· Friendly International
Peru
ATT
5/10
DEF
4/10
vs
Bolivia
4/10
ATT
4/10
DEF
Scheduled· Sat 14 Nov · 01:00
Best Pick
Home Win
55%
Correct Score
Top
1-0
11.2%
8.91
Top
1-1
11.2%
8.91
2-0
9.8%
10.22
2-1
9.8%
10.22
0-0
6.4%
15.53
0-1
6.4%
15.53
3-0
5.7%
17.59
3-1
5.7%
17.59
155%
1.82TOP
X24%
4.23
222%
4.63
1X79%
X246%
1277%
Probable XI4-4-2
12
Duarte
Corzo
Callens
Santamaría
Villacorta
14
Quiroz
Burlamaqui
3
Solar
Quesada
7
Ugarriza
20
Valera
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Peru32 players
55.0/ 99Strong
Top Rated
1Erick NoriegaMID85
2Adrián QuirozMID70
3Jesús PretellMID67
Bolivia39 players
46.5/ 99Average
Top Rated
1Marcelo TorrezDEF68
2Rai LimaMID67
3Óscar LópezMID64
Peru90
PlayerOverall RatingImpactAggressionDiscipline
8
Erick Noriega85918158
14
Adrián Quiroz70687656
6
Jesús Pretell67459989
3
Fabio Gruber66862956
20
Álex Valera66644772
19
Yoshimar Yotún 65988616
11
Jairo Vélez64387891
16
Wilder José Cartagena Mendoza64539852
5
Miguel Araujo 63734356
Kevin Quevedo63704550
17
Jairo Concha60994314
Bryan Reyna60635450
Piero Aldair Quispe Córdova59694550
Bolivia94
PlayerOverall RatingImpactAggressionDiscipline
22
Marcelo Torrez68735161
8
Rai Lima67844550
16
Óscar López64499261
5
Efraín Morales63645161
11
Fernando Nava61649138
16
Ervin Vaca58375183
14
Robson Matheus56434972
22
Leonardo Zabala55634350
21
Yomar Rocha54456556
2
Diego Arroyo 51523661
9
José Flores 51495356
3
Diego Medina50345367
9
Daniel Ribera48455356
Player Rankings
Snipers
Top goal threats
1
Kevin QuevedoPeru
70
2
Álex ValeraPeru
64
3
Fernando NavaBolivia
64
4
Bryan ReynaPeru
63
5
Yordy ReynaPeru
63
Wall
Top defenders
1
Renzo GarcesPeru
87
2
Fabio GruberPeru
86
3
Miguel Araujo Peru
73
4
Marcelo TorrezBolivia
73
5
Efraín MoralesBolivia
64
Control
Top playmakers
1
Jairo ConchaPeru
99
2
Yoshimar Yotún Peru
98
3
Erick NoriegaPeru
91
4
Rai LimaBolivia
84
5
Jesús CastilloPeru
75
Bad Boys
Most likely to get booked
1
Marcos LópezPeru
1
2
Renzo GarcesPeru
1
3
Enzo MonteiroBolivia
11
4
Jairo ConchaPeru
14
5
Ramiro Vaca Bolivia
14
Power Rankings
Strongest overall
1
Erick NoriegaPeru
85
2
Adrián QuirozPeru
70
3
Marcelo TorrezBolivia
68
4
Jesús PretellPeru
67
5
Rai LimaBolivia
67

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Peru vs Bolivia 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 Peru vs Bolivia 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 Peru 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 Peru vs Bolivia 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 Peru 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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