· Liga Portugal 2
Penafiel
ATT
5/10
DEF
6/10
vs
Lusitania FC
7/10
ATT
5/10
DEF
Scheduled· Sun 15 Nov · 15:30
Best Pick
Under 2.5
55%
Correct Score
Top
1-1
12.6%
6.50
Top
1-0
12%
7.50
2-1
9%
9.50
0-1
8.9%
8.50
0-0
8.5%
9.00
2-0
8.5%
11.00
1-2
6.6%
11.00
2-2
4.7%
15.00
145%
2.20TOP
X27%
3.20
228%
2.90
1X72%
X255%
1273%
Probable XI4-4-2
12
Rojas
6
Pica
3
Silva
7
Borel
23
Oliveira
8
Carbonell
85
Augusto
50
Varela
Kébé
Millán
99
Mendes
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Penafiel31 players
49.5/ 99Average
Top Rated
1João Miguel DEF75
2Clau Mendes FWD75
3Douglas BorelDEF73
Lusitania FC28 players
56.4/ 99Strong
Top Rated
1Dylan CollardDEF79
2Miguel TeixeiraMID74
3André RicardoMID70
Penafiel43
PlayerOverall RatingImpactAggressionDiscipline
4
João Miguel 75954455
99
Clau Mendes 75903450
7
Douglas Borel73639954
Joseph Séry66667452
8
Reko63646254
Ibrahima Kébé60449957
11
Juan David Fuentes Garrido60654550
79
Tomas Teixeira59459951
2
Iano Simão58437856
32
Joan Femenías5759950
38
Mairlon Oliveira56399651
10
Davo52544549
14
Jorge Meré51416553
Lusitania FC31
PlayerOverall RatingImpactAggressionDiscipline
4
Dylan Collard79995153
88
Miguel Teixeira74757657
10
André Ricardo70718749
Francisco Canário70679955
27
Fabinho68785156
7
Micael Filipe Correia Borges67716550
17
João Santos66773547
42
Guilherme Soares61695250
29
Brian Araújo60639751
16
Luís Rocha59449549
21
Paulité58692544
Josué Humberto Gonçalves Leal de Sá57634255
60
Daniel56506652
Player Rankings
Snipers
Top goal threats
1
Clau Mendes Penafiel
90
2
João SantosLusitania FC
77
3
Micael Filipe Correia BorgesLusitania FC
71
4
PaulitéLusitania FC
69
5
Francisco CanárioLusitania FC
67
Wall
Top defenders
1
Dylan CollardLusitania FC
99
2
João Miguel Penafiel
95
3
Guilherme SoaresLusitania FC
69
4
Douglas BorelPenafiel
63
5
Josué Humberto Gonçalves Leal de SáLusitania FC
63
Control
Top playmakers
1
FabinhoLusitania FC
78
2
Miguel TeixeiraLusitania FC
75
3
André RicardoLusitania FC
71
4
RekoPenafiel
64
5
Francisco VarelaPenafiel
58
Bad Boys
Most likely to get booked
1
Àlex CarbonellPenafiel
42
2
Cláudio SilvaPenafiel
43
3
Teddy AllohPenafiel
43
4
Bruno FariaLusitania FC
43
5
Jaime SánchezPenafiel
44
Power Rankings
Strongest overall
1
Dylan CollardLusitania FC
79
2
João Miguel Penafiel
75
3
Clau Mendes Penafiel
75
4
Miguel TeixeiraLusitania FC
74
5
Douglas BorelPenafiel
73

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Penafiel vs Lusitania FC 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 Penafiel vs Lusitania FC 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 Penafiel 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 Penafiel vs Lusitania FC 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 Penafiel 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.

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