La Liga · Spain

Athletic ClubvSevilla

Saturday 22 August at 16:00San Mamés Barriaexpected lineup

Referee

Not appointed

0 matches on record

Fouls per game

—

League average

23.2

fouls per match, both teams

Angles

every priced line builder

formRubén Vargas over 0.5 shots: 10/10 of his last 10, 12 in a row
Last 20: 18/20 · 5/5 away · strip 1 3 1 1 4 2 1 2 2 1 · market 80% · best 1.30 (Bet365) · bet365 1.30
formOihan Sancet over 0.5 tackles: 9/10 of his last 10, 9 in a row
Last 20: 14/20 · 5/5 at home · strip 3 2 1 3 1 2 1 1 1 0 · market 74% · best 1.40 (10Bet) · bet365 1.40
formAymeric Laporte over 0.5 tackles: 8/10 of his last 10, 4 in a row
Last 20: 15/20 · 5/6 at home · strip 1 2 2 1 0 2 0 2 2 1 · market 68% · best 1.47 (Parimatch) · bet365 1.36
formLucien Agoumé over 2.5 tackles: 8/10 of his last 10
Last 20: 11/20 · 3/4 away · strip 4 1 3 4 3 4 4 4 3 0 · market 67% · best 2.10 (10Bet) · bet365 1.53
formAitor Paredes over 0.5 fouls: 8/10 of his last 10
Last 20: 13/20 · strip 0 1 1 2 2 2 1 3 0 1 · market 77% · best 1.30 (Parimatch) · bet365 1.20
opponentSevilla concede 3.2 shots on target a game — 22nd most of 23 in La Liga
Over their last 10. A hard place to get shots on target.
opponentSevilla concede 9.5 shots a game — 21st most of 23 in La Liga
Over their last 10. A hard place to get shots.
opponentSevilla concede 13.2 tackles a game — 22nd most of 23 in La Liga
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 — 3 live calls

Dani Vivianover 1.50 Tackles1.73 at bet365 · fair 1.53+12.9%
Álex Berenguerover 0.50 Fouls1.40 at bet365 · fair 1.25+12.0%
Mikel Jauregizarover 0.50 Fouls1.25 at bet365 · fair 1.15+8.7%

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

Fouls55 playersHighest: Lucien Agoumé, 1.41 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
Lucien AgouméMID	Sevilla	1.62	76provisional	1.41	41%	70	027% 132% 223% 312% 45% 51% 6+1%
Iñigo Ruiz de GalarretaMID	Athletic Club	1.77	65provisional	1.40	41%	60	026% 134% 223% 311% 44% 51% 6+0%
Beñat PradosMID	Athletic Club	2.05	53provisional	1.38	39%	32	029% 132% 221% 311% 45% 52% 6+1%
Mikel JauregizarMID	Athletic Club	1.50	76provisional	1.31	38%	70	028% 134% 222% 310% 44% 51% 6+0%
Juan IglesiasDEF	Sevilla	1.37	83provisional	1.29	37%	75	028% 135% 222% 310% 43% 51% 6+0%
José Ángel CarmonaDEF	Sevilla	1.39	80provisional	1.28	36%	70	029% 135% 222% 310% 43% 51% 6+0%
Gorka GuruzetaFWD	Athletic Club	1.54	66provisional	1.22	34%	71	031% 134% 221% 39% 43% 51% 6+0%
MarcãoDEF	Sevilla	1.47	69provisional	1.19	34%	25	034% 133% 220% 39% 43% 51% 6+0%
Gabriel SuazoDEF	Sevilla	1.33	76provisional	1.17	33%	30	032% 135% 221% 38% 43% 51% 6+0%
Isaac RomeroFWD	Sevilla	1.45	67provisional	1.17	32%	61	033% 135% 220% 38% 43% 51% 6+0%
Rafa MirFWD	—	1.45	64provisional	1.12	31%	47	035% 134% 219% 38% 43% 51% 6+0%
Tanguy NianzouDEF	Sevilla	1.32	70provisional	1.09	30%	18	035% 135% 219% 37% 42% 51% 6+0%
Neal MaupayFWD	Sevilla	1.38	63provisional	1.05	28%	12	037% 134% 218% 37% 42% 51% 6+0%
César AzpilicuetaDEF	Sevilla	1.22	70provisional	1.02	27%	31	037% 136% 218% 37% 42% 5+0%
Alejandro RegoMID	Athletic Club	2.02	35provisional	1.01	26%	29	040% 134% 217% 36% 42% 51% 6+0%
Aitor ParedesDEF	Athletic Club	1.06	83provisional	1.00	26%	42	038% 136% 218% 36% 42% 5+0%
Dani VivianDEF	Athletic Club	1.05	84provisional	0.99	26%	63	038% 136% 218% 36% 42% 5+0%
Nico WilliamsFWD	Athletic Club	1.15	72provisional	0.98	26%	54	039% 136% 218% 36% 42% 5+0%
Dodi LukébakioFWD	Sevilla	1.03	82provisional	0.97	25%	40	039% 136% 218% 36% 41% 5+0%
Nemanja GudeljMID	Sevilla	1.24	66provisional	0.96	25%	65	041% 134% 217% 36% 42% 50% 6+0%
Álex BerenguerFWD	Athletic Club	1.13	71provisional	0.95	25%	67	040% 135% 217% 36% 42% 5+0%
Unai VencedorMID	—	1.75	39provisional	0.93	24%	16	044% 132% 215% 36% 42% 51% 6+0%
Kike SalasDEF	Sevilla	0.99	82provisional	0.92	24%	58	041% 135% 217% 35% 41% 5+0%
Álvaro DjalóFWD	Athletic Club	1.59	43provisional	0.91	23%	17	043% 134% 216% 36% 42% 50% 6+0%
Iñaki WilliamsFWD	Athletic Club	1.01	77provisional	0.90	23%	65	042% 136% 216% 35% 41% 5+0%
Batista MendyMID	Sevilla	1.58	43provisional	0.89	23%	27	045% 132% 215% 36% 42% 51% 6+0%
Maroan SannadiFWD	Athletic Club	1.86	33provisional	0.89	22%	26	044% 134% 215% 35% 42% 50% 6+0%
Joan JordánMID	Sevilla	1.56	41provisional	0.88	22%	29	044% 134% 215% 35% 42% 50% 6+0%
Jon GuridiMID	Sevilla	1.11	62provisional	0.84	21%	62	045% 134% 215% 35% 41% 5+0%
Oihan SancetMID	Athletic Club	1.18	56provisional	0.83	21%	57	045% 134% 215% 35% 41% 5+0%
Yuri BerchicheDEF	Athletic Club	0.90	80provisional	0.82	20%	62	045% 135% 215% 34% 41% 5+0%
Hugo RincónDEF	—	1.44	40provisional	0.78	19%	33	049% 132% 213% 34% 41% 5+0%
Iñigo LekueDEF	Athletic Club	1.25	50provisional	0.78	19%	27	050% 131% 213% 35% 41% 5+0%
Peque FernándezFWD	Sevilla	1.39	42provisional	0.78	19%	53	050% 131% 213% 34% 41% 5+0%
Jesús AresoDEF	Athletic Club	0.97	66provisional	0.76	18%	62	049% 133% 213% 34% 41% 5+0%
OsoDEF	Sevilla	0.88	73provisional	0.74	17%	22	049% 134% 213% 34% 41% 5+0%
Adama BoiroDEF	Athletic Club	0.90	67provisional	0.72	17%	32	050% 133% 212% 33% 41% 5+0%
Yeray ÁlvarezDEF	Athletic Club	0.76	82provisional	0.71	16%	28	050% 134% 212% 33% 41% 5+0%
Adrià PedrosaDEF	Sevilla	1.23	43provisional	0.70	16%	58	053% 131% 212% 34% 41% 5+0%
Andoni GorosabelDEF	Athletic Club	0.98	59provisional	0.70	16%	44	052% 132% 212% 33% 41% 5+0%
Robert NavarroFWD	Athletic Club	1.07	48provisional	0.67	15%	55	053% 132% 211% 33% 41% 5+0%
Alfon GonzálezFWD	Sevilla	1.11	45provisional	0.66	15%	46	054% 131% 211% 33% 41% 5+0%
Andrés CastrínDEF	Sevilla	0.87	60provisional	0.63	14%	19	055% 131% 211% 33% 41% 5+0%
Alexis SánchezFWD	Sevilla	1.05	44provisional	0.62	14%	41	056% 131% 210% 33% 41% 5+0%
Adnan JanuzajFWD	Sevilla	1.47	27provisional	0.61	13%	30	056% 131% 210% 32% 41% 5+0%
Nico SerranoFWD	Athletic Club	1.73	21provisional	0.59	12%	20	059% 129% 29% 33% 41% 5+0%
Aymeric LaporteDEF	Athletic Club	0.59	82provisional	0.55	11%	25	058% 131% 29% 32% 4+0%
Manu BuenoMID	Sevilla	1.13	33provisional	0.52	11%	18	062% 127% 28% 32% 40% 5+0%
Mikel VesgaMID	Athletic Club	1.30	26provisional	0.52	10%	48	061% 129% 28% 32% 40% 5+0%
Rubén VargasFWD	Sevilla	0.86	45provisional	0.51	10%	44	062% 128% 28% 32% 4+0%
Urko IzetaFWD	Athletic Club	1.64	16provisional	0.47	9%	13	064% 128% 27% 31% 4+0%
Stanis IdumboMID	Sevilla	0.77	33provisional	0.36	6%	15	071% 123% 25% 31% 4+0%
Chidera EjukeFWD	Sevilla	0.66	30provisional	0.30	4%	53	075% 121% 24% 3+1%
Unai SimónGK	Athletic Club	0.04	89provisional	0.04	—	58	097% 1+3%
Odysseas VlachodimosGK	Sevilla	0.01	88provisional	0.01	—	34	099% 1+1%

All 55 players shown, ranked by projection. Show fewer ↥

Shots55 playersHighest: Gorka Guruzeta, 2.09 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
Gorka GuruzetaFWD	Athletic Club	2.72	66provisional	2.09	58%	71	016% 125% 224% 317% 410% 55% 6+3%
Nico WilliamsFWD	Athletic Club	2.22	72provisional	1.85	53%	54	019% 128% 224% 315% 48% 53% 6+2%
Iñaki WilliamsFWD	Athletic Club	1.96	77provisional	1.72	50%	65	021% 130% 224% 314% 47% 53% 6+1%
Rafa MirFWD	—	2.15	64provisional	1.61	46%	47	025% 129% 222% 313% 46% 53% 6+1%
Dodi LukébakioFWD	Sevilla	1.64	82provisional	1.52	44%	40	024% 132% 224% 313% 45% 52% 6+1%
Oihan SancetMID	Athletic Club	2.15	56provisional	1.45	41%	57	027% 132% 222% 311% 45% 52% 6+1%
Álex BerenguerFWD	Athletic Club	1.74	71provisional	1.43	41%	67	027% 132% 223% 312% 45% 52% 6+1%
Isaac RomeroFWD	Sevilla	1.61	67provisional	1.26	35%	61	031% 133% 221% 310% 44% 51% 6+0%
Álvaro DjalóFWD	Athletic Club	1.90	43provisional	1.02	27%	17	040% 133% 217% 37% 42% 51% 6+0%
Neal MaupayFWD	Sevilla	1.38	63provisional	1.02	27%	12	040% 133% 217% 37% 42% 51% 6+0%

10 of 55 players shown, ranked by projection. Show all 55 →

Shots on target55 playersHighest: Gorka Guruzeta, 0.78 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
Gorka GuruzetaFWD	Athletic Club	1.01	66provisional	0.78	52%	71	048% 133% 214% 34% 41% 5+0%
Rafa MirFWD	—	0.87	64provisional	0.66	46%	47	054% 131% 211% 33% 41% 5+0%
Nico WilliamsFWD	Athletic Club	0.79	72provisional	0.66	47%	54	053% 132% 211% 33% 41% 5+0%
Iñaki WilliamsFWD	Athletic Club	0.72	77provisional	0.63	46%	65	054% 132% 211% 33% 4+1%
Dodi LukébakioFWD	Sevilla	0.66	82provisional	0.61	45%	40	055% 132% 210% 32% 4+0%
Álex BerenguerFWD	Athletic Club	0.69	71provisional	0.57	42%	67	058% 131% 29% 32% 4+0%
Oihan SancetMID	Athletic Club	0.77	56provisional	0.52	39%	57	061% 129% 28% 32% 4+0%
Isaac RomeroFWD	Sevilla	0.59	67provisional	0.46	36%	61	064% 128% 27% 31% 4+0%
Neal MaupayFWD	Sevilla	0.56	63provisional	0.42	33%	12	067% 126% 26% 31% 4+0%
Maroan SannadiFWD	Athletic Club	0.91	33provisional	0.40	31%	26	069% 124% 26% 31% 4+0%

10 of 55 players shown, ranked by projection. Show all 55 →

Fouls won55 playersHighest: Peque Fernández, 1.71 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
Peque FernándezFWD	Sevilla	3.30	42provisional	1.71	45%	53	028% 127% 218% 312% 47% 54% 6+3%
Juan IglesiasDEF	Sevilla	1.73	83provisional	1.62	47%	75	022% 131% 224% 314% 46% 52% 6+1%
Dodi LukébakioFWD	Sevilla	1.70	82provisional	1.57	46%	40	022% 132% 224% 313% 46% 52% 6+1%
Nico WilliamsFWD	Athletic Club	1.68	72provisional	1.38	40%	54	028% 133% 222% 311% 44% 51% 6+1%
Hugo RincónDEF	—	2.69	40provisional	1.34	35%	33	033% 131% 218% 39% 44% 52% 6+2%
MarcãoDEF	Sevilla	1.60	69provisional	1.26	36%	25	033% 131% 220% 310% 44% 51% 6+0%
José Ángel CarmonaDEF	Sevilla	1.40	80provisional	1.26	36%	70	030% 134% 222% 310% 43% 51% 6+0%
Iñigo Ruiz de GalarretaMID	Athletic Club	1.62	65provisional	1.23	35%	60	031% 134% 221% 39% 43% 51% 6+0%
Neal MaupayFWD	Sevilla	1.68	63provisional	1.23	34%	12	033% 132% 220% 39% 44% 51% 6+0%
Álex BerenguerFWD	Athletic Club	1.39	71provisional	1.14	31%	67	034% 134% 220% 38% 43% 51% 6+0%

10 of 55 players shown, ranked by projection. Show all 55 →

Saves2 playersHighest: Odysseas Vlachodimos, 3.12 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
Odysseas VlachodimosGK	Sevilla	3.16	88provisional	3.12	58%	34	06% 115% 221% 320% 416% 510% 6+12%
Unai SimónGK	Athletic Club	2.36	89provisional	2.34	41%	58	012% 123% 225% 319% 411% 56% 6+5%

2 of 7 markets have no projection for this fixture.