· UEFA Youth League
Benfica U19
ATT
7/10
DEF
5/10
6:0
Pafos U19
5/10
ATT
5/10
DEF
Full-time
Best Pick
Over 2.5
73%
Correct Score91′
Top
2-1
9%
19.00
1-1
7.7%
29.00
3-1
7.1%
15.00
2-2
6.5%
34.00
2-0
6.3%
13.00
1-2
5.5%
51.00
1-0
5.4%
23.00
3-2
5.1%
34.00
158%
1.73TOP
X19%
5.13
223%
4.40
1X77%
X242%
1281%
xG — Expected Goals
Benfica U19
Benfica U19 exp
Pafos U19
Pafos U19 exp
goal
per 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Benfica U19
Pafos U19
Probable XI4-4-2
Ferreira
Coelho
Queiroz
Neto
Neto
Ferreira
Figueiredo
Quintas
Stevan
Silva
Lopes
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Benfica U199 players
74.7/ 99Elite
Top Rated
1Miguel FigueiredoMID96
2Francisco SilvaFWD86
3StevanMID82
Pafos U193 players
40.7/ 99Average
Top Rated
1Paris Gregory PapastylianouFWD45
2Georgios MichaelMID41
3Ioannis IgnatiadisDEF36
Benfica U1915
PlayerOverall RatingImpactAggressionDiscipline
Miguel Figueiredo96999983
Francisco Silva86999737
Stevan82997250
Rafael Quintas77995150
Ricardo Neto75994550
José Neto74628367
Rui Pedro Ferreira da Silva74576989
Diogo José de Nazaré e Jesus Ferreira57591750
Léo Lopes51524550
Diogo Alexandre Palma Coelho————
Gabriel Queiroz————
Isaac Gonçalves Ferreira————
Nilson Semedo————
Pafos U199
PlayerOverall RatingImpactAggressionDiscipline
Paris Gregory Papastylianou45454550
Georgios Michael41393950
Ioannis Ignatiadis36343450
Andreas Andreou————
Lucca Vetter Paulino————
Nicolas Aristodemou————
Victoras Neokleous————
Constantinos Christofi————
Melinos Kais————
Player Rankings
Snipers
Top goal threats
1
Francisco SilvaBenfica U19
99
2
Léo LopesBenfica U19
52
3
Paris Gregory PapastylianouPafos U19
45
Wall
Top defenders
1
Ricardo NetoBenfica U19
99
2
José NetoBenfica U19
62
3
Rui Pedro Ferreira da SilvaBenfica U19
57
4
Ioannis IgnatiadisPafos U19
34
Control
Top playmakers
1
Miguel FigueiredoBenfica U19
99
2
Rafael QuintasBenfica U19
99
3
StevanBenfica U19
99
4
Georgios MichaelPafos U19
39
Bad Boys
Most likely to get booked
1
Francisco SilvaBenfica U19
37
2
Diogo José de Nazaré e Jesus FerreiraBenfica U19
50
3
Ricardo NetoBenfica U19
50
4
Rafael QuintasBenfica U19
50
5
StevanBenfica U19
50
Power Rankings
Strongest overall
1
Miguel FigueiredoBenfica U19
96
2
Francisco SilvaBenfica U19
86
3
StevanBenfica U19
82
4
Rafael QuintasBenfica U19
77
5
Ricardo NetoBenfica U19
75

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Benfica U19 vs Pafos U19 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 Benfica U19 vs Pafos U19 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 Benfica U19 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 Benfica U19 vs Pafos U19 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 Benfica U19 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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