· Mexico Liga MX
Puebla
ATT
3/10
DEF
3/10
vs
Pumas UNAM
6/10
ATT
6/10
DEF
Planlandı· Sat 31 Oct · 03:00

AI Maç Analizi

Pumas’ sharper attack should keep Puebla’s fragile defense under pressure, while the hosts’ home threat offers a route into the contest. With chances expected at both ends, momentum points toward an open, goal-rich Liga MX encounter.

Match GoalsOver 2.5
En iyi tahmin
KG VAR
52%
Doğru skor
Mükemmel
1-1
12.3%
7.00
Mükemmel
0-1
11%
10.00
1-2
9.1%
10.00
1-0
8.4%
12.00
0-2
8.1%
13.00
0-0
7.5%
13.00
2-1
6.9%
11.00
2-2
5.1%
12.00
129%
2.75
X26%
3.40
245%
2.40TOP
1X55%
X271%
1274%

Kombine

4 seçim

Pumas’ 1.58 xG and solid attack give them the edge, while Puebla’s fragile defense should offer chances for the visitors to avoid defeat. Their combined 2.69 xG supports over 1.5 goals and a first-half breakthrough; expected corners total 10, making over 8.5 a logical fit.

1Double ChanceDraw or Pumas UNAM
2Match GoalsOver 1.5
3Corners Over/UnderOver 8.5
4First Half Over/UnderOver 0.5
Confirmed XI4-5-1
28
Gutiérrez
12
Moreno
5
Almada
4
Vargas
7
Monárrez
24
Organista
14
Armenta
27
Garnica
10
Tomás
8
Moreno
9
Hernández
Impact Player Hot Head
Puebla38 oyuncu
50.2/ 99Orta
En İyiler
1Miguel RamírezMID71
2José Pachuca DEF70
3Ángelo AraosMID69
Pumas UNAM31 oyuncu
49.7/ 99Orta
En İyiler
1Sebastián CórdovaMID67
2Luciano HerreraFWD67
3Israel Luna LópezMID62
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
Edgar GuerraPuebla
70
2
Guillermo Martínez AyalaPumas UNAM
70
3
Luifer Hernández Puebla
66
4
Luciano HerreraPumas UNAM
62
5
Emiliano GómezPuebla
61
Duvar
En iyi savunmacılar
1
Tony LeonePumas UNAM
78
2
Eduardo Ramsés Navarro AgramontPuebla
74
3
Juan Pablo VargasPuebla
73
4
Ángel Amhed Azuaje GarcíaPumas UNAM
71
5
Facundo AlmadaPuebla
70
Kontrol
En iyi oyun kurucular
1
Adalberto Eliécer Carrasquilla AlcázarPumas UNAM
69
2
Pedro Jeampierre Vite UcaPumas UNAM
59
3
Miguel RamírezPuebla
57
4
Mathías TomásPuebla
53
5
Alejandro OrganistaPuebla
51
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Ignacio Maestro PuchPuebla
1
2
César GarzaPumas UNAM
1
3
Ángel LeyvaPuebla
5
4
Ángel Amhed Azuaje GarcíaPumas UNAM
7
5
Heriberto JuradoPuebla
9
Güç sıralaması
Genel olarak en güçlüler
1
Miguel RamírezPuebla
71
2
José Pachuca Puebla
70
3
Ángelo AraosPuebla
69
4
Sebastián CórdovaPumas UNAM
67
5
Luciano HerreraPumas UNAM
67

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Puebla vs Pumas UNAM 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 Pumas UNAM 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 Pumas UNAM 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.

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