· Friendly International
Colombia
ATT
5/10
DEF
5/10
vs
Chile
6/10
ATT
5/10
DEF
Scheduled· Sat 14 Nov · 00:00
Best Pick
BTTS
55%
Correct Score
Top
1-1
11.8%
8.48
Top
1-0
10.2%
9.84
2-1
9.4%
10.67
2-0
8.1%
12.37
0-1
7.4%
13.48
1-2
6.8%
14.62
0-0
6.4%
15.64
2-2
5.4%
18.39
147%
2.11TOP
X25%
4.02
228%
3.60
1X72%
X253%
1275%
Probable XI4-4-2
12
Quintana
4
Mosquera
Román
Cuesta
Borja
14
Puerta
10
Rodríguez
16
Lerma
14
Solís
11
Durán
26
Gómez
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Colombia38 players
60.8/ 99Strong
Top Rated
1Richard RíosMID88
2Davinson SánchezDEF84
3Johan Carbonero FWD83
Chile33 players
57.4/ 99Strong
Top Rated
1Igor LichnovskyDEF80
2Felipe FaúndezDEF78
3Gabriel SuazoDEF74
Colombia64
PlayerOverall RatingImpactAggressionDiscipline
6
Richard Ríos88899573
23
Davinson Sánchez84935272
18
Johan Carbonero 83994550
5
Kevin Castaño81757172
8
Jorge Carrascal80749972
9
Jhon Córdoba79816367
20
Juan Fernando Quintero77994156
10
James Rodríguez75993356
14
Jhon Solís74657567
14
Gustavo Puerta72999922
20
Kevin Viveros71667667
2
Daniel Muñoz70489991
15
Juan Portilla67666061
Chile99
PlayerOverall RatingImpactAggressionDiscipline
13
Igor Lichnovsky80867556
2
Felipe Faúndez78687072
17
Gabriel Suazo74589580
7
Lucas Cepeda69648967
3
Guillermo Maripán67685067
19
Nils Reichmuth67557067
16
Gonzalo Tapia67698844
10
Marcelino Núñez66586467
4
Iván Román65826337
11
Darío Osorio65548078
14
Víctor Felipe Méndez64774750
8
Felipe Loyola62476072
13
Benjamín Kuscevic 61624961
Player Rankings
Snipers
Top goal threats
1
Johan Carbonero Colombia
99
2
Jhon CórdobaColombia
81
3
Cucho HernándezColombia
73
4
Gonzalo TapiaChile
69
5
Kevin ViverosColombia
66
Wall
Top defenders
1
Davinson SánchezColombia
93
2
Igor LichnovskyChile
86
3
Iván RománChile
82
4
Willer DittaColombia
74
5
Yerry MinaColombia
70
Control
Top playmakers
1
Gustavo PuertaColombia
99
2
James RodríguezColombia
99
3
Juan Fernando QuinteroColombia
99
4
Richard RíosColombia
89
5
Jefferson LermaColombia
87
Bad Boys
Most likely to get booked
1
Yáser AsprillaColombia
1
2
Matías Sepúlveda Chile
14
3
Vicente Tomás Pizarro DurcudoyChile
14
4
Leandro HernándezChile
19
5
Jonathan VillagraChile
21
Power Rankings
Strongest overall
1
Richard RíosColombia
88
2
Davinson SánchezColombia
84
3
Johan Carbonero Colombia
83
4
Kevin CastañoColombia
81
5
Jorge CarrascalColombia
80

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Colombia 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 Colombia 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 Colombia 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 Colombia 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 Colombia 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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