelliMID	Como	0.24	33provisional	0.09	8%	3	092% 18% 2+1%
Billy GilmourMID	Napoli	0.16	46provisional	0.08	8%	26	092% 17% 2+0%
Alisson SantosFWD	Napoli	0.12	60provisional	0.08	8%	11	092% 18% 2+0%
Matteo PolitanoFWD	Napoli	0.10	71provisional	0.08	8%	62	092% 17% 2+0%
Rasmus HøjlundFWD	Napoli	0.09	84provisional	0.08	8%	32	092% 17% 2+0%
Eljif ElmasMID	Napoli	0.13	54provisional	0.08	7%	23	093% 17% 2+0%
Lorenzo LuccaFWD	Napoli	0.26	25provisional	0.07	7%	4	093% 16% 2+1%
Anastasios DouvikasFWD	Como	0.11	56provisional	0.07	7%	37	093% 16% 2+0%
Pasquale MazzocchiDEF	Napoli	0.21	28provisional	0.06	6%	10	094% 16% 2+0%
Kevin De BruyneMID	Napoli	0.09	65provisional	0.06	6%	17	094% 16% 2+0%
Noa LangFWD	Napoli	0.15	37provisional	0.06	6%	9	094% 16% 2+0%
Leonardo SpinazzolaDEF	Napoli	0.09	58provisional	0.06	6%	43	094% 15% 2+0%
Alex MeretGK	Napoli	0.05	89provisional	0.05	5%	45	095% 15% 2+0%
Martin BaturinaMID	Como	0.08	55provisional	0.05	5%	21	095% 15% 2+0%
GiovaneFWD	Napoli	0.16	28provisional	0.05	5%	3	095% 15% 2+0%
Nicolas KühnFWD	Como	0.14	29provisional	0.05	4%	9	096% 14% 2+0%
Vanja Milinković-SavićGK	Napoli	0.04	90provisional	0.04	4%	27	096% 1+4%
Cyril NgongeFWD	Napoli	0.31	12provisional	0.04	4%	1	096% 14% 2+0%
Alessandro GabrielloniFWD	Como	0.28	11provisional	0.03	3%	1	097% 1+3%
Jean ButezGK	Como	0.02	89provisional	0.02	2%	57	098% 1+2%

All 52 players shown, ranked by projection. Show fewer ↥

Saves3 playersHighest: Jean Butez, 2.82 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
Jean ButezGK	Como	2.85	89provisional	2.82	52%	57	08% 118% 222% 320% 414% 59% 6+9%
Vanja Milinković-SavićGK	Napoli	2.30	90provisional	2.30	40%	27	012% 124% 225% 319% 411% 56% 6+4%
Alex MeretGK	Napoli	2.12	89provisional	2.09	34%	45	014% 126% 225% 317% 410% 55% 6+3%

1 of 7 markets have no projection for this fixture.