Ferencvaros carry the stronger attacking platform, with a 2.14 xG pointing to sustained pressure and enough chances to drive the total upward. Torreense’s sturdy defense is a hurdle, but their low attack power may leave them under pressure for long stretches; as the game opens up, three goals look a likely outcome in this Europa League clash.
Match GoalsOver 2.5
Best Pick
BTTS
53%
Correct Score
Top
1-1
12.3%
9.50
Top
1-0
10.3%
8.00
2-1
8.9%
9.00
0-1
8.4%
23.00
2-0
7.5%
7.00
1-2
7.4%
26.00
0-0
7.1%
15.00
2-2
5.3%
19.00
143%
1.33TOP
X26%
5.00
231%
9.00
1X69%
X257%
1274%
Combined Bet Builder
3 legs
Ferencvaros are the logical side to control this tie: their 2.12 full-match xG dwarfs Torreense’s 0.56, while 7.94 projected corners point to sustained pressure. A 0.78 first-half xG supports an early lead; despite Torreense’s sturdy defence, Ferencvaros’ dominance should drive the win and create enough chances for over 2.5 goals.
1Match WinnerFerencvaros TC
2Match GoalsOver 2.5
3First Half WinnerFerencvaros TC
Confirmed XI2-5-3
63
Radnóti
4
Gómez
28
Raemaekers
14
Osváth
20
Cadu
16
Zachariassen
6
Nagy
17
Corbu
7
Birmancevic
76
Lisztes
11
Bamidele
Impact Player Hot Head
Ferencvaros TC43 players
53.0/ 99Average
Top Rated
1Jonathan LeviFWD75
2Toon RaemaekersDEF73
3Gábor SzalaiDEF72
Torreense29 players
37.7/ 99Developing
Top Rated
1DaniloDEF71
2StopiraDEF60
3Lucas PaesGK57
Player Rankings
Snipers
Top goal threats
1
Jonathan LeviFerencvaros TC
69
2
Elton AcolatseFerencvaros TC
65
3
Musa DrammehTorreense
64
4
Kévin ZohiTorreense
58
5
Aleksandar PesicFerencvaros TC
56
Wall
Top defenders
1
Gábor SzalaiFerencvaros TC
77
2
Toon RaemaekersFerencvaros TC
70
3
Endre BotkaFerencvaros TC
68
4
Mariano GómezFerencvaros TC
68
5
Csongor LakatosFerencvaros TC
66
Control
Top playmakers
1
Mohammed Abu FaniFerencvaros TC
91
2
CostinhaTorreense
63
3
Gabi KanichowskyFerencvaros TC
57
4
Philippe RommensFerencvaros TC
52
5
Ádám NagyFerencvaros TC
51
Bad Boys
Most likely to get booked
1
Callum O'DowdaFerencvaros TC
1
Aggression 51
2
Alejandro AlfaroTorreense
1
Aggression 29
3
Andre SimõesTorreense
1
Aggression 45
4
Dany JeanTorreense
1
Aggression 30
5
Kévin ZohiTorreense
1
Aggression 20
Power Rankings
Strongest overall
1
Jonathan LeviFerencvaros TC
75
2
Toon RaemaekersFerencvaros TC
73
3
Gábor SzalaiFerencvaros TC
72
4
Nathan Zohoré Ferencvaros TC
72
5
DaniloTorreense
71
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Ferencvaros TC vs Torreense 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 Ferencvaros TC vs Torreense 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 Ferencvaros TC 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 Ferencvaros TC vs Torreense 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 Ferencvaros TC 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.