Villarreal’s potent home attack should drive the tempo, while defensive vulnerability gives Getafe a route into the contest. That blend of pressure and openings makes a high-scoring affair, clearing the goals line, the compelling expectation.
Match GoalsOver 2.5
Best Pick
Home Win
66%
Correct Score
Top
2-0
12.1%
7.00
Top
1-0
11.8%
7.00
2-1
9.8%
8.50
1-1
9.5%
7.50
3-0
8.3%
11.00
3-1
6.7%
12.00
0-0
5.7%
11.00
0-1
4.6%
17.00
166%
1.53TOP
X20%
4.10
214%
6.25
1X86%
X234%
1280%
Combined Bet Builder
4 legs
Villarreal’s sharper attack should keep Getafe pinned back, with Ayoze Pérez well placed to make the pressure count. Getafe’s solid defence may frustrate them early, but the hosts look capable of breaking through, taking control and driving a lively, high-scoring contest. Sustained pressure from both sides should also bring plenty of corners.
1GoalscorersAnytime
2Match WinnerVillarreal CF
3Match GoalsOver 2.5
4Corners Over/UnderOver 8.5
Confirmed XI4-5-1
25
Gulácsi
3
Freeman
6
Navarro
12
Veiga
23
Cardona
14
Comesaña
18
Gueye
19
Pépé
10
Moleiro
17
Buchanan
9
Mikautadze
Impact Player Hot Head
Villarreal CF30 players
49.4/ 99Average
Top Rated
1Nathan-Dylan SalibaMID74
2Pau NavarroDEF70
3Santi ComesañaMID67
Getafe CF31 players
43.3/ 99Average
Top Rated
1Javi MuñozMID69
2J. ValouDEF62
3Saba Sazonov DEF62
Player Rankings
Snipers
Top goal threats
1
Georges MikautadzeVillarreal CF
66
2
Alberto MoleiroVillarreal CF
54
3
Gerard Moreno Villarreal CF
46
4
José Luis PérezGetafe CF
46
5
Pau CabanesVillarreal CF
45
Wall
Top defenders
1
Juan BerrocalGetafe CF
74
2
Zaid RomeroGetafe CF
71
3
J. ValouGetafe CF
70
4
Juan FoythVillarreal CF
68
5
Renato VeigaVillarreal CF
68
Control
Top playmakers
1
Carlos MaciàVillarreal CF
75
2
Pape GueyeVillarreal CF
73
3
Óscar LópezGetafe CF
73
4
Santi ComesañaVillarreal CF
58
5
Nathan-Dylan SalibaVillarreal CF
57
Bad Boys
Most likely to get booked
1
Juan FoythVillarreal CF
1
Aggression 43
2
Thiago FernándezVillarreal CF
1
Aggression 53
3
Djené Getafe CF
1
Aggression 37
4
Johan MojicaGetafe CF
1
Aggression 36
5
Sebastián BoselliGetafe CF
1
Aggression 99
Power Rankings
Strongest overall
1
Nathan-Dylan SalibaVillarreal CF
74
2
Pau NavarroVillarreal CF
70
3
Javi MuñozGetafe CF
69
4
Santi ComesañaVillarreal CF
67
5
Santiago MouriñoVillarreal CF
66
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Villarreal CF vs Getafe CF based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.
The Bet Builder combines multiple selections from the Villarreal CF vs Getafe CF match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.
xG (expected goals) measures the quality of scoring chances. If Villarreal CF has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).
Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Villarreal CF vs Getafe CF without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.
A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Villarreal CF a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.
AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.