Benfica’s relentless wide pressure and +66% corner strength make them likely to drive the count, while Lech also win corners very often with a +30% attack edge. Their defenses may limit easy chances, but Benfica’s attacking quality should keep Lech under pressure and create repeated clearances. Over 8.5 corners looks the most likely outcome.
Corners Over/UnderOver 8.5
Best Pick
Home Win
62%
Correct Score
Top
2-0
10.2%
8.00
Top
2-1
10%
11.00
1-0
10%
12.00
1-1
9.8%
17.00
3-0
6.9%
7.50
3-1
6.8%
11.00
0-0
4.9%
29.00
2-2
4.9%
29.00
162%
1.13TOP
X21%
9.00
217%
17.00
1X83%
X238%
1279%
Combined Bet Builder
3 legs
Benfica’s 2.18 xG and +54% attack power point to sustained pressure against Lech’s modest 0.89 xG threat. That attacking edge makes a Benfica win the likeliest outcome, with Pavlidis well placed to finish and the hosts’ firepower capable of driving the match beyond 3.5 goals.
1Match WinnerSL Benfica FC
2GoalscorersAnytime
3Match GoalsOver 3.5
Confirmed XI4-5-1
1
Trubin
6
Bah
3
Circati
44
Araújo
34
Karouani
8
Aursnes
16
Silva
27
Silva
30
Echeverri
21
Schjelderup
14
Pavlidis
Impact Player Hot Head
SL Benfica FC36 players
54.1/ 99Average
Top Rated
1Manu Silva MID94
2Franjo IvanovićFWD87
3AnísioFWD86
Lech Poznan24 players
53.7/ 99Average
Top Rated
1Yannick AgneroFWD77
2Radoslaw Murawski MID74
3Mikael Ishak FWD65
Player Rankings
Snipers
Top goal threats
1
AnísioSL Benfica FC
99
2
Jhon Durán SL Benfica FC
98
3
Franjo IvanovićSL Benfica FC
84
4
Vangelis PavlidisSL Benfica FC
80
5
Mikael Ishak Lech Poznan
78
Wall
Top defenders
1
Terry YegbeLech Poznan
63
2
João MoutinhoLech Poznan
57
3
Alessandro CircatiSL Benfica FC
54
4
Wojciech MonkaLech Poznan
54
5
José NetoSL Benfica FC
52
Control
Top playmakers
1
Manu Silva SL Benfica FC
92
2
Diogo Ferreira PriosteSL Benfica FC
90
3
Fredrik AursnesSL Benfica FC
79
4
João PalhinhaSL Benfica FC
73
5
Enzo BarrenecheaSL Benfica FC
69
Bad Boys
Most likely to get booked
1
Gianluca PrestianniSL Benfica FC
1
Aggression 49
2
Jhon Durán SL Benfica FC
1
Aggression 99
3
Tomás AraújoSL Benfica FC
5
Aggression 38
4
Wojciech MonkaLech Poznan
21
Aggression 20
5
Henrique AraújoSL Benfica FC
25
Aggression 9
Power Rankings
Strongest overall
1
Manu Silva SL Benfica FC
94
2
Franjo IvanovićSL Benfica FC
87
3
AnísioSL Benfica FC
86
4
Diogo Ferreira PriosteSL Benfica FC
83
5
Yannick AgneroLech Poznan
77
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for SL Benfica FC vs Lech Poznan 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 SL Benfica FC vs Lech Poznan 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 SL Benfica FC 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 SL Benfica FC vs Lech Poznan 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 SL Benfica FC 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.