La Liga · Spain

Racing SantandervElche

Friday 28 August at 18:00Campos de Sport de El Sardinerolineups 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

Fouls14 playersHighest: Aboubacar Sangaré Traoré, 1.83 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
Aboubacar Sangaré TraoréDEF	Elche	3.64	45provisional	1.83	49%	7	029% 122% 218% 314% 49% 55% 6+3%
José Antonio Morente OlivaFWD	Elche	2.26	72provisional	1.81	52%	11	020% 128% 224% 315% 48% 53% 6+2%
Víctor Chust GarcíaDEF	Elche	2.03	70provisional	1.57	45%	27	027% 129% 221% 313% 46% 53% 6+1%
German Valera KarabinaiteFWD	Elche	1.62	80provisional	1.43	42%	33	026% 132% 223% 312% 45% 51% 6+1%
Gonzalo Villar del FraileMID	Elche	2.00	60provisional	1.33	37%	8	034% 129% 219% 311% 45% 52% 6+1%
David AffengruberDEF	Elche	1.41	82provisional	1.28	37%	35	029% 134% 222% 310% 43% 51% 6+0%
Pedro Bigas RigoDEF	Elche	1.48	74provisional	1.21	34%	25	033% 133% 221% 39% 43% 51% 6+0%
Martim Carvalho NetoMID	Elche	1.98	54provisional	1.19	33%	20	034% 133% 220% 39% 43% 51% 6+0%
Marc Aguado PallaresMID	Elche	1.41	72provisional	1.13	31%	31	035% 134% 220% 38% 43% 51% 6+0%
Grady DianganaFWD	Elche	2.17	47provisional	1.12	31%	13	038% 131% 218% 38% 43% 51% 6+0%

10 of 14 players shown, ranked by projection. Show all 14 →

Tackles14 playersHighest: Víctor Chust García, 2.65 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	3.42	70provisional	2.65	64%	27	016% 120% 218% 315% 411% 58% 6+11%
David AffengruberDEF	Elche	2.85	82provisional	2.60	68%	35	012% 120% 222% 318% 412% 58% 6+8%
Marc Aguado PallaresMID	Elche	2.54	72provisional	2.03	55%	31	020% 125% 222% 316% 49% 55% 6+4%
German Valera KarabinaiteFWD	Elche	2.27	80provisional	2.01	56%	33	019% 125% 223% 316% 49% 55% 6+3%
Aboubacar Sangaré TraoréDEF	Elche	3.88	45provisional	1.95	50%	7	029% 121% 217% 313% 49% 55% 6+6%
Pedro Bigas RigoDEF	Elche	2.28	74provisional	1.88	52%	25	022% 126% 222% 315% 48% 54% 6+3%
José Antonio Morente OlivaFWD	Elche	2.26	72provisional	1.80	50%	11	022% 128% 222% 314% 48% 54% 6+2%
Gonzalo Villar del FraileMID	Elche	2.71	60provisional	1.80	48%	8	028% 125% 219% 313% 48% 54% 6+4%
Martim Carvalho NetoMID	Elche	2.51	54provisional	1.51	42%	20	028% 130% 221% 311% 46% 52% 6+2%
Grady DianganaFWD	Elche	2.14	47provisional	1.11	30%	13	040% 130% 217% 38% 43% 51% 6+1%

10 of 14 players shown, ranked by projection. Show all 14 →

Shots14 playersHighest: José Antonio Morente Oliva, 1.61 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
José Antonio Morente OlivaFWD	Elche	2.02	72provisional	1.61	46%	11	024% 130% 223% 313% 46% 52% 6+1%
Lucas CepedaFWD	Elche	3.28	40provisional	1.45	36%	4	038% 126% 215% 39% 46% 53% 6+3%
German Valera KarabinaiteFWD	Elche	1.60	80provisional	1.42	41%	33	027% 132% 222% 312% 45% 52% 6+1%
Grady DianganaFWD	Elche	2.28	47provisional	1.18	33%	13	037% 130% 218% 39% 44% 51% 6+1%
Gonzalo Villar del FraileMID	Elche	1.15	60provisional	0.77	19%	8	051% 130% 213% 35% 41% 5+0%
Martim Carvalho NetoMID	Elche	1.16	54provisional	0.70	16%	20	052% 132% 212% 33% 41% 5+0%
David AffengruberDEF	Elche	0.58	82provisional	0.53	10%	35	060% 130% 28% 32% 4+0%
Pedro Bigas RigoDEF	Elche	0.61	74provisional	0.50	10%	25	062% 129% 28% 32% 4+0%
Víctor Chust GarcíaDEF	Elche	0.56	70provisional	0.43	8%	27	066% 126% 26% 31% 4+0%
Aboubacar Sangaré TraoréDEF	Elche	0.65	45provisional	0.33	5%	7	074% 121% 25% 31% 4+0%
John Nwankwo DonaldDEF	Elche	0.91	31provisional	0.31	6%	7	076% 118% 24% 31% 4+0%
Marc Aguado PallaresMID	Elche	0.38	72provisional	0.30	4%	31	074% 122% 24% 3+0%
Federico Redondo SolariMID	Elche	1.36	16provisional	0.24	4%	2	080% 116% 23% 31% 4+0%
Matías Ezequiel DituroGK	Elche	0.00	90provisional	0.00	—	22	0100% 1+0%

All 14 players shown, ranked by projection. Show fewer ↥

Fouls won14 playersHighest: German Valera Karabinaite, 2.51 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
German Valera KarabinaiteFWD	Elche	2.84	80provisional	2.51	68%	33	012% 120% 222% 319% 413% 57% 6+6%
Gonzalo Villar del FraileMID	Elche	3.08	60provisional	2.05	54%	8	023% 123% 219% 314% 410% 56% 6+5%
José Antonio Morente OlivaFWD	Elche	1.56	72provisional	1.24	35%	11	032% 133% 220% 39% 43% 51% 6+0%
Grady DianganaFWD	Elche	2.34	47provisional	1.21	33%	13	036% 130% 218% 39% 44% 51% 6+1%
David AffengruberDEF	Elche	1.27	82provisional	1.16	32%	35	034% 134% 220% 38% 43% 51% 6+0%
Lucas CepedaFWD	Elche	2.33	40provisional	1.03	27%	4	047% 127% 213% 37% 44% 52% 6+1%
Marc Aguado PallaresMID	Elche	1.09	72provisional	0.87	22%	31	044% 133% 215% 35% 41% 5+0%
Pedro Bigas RigoDEF	Elche	1.05	74provisional	0.86	22%	25	045% 133% 215% 35% 41% 5+0%
Aboubacar Sangaré TraoréDEF	Elche	1.66	45provisional	0.83	22%	7	050% 128% 214% 36% 42% 50% 6+0%
Martim Carvalho NetoMID	Elche	1.31	54provisional	0.79	19%	20	048% 133% 213% 34% 41% 5+0%

10 of 14 players shown, ranked by projection. Show all 14 →

Cards14 playersHighest: Víctor Chust García, 0.30 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	0.39	70provisional	0.30	26%	27	074% 121% 24% 3+1%
Aboubacar Sangaré TraoréDEF	Elche	0.57	45provisional	0.29	24%	7	076% 120% 24% 3+1%
David AffengruberDEF	Elche	0.28	82provisional	0.26	23%	35	077% 120% 23% 3+0%
Pedro Bigas RigoDEF	Elche	0.27	74provisional	0.22	19%	25	081% 117% 22% 3+0%
José Antonio Morente OlivaFWD	Elche	0.27	72provisional	0.21	19%	11	081% 117% 22% 3+0%
Gonzalo Villar del FraileMID	Elche	0.32	60provisional	0.21	18%	8	082% 116% 22% 3+0%
German Valera KarabinaiteFWD	Elche	0.21	80provisional	0.19	17%	33	083% 115% 22% 3+0%
Marc Aguado PallaresMID	Elche	0.23	72provisional	0.18	16%	31	084% 115% 21% 3+0%
Grady DianganaFWD	Elche	0.32	47provisional	0.17	15%	13	085% 113% 21% 3+0%
Martim Carvalho NetoMID	Elche	0.26	54provisional	0.16	15%	20	085% 113% 2+1%

10 of 14 players shown, ranked by projection. Show all 14 →

Saves1 playersHighest: Matías Ezequiel Dituro, 3.43 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
Matías Ezequiel DituroGK	Elche	3.43	90provisional	3.43	63%	22	05% 113% 219% 320% 416% 512% 6+16%

1 of 7 markets have no projection for this fixture.