Santa Clara’s home initiative should keep pressure building, while Gil Vicente’s stable but not dominant defence leaves room for openings. Though both attacks have been subdued, the hosts’ greater attacking threat and the visitors’ chance to break forward point to a match likely to produce the goals needed for Over 1.5.
Match GoalsOver 2.5
Best Pick
Under 2.5
54%
Correct Score
Top
1-1
12.4%
6.00
Top
1-0
12%
6.00
2-1
9.1%
10.00
2-0
8.8%
9.50
0-1
8.4%
8.00
0-0
8.2%
6.50
1-2
6.4%
13.00
2-2
4.7%
19.00
147%
2.25TOP
X26%
3.10
227%
3.40
1X73%
X253%
1274%
Combined Bet Builder
3 legs
Santa Clara’s home advantage and sturdier defence (18% stronger than average) make the hosts-or-draw the logical anchor against Gil Vicente’s weak attack. Combined xG is 1.98, supporting two or more goals, while projected corners total 8.19—above the 7.5 line. The 2.75 builder fits the matchup profile.
1Double ChanceSanta Clara or Draw
2Match GoalsOver 2.5
3Corners Over/UnderOver 7.5
Confirmed XI4-3-3
12
Neneca
42
Soares
4
Pacheco
21
Fernandes
5
Romão
41
Borges
20
Ribeiro
8
Tavares
70
Lopes
7
Fernando
10
Brenner
Impact Player Hot Head
Santa Clara26 players
47.0/ 99Average
Top Rated
1Henrique SilvaDEF73
2Pedro Pacheco DEF65
3Gonçalo PaciênciaFWD63
Gil Vicente28 players
53.3/ 99Average
Top Rated
1Tidjany Chabrol TouréFWD93
2Diogo Ferreira PriosteMID83
3José SilvaDEF70
Player Rankings
Snipers
Top goal threats
1
Tidjany Chabrol TouréGil Vicente
99
2
Gonçalo PaciênciaSanta Clara
80
3
Héctor HernándezGil Vicente
67
4
Sergio BermejoGil Vicente
63
5
Walace dos Santos FrançaGil Vicente
62
Wall
Top defenders
1
Henrique SilvaSanta Clara
80
2
Antonio Espigares MorillasGil Vicente
75
3
Pedro Pacheco Santa Clara
68
4
Sidney LimaSanta Clara
61
5
Emanuel FernandesSanta Clara
54
Control
Top playmakers
1
Diogo Ferreira PriosteGil Vicente
91
2
Zé CarlosGil Vicente
67
3
Guilherme GomesGil Vicente
59
4
Andreas LausenGil Vicente
57
5
HiagoSanta Clara
56
Bad Boys
Most likely to get booked
1
Emanuel FernandesSanta Clara
1
Aggression 29
2
Daniel BorgesSanta Clara
1
Aggression 58
3
Tiago RibeiroSanta Clara
1
Aggression 43
4
VasconcelosSanta Clara
1
Aggression 51
5
Luiz Fernando Salles de CastroSanta Clara
1
Aggression 99
Power Rankings
Strongest overall
1
Tidjany Chabrol TouréGil Vicente
93
2
Diogo Ferreira PriosteGil Vicente
83
3
Henrique SilvaSanta Clara
73
4
José SilvaGil Vicente
70
5
Sergio BermejoGil Vicente
68
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Santa Clara vs Gil Vicente 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 Santa Clara vs Gil Vicente 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 Santa Clara 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 Santa Clara vs Gil Vicente 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 Santa Clara 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.