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

22.6

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 v20260813-1451 · Tackles v20260813-1451 · Shots v20260813-1451 · Fouls won v20260813-1451 · Cards v20260813-1451 · Saves v20260813-1451

Model projections

Fouls35 playersHighest: Pablo Martín Paez Gaviria, 1.34 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
Pablo Martín Paez GaviriaMID	FC Barcelona	2.67	45provisional	1.34	37%	21	037% 126% 217% 311% 45% 52% 6+1%
Marc Casadó TorrasMID	FC Barcelona	2.13	55provisional	1.31	37%	33	035% 128% 219% 311% 45% 52% 6+1%
Fermín López MarínMID	FC Barcelona	2.21	53provisional	1.29	36%	40	032% 132% 220% 310% 44% 52% 6+1%
Aboubacar Sangaré TraoréDEF	Elche	2.57	45provisional	1.29	37%	7	037% 126% 218% 311% 45% 52% 6+1%
José Antonio Morente OlivaFWD	Elche	1.60	72provisional	1.27	36%	11	030% 133% 221% 310% 44% 51% 6+0%
Víctor Chust GarcíaDEF	Elche	1.43	70provisional	1.11	31%	27	037% 132% 218% 38% 43% 51% 6+0%
Lamine Yamal Nasraoui EbanaFWD	FC Barcelona	1.16	82provisional	1.06	29%	62	036% 135% 219% 37% 42% 50% 6+0%
Daniel Olmo CarvajalMID	FC Barcelona	1.64	57provisional	1.04	28%	43	039% 133% 218% 37% 42% 51% 6+0%
Marc Bernal CasasMID	FC Barcelona	2.23	41provisional	1.02	27%	12	045% 128% 215% 38% 43% 51% 6+0%
German Valera KarabinaiteFWD	Elche	1.14	80provisional	1.01	27%	33	038% 135% 218% 37% 42% 5+1%

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

Tackles35 playersHighest: Víctor Chust García, 1.93 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
Víctor Chust GarcíaDEF	Elche	2.49	70provisional	1.93	51%	27	023% 125% 220% 314% 49% 55% 6+4%
David AffengruberDEF	Elche	2.08	82provisional	1.89	53%	35	019% 127% 223% 315% 48% 54% 6+3%
Jules KoundéDEF	FC Barcelona	2.11	78provisional	1.84	51%	55	022% 127% 222% 314% 48% 54% 6+3%
Eric García MartretDEF	FC Barcelona	2.28	68provisional	1.73	47%	51	026% 127% 221% 313% 47% 54% 6+3%
Pedro González LópezMID	FC Barcelona	2.05	76provisional	1.72	49%	61	023% 129% 223% 314% 47% 53% 6+2%
Marc Aguado PallaresMID	Elche	1.85	72provisional	1.47	42%	31	028% 130% 221% 312% 45% 52% 6+1%
German Valera KarabinaiteFWD	Elche	1.65	80provisional	1.47	42%	33	028% 131% 222% 312% 45% 52% 6+1%
Marc Casadó TorrasMID	FC Barcelona	2.33	55provisional	1.43	39%	33	035% 127% 218% 311% 46% 53% 6+2%
Aboubacar Sangaré TraoréDEF	Elche	2.82	45provisional	1.42	39%	7	037% 125% 217% 311% 46% 53% 6+2%
Marc Bernal CasasMID	FC Barcelona	3.02	41provisional	1.39	36%	12	038% 126% 215% 39% 46% 53% 6+2%

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

Shots35 playersHighest: Lamine Yamal Nasraoui Ebana, 3.41 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 Yamal Nasraoui EbanaFWD	FC Barcelona	3.75	82provisional	3.41	81%	62	06% 113% 218% 319% 416% 512% 6+16%
Raphael Dias BelloliFWD	FC Barcelona	3.26	73provisional	2.64	70%	54	011% 120% 222% 319% 413% 58% 6+8%
Robert LewandowskiFWD	FC Barcelona	3.15	72provisional	2.51	67%	13	013% 120% 222% 318% 413% 57% 6+7%
Ferrán Torres GarcíaFWD	FC Barcelona	3.23	51provisional	1.84	50%	40	024% 127% 220% 314% 48% 54% 6+3%
Daniel Olmo CarvajalMID	FC Barcelona	2.74	57provisional	1.73	48%	43	023% 129% 222% 314% 47% 53% 6+2%
Fermín López MarínMID	FC Barcelona	2.79	53provisional	1.63	45%	40	026% 129% 221% 312% 47% 53% 6+2%
José Antonio Morente OlivaFWD	Elche	1.32	72provisional	1.05	28%	11	038% 134% 218% 37% 42% 51% 6+0%
Lucas CepedaFWD	Elche	2.14	40provisional	0.95	24%	4	049% 127% 213% 37% 43% 51% 6+1%
German Valera KarabinaiteFWD	Elche	1.05	80provisional	0.93	24%	33	042% 134% 216% 36% 42% 5+0%
Grady DianganaFWD	Elche	1.48	47provisional	0.77	19%	13	050% 131% 213% 34% 41% 5+0%

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

Fouls won35 playersHighest: Lamine Yamal Nasraoui Ebana, 1.83 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 Yamal Nasraoui EbanaFWD	FC Barcelona	2.01	82provisional	1.83	53%	62	019% 128% 224% 315% 48% 53% 6+2%
German Valera KarabinaiteFWD	Elche	1.95	80provisional	1.73	50%	33	021% 129% 224% 315% 47% 53% 6+2%
Pedro González LópezMID	FC Barcelona	1.71	76provisional	1.44	41%	61	027% 132% 222% 312% 45% 52% 6+1%
Gonzalo Villar del FraileMID	Elche	2.12	60provisional	1.41	39%	8	033% 128% 219% 311% 46% 52% 6+1%
Daniel Olmo CarvajalMID	FC Barcelona	1.91	57provisional	1.20	33%	43	034% 132% 219% 39% 43% 51% 6+0%
Marc Casadó TorrasMID	FC Barcelona	1.84	55provisional	1.13	31%	33	040% 129% 217% 39% 44% 51% 6+1%
Pablo Martín Paez GaviriaMID	FC Barcelona	2.09	45provisional	1.05	28%	21	044% 127% 215% 38% 43% 51% 6+1%
Alejandro Balde MartínezDEF	FC Barcelona	1.35	69provisional	1.04	28%	53	038% 134% 218% 37% 42% 51% 6+0%
Frenkie de JongMID	FC Barcelona	1.53	54provisional	0.92	24%	37	045% 131% 215% 36% 42% 51% 6+0%
Raphael Dias BelloliFWD	FC Barcelona	1.10	73provisional	0.89	23%	54	043% 134% 216% 35% 41% 5+0%

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

Cards35 playersHighest: Víctor Chust García, 0.21 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
Víctor Chust GarcíaDEF	Elche	0.28	70provisional	0.21	19%	27	081% 117% 22% 3+0%
Aboubacar Sangaré TraoréDEF	Elche	0.40	45provisional	0.20	18%	7	082% 115% 22% 3+0%
Pablo Martín Paez GaviriaMID	FC Barcelona	0.38	45provisional	0.19	17%	21	083% 115% 22% 3+0%
Marc Bernal CasasMID	FC Barcelona	0.40	41provisional	0.18	16%	12	084% 114% 22% 3+0%
David AffengruberDEF	Elche	0.20	82provisional	0.18	17%	35	083% 115% 21% 3+0%
Marc Casadó TorrasMID	FC Barcelona	0.29	55provisional	0.18	16%	33	084% 114% 22% 3+0%
Eric García MartretDEF	FC Barcelona	0.22	68provisional	0.17	15%	51	085% 114% 21% 3+0%
Fermín López MarínMID	FC Barcelona	0.28	53provisional	0.16	15%	40	085% 113% 2+1%
Jules KoundéDEF	FC Barcelona	0.18	78provisional	0.16	14%	55	086% 113% 2+1%
Pedro Bigas RigoDEF	Elche	0.19	74provisional	0.15	14%	25	086% 113% 2+1%

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

Saves3 playersHighest: Joan García Pons, 2.89 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
Joan García PonsGK	FC Barcelona	2.89	90provisional	2.89	53%	30	07% 117% 222% 320% 415% 59% 6+9%
Wojciech SzczęsnyGK	FC Barcelona	2.70	90provisional	2.70	49%	23	09% 119% 223% 320% 414% 58% 6+7%
Matías Ezequiel DituroGK	Elche	2.68	90provisional	2.68	49%	22	09% 119% 223% 320% 414% 58% 6+7%

1 of 7 markets have no projection for this fixture.