· Liga Portugal 2
Torreense
ATT
6/10
DEF
5/10
vs
Amarante
4/10
ATT
5/10
DEF
Scheduled· Mon 26 Oct · 20:45
Best Pick
Under 2.5
55%
Correct Score
Top
1-0
13.2%
6.00
Top
1-1
11.9%
7.00
2-0
10.5%
7.50
2-1
9.4%
8.50
0-0
8.3%
8.50
0-1
7.5%
11.00
3-0
5.5%
13.00
1-2
5.3%
15.00
154%
1.70TOP
X25%
3.30
221%
4.33
1X79%
X246%
1275%
Probable XI4-4-2
Adriel
Agbor
Luengo
Danilo
Bruno
Alfaro
Oudrhiri
Simões
Reina
Baradji
Omoregbe
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Torreense30 players
36.9/ 99Developing
Top Rated
1DaniloDEF71
2StopiraDEF59
3Lucas PaesGK57
Amarante21 players
48.4/ 99Average
Top Rated
1Simao SilvaFWD96
2Leandro FerreiraFWD59
3Diogo Miguel Pereira CostaDEF54
Torreense40
PlayerOverall RatingImpactAggressionDiscipline
Danilo71578067
Stopira59622287
Lucas Paes5759950
Musa Drammeh52632834
Manuel Pozo Guerrero51532760
Joel Sebastián Romero46454550
Kévin Zohi46621515
David Bruno45343178
Costinha45633227
Vando Félix45454550
Brian Agbor44463849
Mohamed Ali Diadié44562941
Javi Vázquez41313764
Amarante30
PlayerOverall RatingImpactAggressionDiscipline
Simao Silva96999978
Leandro Ferreira59515972
Diogo Miguel Pereira Costa54565250
Feliciano Mendes52535349
Faissal Zangre51514356
Rodrigo Guedes51399934
Rúben Alves5192601
Tokinho51266670
Goncalo Cunha50552761
Luís Machado48396561
Diogo Fernandes45433950
Alemão45454550
Kenneth Ikugar45454550
Player Rankings
Snipers
Top goal threats
1
Simao SilvaAmarante
99
2
Musa DrammehTorreense
63
3
Kévin ZohiTorreense
62
4
Manuel Pozo GuerreroTorreense
53
5
Leandro FerreiraAmarante
51
Wall
Top defenders
1
StopiraTorreense
62
2
DaniloTorreense
57
3
Mohamed Ali DiadiéTorreense
56
4
Diogo Miguel Pereira CostaAmarante
56
5
Goncalo CunhaAmarante
55
Control
Top playmakers
1
Rúben AlvesAmarante
92
2
CostinhaTorreense
63
3
Faissal ZangreAmarante
51
4
Joel Sebastián RomeroTorreense
45
5
Zé BrenoTorreense
39
Bad Boys
Most likely to get booked
1
Alejandro AlfaroTorreense
1
2
Andre SimõesTorreense
1
3
Dany JeanTorreense
1
4
Rúben AlvesAmarante
1
5
Leo AzevedoTorreense
13
Power Rankings
Strongest overall
1
Simao SilvaAmarante
96
2
DaniloTorreense
71
3
StopiraTorreense
59
4
Leandro FerreiraAmarante
59
5
Lucas PaesTorreense
57

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Torreense vs Amarante 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 Torreense vs Amarante 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 Torreense 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 Torreense vs Amarante 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 Torreense 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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