Puebla’s porous backline invites pressure, while Monterrey’s stronger chance creation should keep the contest stretched. Both defenses look vulnerable, and that open-game script makes the total climbing beyond the required mark appear the natural outcome.
Match GoalsOver 2.5
Лучший выбор
Меньше 2.5
53%
Точный счёт
Отличная
1-1
12.6%
7.00
Отличная
1-0
9.9%
13.00
0-1
9.8%
8.00
2-1
8.2%
15.00
1-2
8.1%
8.50
0-0
7.6%
12.00
2-0
6.4%
23.00
0-2
6.3%
9.00
137%
4.20TOP
X27%
3.80
236%
1.80
1X64%
X263%
1273%
Экспресс
3 выборов
Monterrey’s stronger attacking threat should set the tempo, while Puebla’s leaky defence invites pressure; yet the hosts have enough at home to strike back. With both back lines looking vulnerable, the match appears poised to open up into goals at either end, and sustained attacks should keep the corner count climbing beyond the line.
1Both Teams to ScoreYes
2Match GoalsOver 2.5
3Corners Over/UnderOver 8.5
Confirmed XI4-5-1
28
Gutiérrez
12
Moreno
5
Almada
4
Vargas
7
Monárrez
24
Organista
16
Ramírez
27
Garnica
10
Tomás
8
Moreno
9
Hernández
Impact Player Hot Head
Puebla39 игроков
50.4/ 99Средний
Лучшие
1Miguel RamírezMID75
2José Pachuca DEF72
3Ángelo AraosMID68
Monterrey32 игроков
53.8/ 99Средний
Лучшие
1Orbelín PinedaMID80
2Luca OrellanoFWD75
3Óliver TorresMID70
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Edgar GuerraPuebla
70
2
Joaquin MoxicaMonterrey
69
3
Luca OrellanoMonterrey
69
4
Roberto Carlos de la Rosa GonzálezMonterrey
65
5
Luifer Hernández Puebla
60
Стена
Лучшие защитники
1
Juan Pablo VargasPuebla
73
2
Facundo AlmadaPuebla
71
3
Eduardo Ramsés Navarro AgramontPuebla
70
4
Nicolás DíazPuebla
69
5
Víctor GuzmánMonterrey
65
Контроль
Лучшие плеймейкеры
1
Orbelín PinedaMonterrey
81
2
Óliver TorresMonterrey
80
3
Sergio CanalesMonterrey
66
4
Miguel RamírezPuebla
63
5
Iker FimbresMonterrey
52
Хулиганы
Склонны к карточкам
1
Nicolás DíazPuebla
1
Агрессия 56
2
Ignacio Maestro PuchPuebla
1
Агрессия 83
3
Carlos SalcedoMonterrey
1
Агрессия 41
4
César GarzaMonterrey
1
Агрессия 58
5
Heriberto JuradoPuebla
8
Агрессия 99
Рейтинг силы
Сильнейшие в целом
1
Orbelín PinedaMonterrey
80
2
Miguel RamírezPuebla
75
3
Luca OrellanoMonterrey
75
4
José Pachuca Puebla
72
5
Óliver TorresMonterrey
70
Часто задаваемые вопросы
Our Poisson model calculates the probability of every possible scoreline for Puebla vs Monterrey 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 Puebla vs Monterrey 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 Puebla 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 Puebla vs Monterrey 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 Puebla 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.