La Liga · Spain

ElchevFC Barcelona

Sunday 23 August at 20:30Estadio Manuel Martínez Valerolineups not announced

Referee

Not appointed

0 matches on record

Fouls per game

—

League average

23.2

fouls per match, both teams

Angles

every priced line builder

opponentElche concede 16.1 fouls a game — 1st most of 23 in La Liga
Over their last 10. Good news for FC Barcelona's foul-drawers.
opponentElche concede 20.0 tackles a game — 1st most of 23 in La Liga
Over their last 10. Good news for FC Barcelona's tacklers.
opponentFC Barcelona concede 2.7 shots on target a game — 23rd most of 23 in La Liga
Over their last 10. A hard place to get shots on target.
opponentFC Barcelona concede 8.6 shots a game — 23rd most of 23 in La Liga
Over their last 10. A hard place to get shots.

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

Fouls45 playersHighest: Buba Sangaré, 1.59 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
Buba SangaréDEF	Elche	2.15	60provisional	1.59	46%	13	024% 130% 223% 313% 46% 52% 6+1%
GaviMID	FC Barcelona	2.02	57provisional	1.42	41%	37	028% 131% 222% 312% 45% 52% 6+1%
Fermín LópezMID	FC Barcelona	1.88	61provisional	1.41	40%	58	027% 133% 223% 311% 45% 52% 6+1%
Marc BernalMID	FC Barcelona	1.76	55provisional	1.20	34%	25	034% 132% 220% 39% 43% 51% 6+0%
Lamine YamalFWD	FC Barcelona	1.28	83provisional	1.20	34%	63	031% 135% 221% 39% 43% 51% 6+0%
Víctor ChustDEF	Elche	1.26	76provisional	1.10	30%	32	035% 135% 219% 38% 42% 51% 6+0%
Gonzalo VillarMID	Elche	1.33	69provisional	1.09	30%	13	036% 134% 219% 38% 42% 51% 6+0%
Marc CasadóMID	FC Barcelona	1.98	40provisional	1.06	28%	47	040% 132% 216% 37% 43% 51% 6+0%
Eric GarcíaDEF	FC Barcelona	1.22	74provisional	1.05	28%	63	037% 135% 219% 37% 42% 51% 6+0%
RodriMID	—	1.23	71provisional	1.03	28%	24	037% 135% 218% 37% 42% 50% 6+0%

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

Shots45 playersHighest: Lamine Yamal, 3.36 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
Lamine YamalFWD	FC Barcelona	3.61	83provisional	3.36	81%	63	05% 113% 219% 320% 416% 512% 6+15%
RaphinhaFWD	FC Barcelona	3.20	65provisional	2.42	64%	58	014% 122% 222% 317% 412% 57% 6+6%
Robert LewandowskiFWD	FC Barcelona	2.78	72provisional	2.31	63%	65	014% 123% 223% 318% 411% 56% 6+5%
Fermín LópezMID	FC Barcelona	2.63	61provisional	1.91	54%	58	019% 127% 223% 315% 48% 54% 6+3%
Anthony GordonFWD	—	2.01	74provisional	1.71	50%	60	021% 130% 224% 314% 47% 53% 6+1%
Ferran TorresFWD	FC Barcelona	2.65	50provisional	1.62	45%	60	026% 129% 221% 312% 46% 53% 6+2%
Dani OlmoMID	FC Barcelona	2.32	54provisional	1.52	43%	58	026% 131% 222% 312% 46% 52% 6+1%
André SilvaFWD	Elche	2.14	50provisional	1.29	35%	46	035% 130% 218% 310% 45% 52% 6+1%
Roony BardghjiFWD	FC Barcelona	2.11	37provisional	1.00	26%	21	043% 131% 215% 37% 43% 51% 6+0%
Karim AdeyemiFWD	—	2.07	38provisional	0.99	26%	53	044% 131% 215% 37% 43% 51% 6+0%

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

Shots on target45 playersHighest: Lamine Yamal, 1.11 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
Lamine YamalFWD	FC Barcelona	1.19	83provisional	1.11	65%	63	035% 135% 219% 38% 42% 51% 6+0%
Robert LewandowskiFWD	FC Barcelona	1.19	72provisional	0.99	60%	65	040% 134% 217% 36% 42% 50% 6+0%
RaphinhaFWD	FC Barcelona	1.19	65provisional	0.90	57%	58	043% 133% 216% 36% 42% 50% 6+0%
Ferran TorresFWD	FC Barcelona	1.28	50provisional	0.78	51%	60	049% 132% 213% 34% 41% 5+0%
Fermín LópezMID	FC Barcelona	1.00	61provisional	0.72	50%	58	050% 133% 212% 34% 41% 5+0%
Anthony GordonFWD	—	0.74	74provisional	0.63	46%	60	054% 132% 211% 32% 4+1%
André SilvaFWD	Elche	1.00	50provisional	0.60	42%	46	058% 129% 210% 33% 41% 5+0%
Dani OlmoMID	FC Barcelona	0.80	54provisional	0.53	39%	58	061% 129% 28% 32% 4+0%
Roony BardghjiFWD	FC Barcelona	0.89	37provisional	0.43	33%	21	067% 125% 26% 31% 4+0%
Karim AdeyemiFWD	—	0.74	38provisional	0.35	28%	53	072% 122% 25% 31% 4+0%

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

Fouls won45 playersHighest: Lamine Yamal, 1.76 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
Lamine YamalFWD	FC Barcelona	1.90	83provisional	1.76	51%	63	019% 129% 225% 315% 47% 53% 6+1%
Gonzalo VillarMID	Elche	2.17	69provisional	1.72	49%	13	023% 128% 223% 314% 47% 53% 6+2%
Anthony GordonFWD	—	2.04	74provisional	1.72	50%	60	020% 130% 224% 315% 47% 53% 6+1%
Germán ValeraFWD	Elche	1.73	75provisional	1.47	42%	45	027% 131% 222% 312% 45% 52% 6+1%
RodriMID	—	1.65	71provisional	1.34	38%	24	029% 133% 222% 311% 44% 51% 6+1%
GaviMID	FC Barcelona	2.01	57provisional	1.34	38%	37	032% 130% 220% 311% 45% 52% 6+1%
PedriMID	FC Barcelona	1.69	66provisional	1.30	36%	66	031% 132% 220% 310% 44% 51% 6+1%
Dani OlmoMID	FC Barcelona	1.76	54provisional	1.13	31%	58	036% 133% 219% 38% 43% 51% 6+0%
Grady DianganaFWD	Elche	1.56	56provisional	1.03	28%	54	039% 133% 217% 37% 42% 51% 6+0%
Robert LewandowskiFWD	FC Barcelona	1.23	72provisional	1.01	27%	65	039% 134% 218% 37% 42% 51% 6+0%

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

Saves5 playersHighest: Iñaki Peña, 3.13 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
Iñaki PeñaGK	Elche	3.22	87provisional	3.13	58%	32	06% 115% 220% 320% 416% 510% 6+12%
Joan GarcíaGK	FC Barcelona	3.08	89provisional	3.05	56%	68	06% 116% 221% 320% 415% 510% 6+11%
Wojciech SzczesnyGK	FC Barcelona	3.02	88provisional	2.97	55%	23	07% 117% 222% 320% 415% 59% 6+10%
Matías DituroGK	Elche	2.99	88provisional	2.94	54%	23	07% 117% 222% 320% 415% 59% 6+10%
Marc-André ter StegenGK	FC Barcelona	2.66	83provisional	2.50	44%	10	010% 121% 224% 319% 413% 57% 6+6%

2 of 7 markets have no projection for this fixture.