Serie A · Italy

FrosinonevJuventus

Sunday 23 August at 17:30Stadio Benito Stirpelineups not announced

Referee

Giovanni Ayroldi

21 matches on record

Fouls per game

28.1

+21% vs league

League average

23.2

fouls per match, both teams

Angles

every priced line builder

refereeGiovanni Ayroldi: 28.3 fouls and 5.2 cards a game (league 25.3 / 3.5)
+3.0 fouls and +1.7 cards against his league's average over his last 20. Leans fouls overs.
opponentJuventus concede 9.9 fouls a game — 21st most of 23 in Serie A
Over their last 10. A hard place to get fouls.
opponentJuventus concede 2.5 shots on target a game — 22nd most of 23 in Serie A
Over their last 10. A hard place to get shots on target.

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

Fouls35 playersHighest: Manuel Locatelli, 1.39 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
Manuel LocatelliMID	Juventus	1.49	82provisional	1.39	40%	72	026% 134% 224% 311% 44% 51% 6+0%
Randal Kolo MuaniFWD	Juventus	1.82	57provisional	1.28	36%	46	031% 133% 221% 310% 44% 51% 6+0%
Weston McKennieMID	Juventus	1.26	79provisional	1.14	32%	68	033% 135% 220% 38% 43% 51% 6+0%
Patrizio MasiniMID	—	1.70	53provisional	1.12	30%	54	038% 132% 218% 38% 43% 51% 6+0%
Alessio ZerbinMID	—	1.55	57provisional	1.08	29%	52	037% 133% 218% 38% 43% 51% 6+0%
Lloyd KellyDEF	Juventus	1.16	80provisional	1.05	29%	57	036% 135% 219% 37% 42% 50% 6+0%
Nico GonzálezFWD	Juventus	1.44	57provisional	1.01	27%	52	039% 134% 217% 37% 42% 51% 6+0%
Francisco ConceiçãoFWD	Juventus	1.25	67provisional	1.01	27%	57	038% 135% 218% 37% 42% 50% 6+0%
Teun KoopmeinersMID	Juventus	1.41	58provisional	1.00	27%	61	040% 134% 217% 37% 42% 51% 6+0%
Zeki ÇelikDEF	—	1.10	79provisional	1.00	26%	65	038% 136% 218% 36% 42% 5+0%

10 of 35 players shown, ranked by projection. Show all 35 →

Shots35 playersHighest: Dušan Vlahović, 2.48 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
Dušan VlahovićFWD	Juventus	3.23	66provisional	2.48	65%	48	014% 121% 221% 317% 412% 57% 6+7%
Kenan YıldızFWD	Juventus	2.24	77provisional	1.96	56%	71	017% 127% 225% 316% 49% 54% 6+2%
Francisco ConceiçãoFWD	Juventus	2.37	67provisional	1.86	53%	57	020% 127% 223% 315% 48% 54% 6+2%
Romano SchmidMID	—	1.59	88provisional	1.55	45%	66	023% 132% 224% 313% 45% 52% 6+1%
Nico GonzálezFWD	Juventus	2.11	57provisional	1.42	40%	52	029% 131% 221% 311% 45% 52% 6+1%
Loïs OpendaFWD	Juventus	2.50	39provisional	1.22	32%	59	039% 130% 216% 38% 44% 52% 6+1%
Jérémie BogaFWD	Juventus	2.11	47provisional	1.21	32%	15	037% 131% 217% 39% 44% 52% 6+1%
Khéphren ThuramMID	Juventus	1.39	61provisional	1.00	27%	70	041% 133% 217% 37% 42% 51% 6+0%
Manuel LocatelliMID	Juventus	1.04	82provisional	0.96	25%	72	039% 136% 217% 36% 42% 5+0%
Edon ZhegrovaFWD	Juventus	3.02	19provisional	0.89	22%	19	042% 136% 216% 35% 41% 5+0%

10 of 35 players shown, ranked by projection. Show all 35 →

Shots on target32 playersHighest: Dušan Vlahović, 0.94 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
Dušan VlahovićFWD	Juventus	1.23	66provisional	0.94	58%	48	042% 133% 216% 36% 42% 50% 6+0%
Kenan YıldızFWD	Juventus	0.89	77provisional	0.78	53%	71	047% 134% 214% 34% 41% 5+0%
Francisco ConceiçãoFWD	Juventus	0.78	67provisional	0.61	44%	57	056% 131% 210% 32% 4+1%
Nico GonzálezFWD	Juventus	0.89	57provisional	0.60	43%	52	057% 130% 210% 33% 41% 5+0%
Romano SchmidMID	—	0.56	88provisional	0.55	42%	66	058% 131% 29% 32% 4+0%
Jérémie BogaFWD	Juventus	0.92	47provisional	0.53	38%	15	062% 127% 28% 32% 40% 5+0%
Loïs OpendaFWD	Juventus	0.82	39provisional	0.40	30%	59	070% 123% 26% 31% 4+0%
Randal Kolo MuaniFWD	Juventus	0.55	57provisional	0.37	30%	46	070% 124% 25% 31% 4+0%
Edon ZhegrovaFWD	Juventus	1.21	19provisional	0.35	29%	19	071% 124% 24% 3+1%
Jonathan DavidFWD	Juventus	0.65	39provisional	0.32	26%	35	074% 121% 24% 31% 4+0%

10 of 32 players shown, ranked by projection. Show all 32 →

Fouls won35 playersHighest: Patrizio Masini, 1.49 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
Patrizio MasiniMID	—	2.40	53provisional	1.49	40%	54	032% 128% 218% 311% 46% 53% 6+2%
Kenan YıldızFWD	Juventus	1.62	77provisional	1.41	41%	71	027% 133% 223% 311% 45% 51% 6+1%
Francisco ConceiçãoFWD	Juventus	1.72	67provisional	1.34	38%	57	030% 132% 221% 311% 44% 51% 6+1%
Arthur MeloMID	—	1.91	57provisional	1.29	36%	15	033% 131% 220% 310% 44% 52% 6+1%
Loïs OpendaFWD	Juventus	2.38	39provisional	1.13	29%	59	041% 129% 215% 38% 44% 52% 6+1%
Nico GonzálezFWD	Juventus	1.62	57provisional	1.08	30%	52	038% 132% 218% 38% 43% 51% 6+0%
Romano SchmidMID	—	1.08	88provisional	1.06	28%	66	036% 136% 219% 37% 42% 50% 6+0%
Dušan VlahovićFWD	Juventus	1.36	66provisional	1.03	28%	48	039% 133% 218% 37% 42% 51% 6+0%
Manuel LocatelliMID	Juventus	1.04	82provisional	0.96	25%	72	039% 136% 217% 36% 42% 5+0%
Florian GrillitschMID	—	1.38	55provisional	0.89	23%	21	044% 133% 215% 35% 42% 50% 6+0%

10 of 35 players shown, ranked by projection. Show all 35 →

Saves3 playersHighest: Guglielmo Vicario, 3.58 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
Guglielmo VicarioGK	—	3.61	89provisional	3.58	66%	55	04% 112% 218% 319% 417% 512% 6+18%
Michele Di GregorioGK	Juventus	3.11	88provisional	3.07	57%	63	06% 116% 221% 320% 415% 510% 6+11%
Mattia PerinGK	Juventus	3.15	85provisional	3.00	55%	14	07% 116% 222% 320% 415% 510% 6+10%

2 of 7 markets have no projection for this fixture.