· AFC Champions League
Pohang
ATT
5/10
DEF
5/10
vs
Gamba Osaka
6/10
ATT
6/10
DEF
Scheduled· Wed 4 Nov · 10:00
Best Pick
Under 2.5
53%
Correct Score
Top
1-1
12.6%
7.94
Top
1-0
10.4%
9.66
0-1
9.3%
10.75
2-1
8.5%
11.72
1-2
7.7%
13.04
0-0
7.6%
13.08
2-0
7%
14.27
0-2
5.7%
17.66
140%
2.50TOP
X26%
3.77
234%
2.98
1X66%
X260%
1274%
Probable XI4-4-2
Hwang
Park
Lee
Han
Eo
Kim
Kim
Kim
Nishiya
Jae-jun
Jung
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Pohang21 players
49.2/ 99Average
Top Rated
1Si-woo JinDEF59
2Jakob TranziskaFWD57
3Chan-yong ParkDEF56
Gamba Osaka24 players
49.0/ 99Average
Top Rated
1Yusei ToshidaFWD71
2Shuto AbeMID69
3WeltonFWD64
Pohang40
PlayerOverall RatingImpactAggressionDiscipline
Si-woo Jin59705741
Jakob Tranziska57435983
Chan-yong Park56594656
Min-kwang Jeon53485456
Sung-yueng Ki 53505356
Jorge Teixeira52494961
Sang-hyeok Cho52399967
Sung-min Hong50514550
Kwang-hoon Shin50534550
Min-jun Kang50494656
Dong-jin Kim 50504256
Kento Nishiya50455156
In-jae Hwang49494550
Gamba Osaka44
PlayerOverall RatingImpactAggressionDiscipline
Yusei Toshida71705072
Shuto Abe69625772
Welton64646556
Koshiro Sumi59635350
Juanpe58635250
Soya Fujiwara56495461
Takashi Usami56623950
Shogo Sasaki52464761
Jun Ichimori51513450
Shinnosuke Nakatani51513861
Ryoya Yamashita51456956
Shinya Nakano48434856
Issam Jebali48414967
Player Rankings
Snipers
Top goal threats
1
Yusei ToshidaGamba Osaka
70
2
WeltonGamba Osaka
64
3
Koshiro SumiGamba Osaka
63
4
Takashi UsamiGamba Osaka
62
5
Jorge TeixeiraPohang
49
Wall
Top defenders
1
Si-woo JinPohang
70
2
Chan-yong ParkPohang
59
3
Sung-wook Jo Pohang
57
4
Kwang-hoon ShinPohang
53
5
Shota FukuokaGamba Osaka
52
Control
Top playmakers
1
JuanpeGamba Osaka
63
2
Shuto AbeGamba Osaka
62
3
Tokuma SuzukiGamba Osaka
57
4
Kim Seung-HoPohang
54
5
Dong-jin Kim Pohang
50
Bad Boys
Most likely to get booked
1
Juninho RochaPohang
1
2
Takeru KishimotoGamba Osaka
1
3
Rin MitoGamba Osaka
22
4
Tokuma SuzukiGamba Osaka
27
5
Sung-wook Jo Pohang
30
Power Rankings
Strongest overall
1
Yusei ToshidaGamba Osaka
71
2
Shuto AbeGamba Osaka
69
3
WeltonGamba Osaka
64
4
Si-woo JinPohang
59
5
Koshiro SumiGamba Osaka
59

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Pohang vs Gamba Osaka 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 Pohang vs Gamba Osaka 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 Pohang 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 Pohang vs Gamba Osaka 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 Pohang 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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