· Friendly International
Mexico
ATT
4/10
DEF
6/10
vs
Chile
5/10
ATT
4/10
DEF
Planlandı· Wed 7 Oct · 02:30
En iyi tahmin
KG VAR
53%
Doğru skor
Mükemmel
1-1
12.3%
8.15
Mükemmel
1-0
10.8%
9.27
2-1
9.1%
10.98
0-1
8.3%
12.10
2-0
8%
12.48
0-0
7.3%
13.76
1-2
7%
14.34
2-2
5.2%
19.31
145%
2.21TOP
X26%
3.88
229%
3.45
1X71%
X255%
1274%
Probable XI4-4-2
Padilla
Gómez
Oliván
Montes
Campillo
Cervantes
Gutiérrez
Fidalgo
Gutiérrez
Vega
Sepúlveda
Impact Player Hot Head
Kadrolar henüz açıklanmadı — aşağıda tam kadro analizi
Mexico55 oyuncu
56.1/ 99Güçlü
En İyiler
1Armando GonzálezFWD84
2Érik LiraMID81
3Israel ReyesDEF80
Chile36 oyuncu
56.6/ 99Güçlü
En İyiler
1Clemente MontesFWD83
2Lucas CepedaFWD82
3Felipe FaúndezDEF78
Mexico67
PlayerGenel DereceEtkiAgresyonDisiplin
Armando González84993161
Érik Lira81769970
Israel Reyes80679991
Germán Berterame76736572
Guillermo Martínez Ayala74892850
José Raúl Rangel Aguilar73789656
Johan Vásquez 72538591
Obed Vargas71596972
Brian Gutiérrez70645872
L. Chávez70805656
Luis Gabriel Rey68538261
Isaías Violante67636767
Orbelín Pineda65496572
Chile99
PlayerGenel DereceEtkiAgresyonDisiplin
Clemente Montes83994550
Lucas Cepeda82817572
Felipe Faúndez78687072
Marcelino Núñez71597572
Gabriel Suazo69529074
Guillermo Maripán67685067
Nils Reichmuth67567067
Iván Román66836337
Víctor Felipe Méndez65765350
Darío Osorio65548178
Gonzalo Tapia64747931
Francisco Sierralta63704956
Felipe Loyola63486072
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
Armando GonzálezMexico
99
2
Clemente MontesChile
99
3
Guillermo Martínez AyalaMexico
89
4
Lucas CepedaChile
81
5
Raúl JiménezMexico
76
Duvar
En iyi savunmacılar
1
Iván RománChile
83
2
Cesar MontesMexico
73
3
Francisco SierraltaChile
70
4
Felipe FaúndezChile
68
5
Guillermo MaripánChile
68
Kontrol
En iyi oyun kurucular
1
Luis RomoMexico
97
2
L. ChávezMexico
80
3
Érik LiraMexico
76
4
Víctor Felipe MéndezChile
76
5
Álvaro FidalgoMexico
70
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Jorge SánchezMexico
1
2
Edson ÁlvarezMexico
1
3
Cesar MontesMexico
3
4
Diego LainezMexico
3
5
Carlos RodríguezMexico
13
Güç sıralaması
Genel olarak en güçlüler
1
Armando GonzálezMexico
84
2
Clemente MontesChile
83
3
Lucas CepedaChile
82
4
Érik LiraMexico
81
5
Israel ReyesMexico
80

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Mexico vs Chile 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 Mexico vs Chile 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 Mexico 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 Mexico vs Chile 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 Mexico 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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