PER90

La Liga · Spain

OsasunavGetafe

Estadio El Sadarlineups 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.

  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 v20260815-1512 · Tackles v20260815-1512 · Shots v20260815-1512 · Fouls won v20260815-1512 · Cards v20260815-1512 · Saves v20260815-1512

Model projections

Fouls40 playersHighest: Mario Martín, 1.95 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.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Mario MartínMIDGetafe2.8761provisional1.9555%30
018%
127%
224%
316%
49%
54%
6+2%
Lucas TorróMIDOsasuna1.9776provisional1.6849%65
021%
130%
225%
315%
47%
52%
6+1%
Christantus UcheFWDGetafe1.9477provisional1.6649%35
021%
131%
225%
314%
46%
52%
6+1%
DavinchiDEFGetafe2.6650provisional1.4741%10
029%
130%
220%
311%
46%
52%
6+1%
Mauro ArambarriMIDGetafe1.5782provisional1.4442%68
025%
133%
224%
312%
45%
51%
6+1%
Ante BudimirFWDOsasuna1.5679provisional1.3739%71
027%
133%
223%
311%
44%
51%
6+0%
Adrián LisoFWDGetafe1.8661provisional1.2636%22
034%
130%
220%
310%
44%
51%
6+0%
Enzo BoyomoDEFOsasuna1.2686provisional1.2134%63
031%
135%
221%
39%
43%
51%
6+0%
Alejandro CatenaDEFOsasuna1.2288provisional1.1933%70
031%
136%
221%
39%
43%
51%
6+0%
Jon MoncayolaMIDOsasuna1.4176provisional1.1833%62
032%
134%
221%
39%
43%
51%
6+0%

10 of 40 players shown, ranked by projection. Show all 40 →

Tackles40 playersHighest: Diego Rico, 1.28 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.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Diego RicoDEFGetafe1.5375provisional1.2835%53
034%
130%
219%
310%
44%
52%
6+1%
Zaid RomeroDEFGetafe1.2190provisional1.2133%15
033%
134%
220%
39%
43%
51%
6+0%
Enzo BoyomoDEFOsasuna1.2486provisional1.1933%63
033%
134%
220%
39%
43%
51%
6+0%
Mauro ArambarriMIDGetafe1.2082provisional1.1030%68
037%
134%
218%
38%
43%
51%
6+0%
Valentin RosierDEFOsasuna1.1382provisional1.0428%30
038%
134%
218%
37%
42%
51%
6+0%
DavinchiDEFGetafe1.8250provisional1.0126%10
043%
131%
215%
37%
43%
51%
6+0%
Kiko FemeníaDEFGetafe1.2272provisional0.9826%27
040%
134%
217%
36%
42%
51%
6+0%
Mario MartínMIDGetafe1.4261provisional0.9625%30
042%
133%
216%
36%
42%
51%
6+0%
Jon MoncayolaMIDOsasuna1.1476provisional0.9625%62
042%
133%
216%
36%
42%
51%
6+0%
Lucas TorróMIDOsasuna1.1176provisional0.9424%65
042%
134%
216%
36%
42%
50%
6+0%

10 of 40 players shown, ranked by projection. Show all 40 →

Shots40 playersHighest: Ante Budimir, 2.61 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.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Ante BudimirFWDOsasuna2.9879provisional2.6170%71
011%
120%
222%
319%
413%
58%
6+7%
Martín SatrianoFWDGetafe1.9089provisional1.8855%18
017%
128%
226%
316%
48%
53%
6+2%
Christantus UcheFWDGetafe1.7477provisional1.4943%35
025%
132%
223%
312%
45%
52%
6+1%
Mauro ArambarriMIDGetafe1.4282provisional1.3037%68
030%
134%
222%
310%
44%
51%
6+0%
Aimar OrozMIDOsasuna1.4376provisional1.2034%62
033%
134%
220%
39%
43%
51%
6+0%
Adrián LisoFWDGetafe1.4661provisional1.0027%22
042%
131%
217%
37%
42%
51%
6+0%
Ramón TerratsMIDGetafe1.0782provisional0.9826%15
039%
136%
218%
36%
42%
5+0%
Borja MayoralFWDGetafe1.5354provisional0.9224%26
045%
131%
215%
36%
42%
51%
6+0%
Rubén GarcíaFWDOsasuna1.2166provisional0.8923%62
044%
134%
216%
35%
41%
5+0%
Raúl MoroFWDOsasuna1.7845provisional0.8823%8
047%
130%
214%
36%
42%
51%
6+0%

10 of 40 players shown, ranked by projection. Show all 40 →

Fouls won40 playersHighest: Aimar Oroz, 2.28 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.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Aimar OrozMIDOsasuna2.7176provisional2.2863%62
014%
123%
223%
318%
411%
56%
6+4%
Christantus UcheFWDGetafe2.4777provisional2.1160%35
015%
125%
225%
317%
410%
55%
6+3%
Martín SatrianoFWDGetafe1.7289provisional1.7150%18
020%
130%
225%
315%
47%
53%
6+1%
Mauro ArambarriMIDGetafe1.8682provisional1.7049%68
021%
130%
224%
314%
47%
53%
6+1%
Diego RicoDEFGetafe1.7375provisional1.4541%53
029%
130%
221%
312%
45%
52%
6+1%
Ramón TerratsMIDGetafe1.5782provisional1.4341%15
025%
133%
223%
311%
44%
51%
6+1%
Adrián LisoFWDGetafe2.1061provisional1.4340%22
031%
129%
220%
312%
45%
52%
6+1%
Abdel AbqarDEFGetafe1.9463provisional1.3538%19
031%
131%
221%
311%
45%
52%
6+1%
Álex SolaFWDGetafe2.3152provisional1.3437%15
032%
130%
220%
310%
45%
52%
6+1%
Ante BudimirFWDOsasuna1.3479provisional1.1833%71
033%
134%
220%
39%
43%
51%
6+0%

10 of 40 players shown, ranked by projection. Show all 40 →

Cards40 playersHighest: Mario Martín, 0.31 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.

PlayerTeamModel /90Exp. minsProjectedOver 0.5nDistribution
Mario MartínMIDGetafe0.4661provisional0.3126%30
074%
122%
24%
3+0%
Dakonam DjenéDEFGetafe0.3279provisional0.2825%63
075%
121%
23%
3+0%
Alejandro CatenaDEFOsasuna0.2988provisional0.2824%70
076%
121%
23%
3+0%
Abdel AbqarDEFGetafe0.4063provisional0.2824%19
076%
120%
23%
3+0%
Domingos DuarteDEFGetafe0.2486provisional0.2320%55
080%
118%
22%
3+0%
Lucas TorróMIDOsasuna0.2776provisional0.2320%65
080%
118%
22%
3+0%
Juan BerrocalDEFGetafe0.3456provisional0.2119%15
081%
116%
22%
3+0%
Enzo BoyomoDEFOsasuna0.2186provisional0.2018%63
082%
117%
22%
3+0%
Mauro ArambarriMIDGetafe0.2282provisional0.2018%68
082%
116%
22%
3+0%
Jon MoncayolaMIDOsasuna0.2376provisional0.2018%62
082%
116%
22%
3+0%
DavinchiDEFGetafe0.3450provisional0.1917%10
083%
115%
22%
3+0%
Valentin RosierDEFOsasuna0.2082provisional0.1816%30
084%
115%
2+1%
Iker MuñozMIDOsasuna0.4437provisional0.1816%21
084%
114%
22%
3+0%
Christantus UcheFWDGetafe0.2177provisional0.1816%35
084%
115%
2+1%
Zaid RomeroDEFGetafe0.1790provisional0.1716%15
084%
115%
2+1%
Martín SatrianoFWDGetafe0.1789provisional0.1716%18
084%
114%
2+1%
Jorge HerrandoDEFOsasuna0.2365provisional0.1715%36
085%
114%
21%
3+0%
Aimar OrozMIDOsasuna0.1976provisional0.1615%62
085%
114%
2+1%
Diego RicoDEFGetafe0.1975provisional0.1615%53
085%
113%
2+1%
Adrián LisoFWDGetafe0.2361provisional0.1614%22
086%
113%
2+1%
Allan NyomDEFGetafe0.3440provisional0.1514%19
086%
112%
21%
3+0%
Kiko FemeníaDEFGetafe0.1972provisional0.1514%27
086%
113%
2+1%
Sergio HerreraGKOsasuna0.1589provisional0.1514%74
086%
113%
2+1%
Álex SolaFWDGetafe0.2452provisional0.1413%15
087%
112%
2+1%
Ramón TerratsMIDGetafe0.1582provisional0.1312%15
088%
112%
2+1%
Rubén GarcíaFWDOsasuna0.1866provisional0.1312%62
088%
111%
2+1%
Abel BretonesDEFOsasuna0.1959provisional0.1211%45
089%
110%
2+1%
JuanmiFWDGetafe0.2741provisional0.1211%13
089%
110%
2+1%
Álex SancrisFWDGetafe0.2839provisional0.1211%11
089%
110%
2+1%
Ante BudimirFWDOsasuna0.1479provisional0.1211%71
089%
111%
2+1%
Sebastián BoselliDEFGetafe0.3134provisional0.1210%4
090%
19%
21%
3+0%
Coba da CostaFWDGetafe0.2341provisional0.1110%20
090%
19%
2+1%
Kike BarjaFWDOsasuna0.4918provisional0.109%8
091%
18%
2+1%
Asier OsambelaMIDOsasuna0.3723provisional0.109%4
091%
18%
2+1%
Moi GómezMIDOsasuna0.1939provisional0.088%22
092%
17%
2+0%
Javi MuñozMIDGetafe0.1933provisional0.077%6
093%
16%
2+0%
Raúl MoroFWDOsasuna0.1245provisional0.066%8
094%
16%
2+0%
Raúl GarcíaFWDOsasuna0.1826provisional0.055%19
095%
15%
2+0%
Borja MayoralFWDGetafe0.0854provisional0.055%26
095%
15%
2+0%
David SoriaGKGetafe0.0190provisional0.011%76
099%
1+1%

All 40 players shown, ranked by projection. Show fewer ↥

Saves2 playersHighest: David Soria, 2.93 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.

PlayerTeamModel /90Exp. minsProjectedOver 2.5nDistribution
David SoriaGKGetafe2.9390provisional2.9354%76
07%
117%
222%
320%
415%
59%
6+10%
Sergio HerreraGKOsasuna2.6289provisional2.6047%74
09%
120%
224%
320%
413%
57%
6+6%

1 of 7 markets have no projection for this fixture.