PER90My betsAccount

La Liga · Spain

Deportivo La CoruñavElche

Estadio Abanca-Riazorlineups 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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 v20260811-1858 · Tackles v20260811-1858 · Shots v20260811-1858 · Shots on target v20260811-1905 · Fouls won v20260811-1858 · Cards v20260811-1905 · Saves v20260812-1340

Model projections

Fouls14 playersHighest: German Valera Karabinaite, 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.

  • German Valera Karabinaite

    FWD · Elche

    provisional
    Mins
    80
    Proj.
    1.41
    O 1.5
    40%
    Model /90
    1.60
    028%
    132%
    222%
    311%
    45%
    52%
    6+1%
  • José Antonio Morente Oliva

    FWD · Elche

    provisional
    Mins
    72
    Proj.
    1.35
    O 1.5
    38%
    Model /90
    1.70
    030%
    132%
    221%
    311%
    44%
    52%
    6+1%
  • Marc Aguado Pallares

    MID · Elche

    provisional
    Mins
    72
    Proj.
    1.32
    O 1.5
    37%
    Model /90
    1.66
    031%
    132%
    221%
    310%
    44%
    51%
    6+1%
  • Gonzalo Villar del Fraile

    MID · Elche

    provisional
    Mins
    60
    Proj.
    1.27
    O 1.5
    35%
    Model /90
    1.92
    036%
    129%
    218%
    310%
    45%
    52%
    6+1%
  • David Affengruber

    DEF · Elche

    provisional
    Mins
    82
    Proj.
    1.18
    O 1.5
    33%
    Model /90
    1.30
    033%
    134%
    220%
    39%
    43%
    51%
    6+0%
  • Víctor Chust García

    DEF · Elche

    provisional
    Mins
    70
    Proj.
    1.16
    O 1.5
    32%
    Model /90
    1.50
    037%
    131%
    218%
    39%
    43%
    51%
    6+0%
  • Pedro Bigas Rigo

    DEF · Elche

    provisional
    Mins
    74
    Proj.
    1.16
    O 1.5
    32%
    Model /90
    1.41
    035%
    133%
    219%
    39%
    43%
    51%
    6+0%
  • Martim Carvalho Neto

    MID · Elche

    provisional
    Mins
    54
    Proj.
    1.16
    O 1.5
    32%
    Model /90
    1.93
    036%
    133%
    219%
    38%
    43%
    51%
    6+0%
  • Grady Diangana

    FWD · Elche

    provisional
    Mins
    47
    Proj.
    0.97
    O 1.5
    26%
    Model /90
    1.88
    043%
    131%
    216%
    37%
    42%
    51%
    6+0%
  • Aboubacar Sangaré Traoré

    DEF · Elche

    provisional
    Mins
    45
    Proj.
    0.96
    O 1.5
    26%
    Model /90
    1.92
    046%
    128%
    215%
    37%
    43%
    51%
    6+0%
  • Lucas Cepeda

    FWD · Elche

    provisional
    Mins
    40
    Proj.
    0.96
    O 1.5
    25%
    Model /90
    2.16
    049%
    126%
    213%
    37%
    43%
    51%
    6+1%
  • John Nwankwo Donald

    DEF · Elche

    provisional
    Mins
    31
    Proj.
    0.90
    O 1.5
    22%
    Model /90
    2.60
    053%
    125%
    211%
    36%
    43%
    52%
    6+1%
  • Federico Redondo Solari

    MID · Elche

    provisional
    Mins
    16
    Proj.
    0.54
    O 1.5
    11%
    Model /90
    3.00
    064%
    125%
    27%
    32%
    41%
    50%
    6+0%
  • Matías Ezequiel Dituro

    GK · Elche

    provisional
    Mins
    90
    Proj.
    0.04
    O 1.5
    —
    Model /90
    0.04
    096%
    1+4%
Tackles14 playersHighest: David Affengruber, 2.25 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.

  • David Affengruber

    DEF · Elche

    provisional
    Mins
    82
    Proj.
    2.25
    O 1.5
    64%
    Model /90
    2.47
    012%
    123%
    225%
    319%
    411%
    55%
    6+3%
  • Marc Aguado Pallares

    MID · Elche

    provisional
    Mins
    72
    Proj.
    2.07
    O 1.5
    59%
    Model /90
    2.59
    017%
    124%
    224%
    318%
    410%
    55%
    6+3%
  • Víctor Chust García

    DEF · Elche

    provisional
    Mins
    70
    Proj.
    1.90
    O 1.5
    53%
    Model /90
    2.46
    021%
    126%
    222%
    315%
    49%
    54%
    6+3%
  • Gonzalo Villar del Fraile

    MID · Elche

    provisional
    Mins
    60
    Proj.
    1.86
    O 1.5
    51%
    Model /90
    2.80
    025%
    124%
    220%
    315%
    49%
    55%
    6+3%
  • Pedro Bigas Rigo

    DEF · Elche

    provisional
    Mins
    74
    Proj.
    1.83
    O 1.5
    53%
    Model /90
    2.22
    020%
    127%
    224%
    316%
    48%
    53%
    6+2%
  • Martim Carvalho Neto

    MID · Elche

    provisional
    Mins
    54
    Proj.
    1.60
    O 1.5
    46%
    Model /90
    2.65
    024%
    130%
    223%
    313%
    46%
    52%
    6+1%
  • Aboubacar Sangaré Traoré

    DEF · Elche

    provisional
    Mins
    45
    Proj.
    1.57
    O 1.5
    44%
    Model /90
    3.13
    032%
    124%
    219%
    313%
    47%
    53%
    6+2%
  • German Valera Karabinaite

    FWD · Elche

    provisional
    Mins
    80
    Proj.
    1.54
    O 1.5
    45%
    Model /90
    1.73
    024%
    131%
    224%
    313%
    45%
    52%
    6+1%
  • José Antonio Morente Oliva

    FWD · Elche

    provisional
    Mins
    72
    Proj.
    1.44
    O 1.5
    42%
    Model /90
    1.80
    026%
    132%
    223%
    312%
    45%
    52%
    6+1%
  • John Nwankwo Donald

    DEF · Elche

    provisional
    Mins
    31
    Proj.
    1.26
    O 1.5
    30%
    Model /90
    3.66
    044%
    126%
    212%
    37%
    45%
    53%
    6+3%
  • Grady Diangana

    FWD · Elche

    provisional
    Mins
    47
    Proj.
    0.99
    O 1.5
    27%
    Model /90
    1.91
    042%
    131%
    217%
    37%
    42%
    51%
    6+0%
  • Lucas Cepeda

    FWD · Elche

    provisional
    Mins
    40
    Proj.
    0.89
    O 1.5
    23%
    Model /90
    2.01
    050%
    127%
    213%
    36%
    43%
    51%
    6+0%
  • Federico Redondo Solari

    MID · Elche

    provisional
    Mins
    16
    Proj.
    0.69
    O 1.5
    15%
    Model /90
    3.85
    057%
    129%
    29%
    33%
    41%
    51%
    6+1%
  • Matías Ezequiel Dituro

    GK · Elche

    provisional
    Mins
    90
    Proj.
    0.01
    O 1.5
    —
    Model /90
    0.01
    099%
    1+1%
Shots14 playersHighest: German Valera Karabinaite, 2.22 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.

  • German Valera Karabinaite

    FWD · Elche

    provisional
    Mins
    80
    Proj.
    2.22
    O 1.5
    60%
    Model /90
    2.50
    017%
    124%
    222%
    316%
    410%
    56%
    6+5%
  • José Antonio Morente Oliva

    FWD · Elche

    provisional
    Mins
    72
    Proj.
    2.17
    O 1.5
    58%
    Model /90
    2.72
    018%
    124%
    222%
    316%
    410%
    55%
    6+5%
  • Grady Diangana

    FWD · Elche

    provisional
    Mins
    47
    Proj.
    1.56
    O 1.5
    42%
    Model /90
    3.01
    031%
    127%
    219%
    311%
    46%
    53%
    6+2%
  • Lucas Cepeda

    FWD · Elche

    provisional
    Mins
    40
    Proj.
    1.43
    O 1.5
    35%
    Model /90
    3.23
    039%
    126%
    214%
    39%
    45%
    53%
    6+4%
  • Marc Aguado Pallares

    MID · Elche

    provisional
    Mins
    72
    Proj.
    1.02
    O 1.5
    27%
    Model /90
    1.28
    040%
    132%
    217%
    37%
    42%
    51%
    6+0%
  • Gonzalo Villar del Fraile

    MID · Elche

    provisional
    Mins
    60
    Proj.
    0.97
    O 1.5
    26%
    Model /90
    1.46
    045%
    129%
    215%
    37%
    43%
    51%
    6+0%
  • Martim Carvalho Neto

    MID · Elche

    provisional
    Mins
    54
    Proj.
    0.90
    O 1.5
    23%
    Model /90
    1.49
    045%
    132%
    215%
    36%
    42%
    51%
    6+0%
  • David Affengruber

    DEF · Elche

    provisional
    Mins
    82
    Proj.
    0.55
    O 1.5
    11%
    Model /90
    0.61
    059%
    130%
    29%
    32%
    4+0%
  • Pedro Bigas Rigo

    DEF · Elche

    provisional
    Mins
    74
    Proj.
    0.50
    O 1.5
    10%
    Model /90
    0.61
    062%
    128%
    28%
    32%
    4+0%
  • Víctor Chust García

    DEF · Elche

    provisional
    Mins
    70
    Proj.
    0.48
    O 1.5
    9%
    Model /90
    0.62
    064%
    126%
    27%
    32%
    4+0%
  • Aboubacar Sangaré Traoré

    DEF · Elche

    provisional
    Mins
    45
    Proj.
    0.34
    O 1.5
    6%
    Model /90
    0.67
    073%
    121%
    25%
    31%
    4+0%
  • Federico Redondo Solari

    MID · Elche

    provisional
    Mins
    16
    Proj.
    0.33
    O 1.5
    6%
    Model /90
    1.85
    075%
    119%
    24%
    31%
    40%
    5+0%
  • John Nwankwo Donald

    DEF · Elche

    provisional
    Mins
    31
    Proj.
    0.28
    O 1.5
    5%
    Model /90
    0.82
    078%
    117%
    24%
    31%
    4+0%
  • Matías Ezequiel Dituro

    GK · Elche

    provisional
    Mins
    90
    Proj.
    0.00
    O 1.5
    —
    Model /90
    0.00
    0100%
    1+0%
Shots on target14 playersHighest: German Valera Karabinaite, 0.76 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.

Fouls won14 playersHighest: German Valera Karabinaite, 1.96 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.

  • German Valera Karabinaite

    FWD · Elche

    provisional
    Mins
    80
    Proj.
    1.96
    O 1.5
    54%
    Model /90
    2.22
    020%
    126%
    222%
    315%
    49%
    54%
    6+4%
  • José Antonio Morente Oliva

    FWD · Elche

    provisional
    Mins
    72
    Proj.
    1.66
    O 1.5
    46%
    Model /90
    2.09
    025%
    129%
    222%
    313%
    47%
    53%
    6+2%
  • Gonzalo Villar del Fraile

    MID · Elche

    provisional
    Mins
    60
    Proj.
    1.34
    O 1.5
    36%
    Model /90
    2.01
    036%
    128%
    218%
    310%
    45%
    52%
    6+1%
  • Marc Aguado Pallares

    MID · Elche

    provisional
    Mins
    72
    Proj.
    1.31
    O 1.5
    36%
    Model /90
    1.64
    033%
    131%
    220%
    310%
    44%
    52%
    6+1%
  • Grady Diangana

    FWD · Elche

    provisional
    Mins
    47
    Proj.
    1.28
    O 1.5
    35%
    Model /90
    2.48
    036%
    129%
    218%
    39%
    45%
    52%
    6+1%
  • Lucas Cepeda

    FWD · Elche

    provisional
    Mins
    40
    Proj.
    1.12
    O 1.5
    28%
    Model /90
    2.53
    046%
    126%
    213%
    37%
    44%
    52%
    6+2%
  • Martim Carvalho Neto

    MID · Elche

    provisional
    Mins
    54
    Proj.
    0.98
    O 1.5
    26%
    Model /90
    1.63
    042%
    132%
    216%
    36%
    42%
    51%
    6+0%
  • David Affengruber

    DEF · Elche

    provisional
    Mins
    82
    Proj.
    0.97
    O 1.5
    25%
    Model /90
    1.06
    041%
    133%
    216%
    36%
    42%
    51%
    6+0%
  • Pedro Bigas Rigo

    DEF · Elche

    provisional
    Mins
    74
    Proj.
    0.80
    O 1.5
    20%
    Model /90
    0.97
    049%
    132%
    214%
    35%
    41%
    5+0%
  • Víctor Chust García

    DEF · Elche

    provisional
    Mins
    70
    Proj.
    0.73
    O 1.5
    18%
    Model /90
    0.94
    052%
    130%
    212%
    34%
    41%
    5+0%
  • Aboubacar Sangaré Traoré

    DEF · Elche

    provisional
    Mins
    45
    Proj.
    0.54
    O 1.5
    12%
    Model /90
    1.07
    063%
    125%
    29%
    33%
    41%
    5+0%
  • Federico Redondo Solari

    MID · Elche

    provisional
    Mins
    16
    Proj.
    0.49
    O 1.5
    10%
    Model /90
    2.70
    067%
    124%
    26%
    32%
    41%
    50%
    6+0%
  • John Nwankwo Donald

    DEF · Elche

    provisional
    Mins
    31
    Proj.
    0.43
    O 1.5
    9%
    Model /90
    1.24
    071%
    120%
    26%
    32%
    41%
    5+0%
  • Matías Ezequiel Dituro

    GK · Elche

    provisional
    Mins
    90
    Proj.
    0.16
    O 1.5
    1%
    Model /90
    0.16
    085%
    114%
    2+1%
Cards14 playersHighest: David Affengruber, 0.21 expectedshow

Known defect — Prices ANY card — yellow or straight red — matching how books settle "to be shown a card" (D-048). The trends column alongside counts yellows only.

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.

Saves14 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.