· Liga Portugal 2
Portimonense
ATT
4/10
DEF
5/10
vs
Academica
7/10
ATT
5/10
DEF
Scheduled· Sun 15 Nov · 14:00
Best Pick
Under 2.5
58%
Correct Score
Top
1-1
13.1%
6.00
Top
0-1
11.8%
9.00
1-0
10.6%
7.50
0-0
9.5%
8.50
1-2
8.1%
12.00
2-1
7.3%
9.50
0-2
7.3%
15.00
2-0
5.9%
10.00
133%
2.10
X28%
3.20
239%
3.10TOP
1X61%
X267%
1272%
Probable XI4-4-2
Friedrich
2
Edney
5
Tunguliyadi
Heitor
4
Cabelinho
20
Okereke
Jesús
25
Filho
80
Favero
7
Djassi
11
Cabral
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Portimonense32 players
48.1/ 99Average
Top Rated
1Francisco CanárioFWD70
2João ReisDEF66
3MamedeFWD65
Academica24 players
42.0/ 99Average
Top Rated
1Rui GomesFWD83
2Afonso de Oliveira RodriguesFWD67
3Leandro SilvaMID66
Portimonense44
PlayerOverall RatingImpactAggressionDiscipline
Francisco Canário70679955
17
João Reis66519554
18
Mamede65696250
Mesaque Dju65638153
11
Eduardo Silva62665252
2
Edney59645350
73
Marlon Junior58565657
19
Midana Cassamá58596149
23
Sébastien Cibois5658950
Heitor54506549
6
Samy53673544
1
Dudu5253950
21
Yanis da Rocha49554147
Academica33
PlayerOverall RatingImpactAggressionDiscipline
23
Rui Gomes83993950
19
Afonso de Oliveira Rodrigues67639952
5
Leandro Silva66971943
13
Kauan Conceicao Ferreira da Silva64597454
10
I. Hassaine56603752
9
Kévin Zohi47551645
9
Martim Tavares4755950
44
Pontus Texel46493751
4
Guilherme41433450
20
Jean Sinisterra41374349
98
Carlos Alves40381750
60
João Oliveira39305149
11
Edson Farias38353848
Player Rankings
Snipers
Top goal threats
1
Rui GomesAcademica
99
2
MamedePortimonense
69
3
Francisco CanárioPortimonense
67
4
Eduardo SilvaPortimonense
66
5
Mesaque DjuPortimonense
63
Wall
Top defenders
1
SamyPortimonense
67
2
EdneyPortimonense
64
3
Kauan Conceicao Ferreira da SilvaAcademica
59
4
Marlon JuniorPortimonense
56
5
João ReisPortimonense
51
Control
Top playmakers
1
Leandro SilvaAcademica
97
2
Yanis da RochaPortimonense
55
3
Welat CagroPortimonense
49
4
Ericson Araújo da Costa FilhoPortimonense
45
5
FaveroPortimonense
45
Bad Boys
Most likely to get booked
1
Leandro SilvaAcademica
43
2
Paulinho MotaPortimonense
44
3
SamyPortimonense
44
4
Kévin ZohiAcademica
45
5
XavierPortimonense
46
Power Rankings
Strongest overall
1
Rui GomesAcademica
83
2
Francisco CanárioPortimonense
70
3
Afonso de Oliveira RodriguesAcademica
67
4
João ReisPortimonense
66
5
Leandro SilvaAcademica
66

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Portimonense vs Academica 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 Portimonense vs Academica 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 Portimonense 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 Portimonense vs Academica 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 Portimonense 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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