· Liga 1
Universitario de Deportes
ATT
5/10
DEF
6/10
3:0
Juan Pablo II
5/10
ATT
3/10
DEF
Full-time
Best Pick
Home Win
53%
Correct Score94′
Top
1-0
11.6%
8.00
Top
1-1
11.6%
10.00
2-1
9.7%
8.50
2-0
9.6%
7.00
0-0
6.9%
17.00
0-1
6.9%
23.00
1-2
5.8%
23.00
3-0
5.4%
9.00
153%
1.89TOP
X24%
4.11
223%
4.41
1X77%
X247%
1276%
xG — Expected Goals
Universitario de Deportes
Universitario de Deportes exp
Juan Pablo II
Juan Pablo II exp
goal
per 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Universitario de Deportes
Juan Pablo II
Confirmed XI3-4-3
1
Romero
2
Fara
3
Riveros
4
Santamaría
24
Polo
10
Calcaterra
17
Concha
33
Inga
19
Flores
18
Lapadula
20
Valera
Impact Player Hot Head
Universitario de Deportes30 players
53.8/ 99Average
Top Rated
1Jairo ConchaMID89
2Jesús CastilloMID80
3Álex ValeraFWD79
Juan Pablo II31 players
50.4/ 99Average
Top Rated
1José Aron Sánchez FloresDEF88
2Iago IriarteDEF87
3Fabio Eduardo Agurto MendozaDEF74
Player Rankings
Snipers
Top goal threats
1
Álex ValeraUniversitario de Deportes
78
2
Lisandro AlzugarayUniversitario de Deportes
63
3
Janio PósitoJuan Pablo II
62
4
Mateo RodríguezJuan Pablo II
59
5
José RiveraUniversitario de Deportes
56
Wall
Top defenders
1
José Aron Sánchez FloresJuan Pablo II
90
2
Iago IriarteJuan Pablo II
87
3
Williams Riveros Universitario de Deportes
71
4
Paolo FuentesJuan Pablo II
69
5
Pablo MíguezJuan Pablo II
65
Control
Top playmakers
1
Jairo ConchaUniversitario de Deportes
95
2
Jesús CastilloUniversitario de Deportes
90
3
Adrián QuirozUniversitario de Deportes
80
4
Horacio CalcaterraUniversitario de Deportes
79
5
Piero Aldair Quispe CórdovaUniversitario de Deportes
74
Bad Boys
Most likely to get booked
1
Horacio CalcaterraUniversitario de Deportes
1
2
Yuriel CeliUniversitario de Deportes
1
3
Matías AlmirónJuan Pablo II
1
4
Christian Alberto Cueva BravoJuan Pablo II
1
5
Juan Manuel RequenaUniversitario de Deportes
4
Power Rankings
Strongest overall
1
Jairo ConchaUniversitario de Deportes
89
2
José Aron Sánchez FloresJuan Pablo II
88
3
Iago IriarteJuan Pablo II
87
4
Jesús CastilloUniversitario de Deportes
80
5
Álex ValeraUniversitario de Deportes
79

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Universitario de Deportes vs Juan Pablo II 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 Universitario de Deportes vs Juan Pablo II 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 Universitario de Deportes 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 Universitario de Deportes vs Juan Pablo II 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 Universitario de Deportes 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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