La Liga · Spain

MálagavDeportivo A Coruña

Monday 24 August at 20:30Estadio La Rosaledalineups not announced

Referee

Not appointed

0 matches on record

Fouls per game

—

League average

23.2

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 v20260816-1555 · Shots v20260816-1555 · Shots on target v20260816-1555 · Fouls won v20260816-1555 · Saves v20260816-1229

Model projections

Fouls6 playersHighest: Adrià Altimira, 1.07 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
Adrià AltimiraDEF	Deportivo A Coruña	1.39	63provisional	1.07	29%	24	036% 135% 219% 37% 42% 51% 6+0%
AngeliñoDEF	—	1.06	55provisional	0.71	17%	45	052% 131% 212% 34% 41% 5+0%
Juan CruzFWD	Málaga	0.75	59provisional	0.55	11%	35	059% 130% 29% 32% 4+0%
Fernando CaleroDEF	—	0.80	55provisional	0.54	11%	49	060% 129% 29% 32% 4+0%
José SalinasDEF	Málaga	1.66	19provisional	0.53	10%	12	062% 128% 28% 32% 41% 5+0%
Leo RománGK	Deportivo A Coruña	0.08	89provisional	0.08	0%	41	092% 18% 2+0%

Shots6 playersHighest: Juan Cruz, 1.00 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
Juan CruzFWD	Málaga	1.42	59provisional	1.00	27%	35	041% 133% 217% 37% 42% 51% 6+0%
AngeliñoDEF	—	0.71	55provisional	0.46	9%	45	065% 126% 27% 32% 4+0%
Fernando CaleroDEF	—	0.49	55provisional	0.32	5%	49	074% 121% 24% 3+1%
Adrià AltimiraDEF	Deportivo A Coruña	0.37	63provisional	0.28	4%	24	076% 120% 23% 3+0%
José SalinasDEF	Málaga	0.45	19provisional	0.13	1%	12	089% 110% 21% 3+0%
Leo RománGK	Deportivo A Coruña	0.00	89provisional	0.00	—	41	0100% 1+0%

Shots on target6 playersHighest: Juan Cruz, 0.37 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
Juan CruzFWD	Málaga	0.52	59provisional	0.37	30%	35	070% 124% 25% 31% 4+0%
AngeliñoDEF	—	0.19	55provisional	0.12	11%	45	089% 110% 2+1%
Fernando CaleroDEF	—	0.14	55provisional	0.09	9%	49	091% 18% 2+1%
Adrià AltimiraDEF	Deportivo A Coruña	0.10	63provisional	0.07	7%	24	093% 17% 2+0%
José SalinasDEF	Málaga	0.12	19provisional	0.03	3%	12	097% 1+3%
Leo RománGK	Deportivo A Coruña	0.00	89provisional	0.00	0%	41	0100% 1+0%

Fouls won6 playersHighest: Adrià Altimira, 1.03 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
Adrià AltimiraDEF	Deportivo A Coruña	1.40	63provisional	1.03	28%	24	039% 134% 218% 37% 42% 51% 6+0%
Juan CruzFWD	Málaga	1.04	59provisional	0.73	17%	35	051% 132% 212% 34% 41% 5+0%
AngeliñoDEF	—	0.87	55provisional	0.56	12%	45	060% 128% 29% 32% 41% 5+0%
Fernando CaleroDEF	—	0.56	55provisional	0.36	6%	49	071% 123% 25% 31% 4+0%
José SalinasDEF	Málaga	1.25	19provisional	0.33	5%	12	074% 120% 24% 31% 4+0%
Leo RománGK	Deportivo A Coruña	0.10	89provisional	0.10	0%	41	091% 19% 2+0%

Saves1 playersHighest: Leo Román, 2.44 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
Leo RománGK	Deportivo A Coruña	2.46	89provisional	2.44	43%	41	011% 122% 224% 319% 412% 56% 6+5%

2 of 7 markets have no projection for this fixture.