Villa’s solid attacking edge and Fulham’s slightly vulnerable defensive profile create a compelling path to a lively contest. Fulham’s resilience may slow the opening, but sustained pressure and counterattacking openings should gradually unlock the goals needed for an over.
Match GoalsOver 2.5
Migliore scelta
Vittoria in casa
56%
Risultato esatto
Eccellente
1-1
11%
8.00
Eccellente
1-0
11%
10.00
2-1
9.8%
9.50
2-0
9.8%
12.00
0-1
6.2%
15.00
0-0
6.2%
15.00
3-1
5.8%
17.00
3-0
5.8%
19.00
156%
1.91TOP
X23%
3.80
221%
3.60
1X79%
X244%
1277%
Combinata
3 selezioni
Aston Villa’s 1.60 xG and +13% attack strength give them a solid platform to drive the scoring, while their slightly vulnerable defence offers Fulham a route to contribute. Both teams to score and over 2.5 goals look aligned with that open script; combined expected corners of 9.99 also support over 8.5 corners.
1Match GoalsOver 2.5
2Both Teams to ScoreYes
3Corners Over/UnderOver 8.5
Confirmed XI4-5-1
1
Suzuki
2
Cash
3
Lindelöf
14
Torres
22
Maatsen
8
Kamara
35
Gomes
47
Alysson
44
Manzambi
7
McGinn
11
Jackson
Impact Player Hot Head
Aston Villa32 giocatori
47.5/ 99Medio
Migliori
1Ibrahim MbayeFWD74
2Ross Barkley MID66
3Ollie WatkinsFWD64
Fulham25 giocatori
49.6/ 99Medio
Migliori
1KevinFWD81
2David AffengruberDEF75
3Kenny TeteDEF66
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Ibrahim MbayeAston Villa
77
2
KevinFulham
75
3
Ollie WatkinsAston Villa
73
4
Nicolas JacksonAston Villa
62
5
Rodrigo MunizFulham
58
Muro
Migliori difensori
1
Jorge CuencaFulham
80
2
Joachim AndersenFulham
72
3
Matteo RuggeriAston Villa
64
4
David AffengruberFulham
59
5
Tyrone MingsAston Villa
58
Controllo
Migliori registi
1
Tom Cairney Fulham
76
2
Alex IwobiFulham
63
3
Ross Barkley Aston Villa
57
4
Shea Emmanuel CharlesFulham
55
5
João GomesAston Villa
54
Cattivi ragazzi
Più propensi ai cartellini
1
Nicolas JacksonAston Villa
1
Aggressività 29
2
Jorge CuencaFulham
1
Aggressività 53
3
Lamare BogardeAston Villa
4
Aggressività 75
4
Alejandro GarnachoAston Villa
12
Aggressività 25
5
AlyssonAston Villa
12
Aggressività 49
Classifica potenza
Più forti in assoluto
1
KevinFulham
81
2
David AffengruberFulham
75
3
Ibrahim MbayeAston Villa
74
4
Ross Barkley Aston Villa
66
5
Kenny TeteFulham
66
Domande frequenti
Our Poisson model calculates the probability of every possible scoreline for Aston Villa vs Fulham 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 Aston Villa vs Fulham 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 Aston Villa 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 Aston Villa vs Fulham 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 Aston Villa 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.