· AFC Champions League Elite
Pohang Steelers
ATT
5/10
DEF
5/10
vs
Gamba Osaka
6/10
ATT
6/10
DEF
Planlandı· Wed 4 Nov · 10:00
En iyi tahmin
Alt 2.5
53%
Doğru skor
Mükemmel
1-1
12.6%
7.94
Mükemmel
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
Kadrolar henüz açıklanmadı — aşağıda tam kadro analizi
Pohang Steelers21 oyuncu
49.2/ 99Orta
En İyiler
1Si-woo JinDEF59
2Jakob TranziskaFWD57
3Chan-yong ParkDEF56
Gamba Osaka24 oyuncu
49.0/ 99Orta
En İyiler
1Yusei ToshidaFWD71
2Shuto AbeMID69
3WeltonFWD64
Pohang Steelers40
PlayerGenel DereceEtkiAgresyonDisiplin
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
PlayerGenel DereceEtkiAgresyonDisiplin
Yusei Toshida71705072
Shuto Abe69625772
Welton64646556
Koshiro Sumi59635350
Juanpe58635250
Soya Fujiwara56495461
Takashi Usami56623950
Shogo Sasaki52464761
Jun Ichimori51513450
Shinnosuke Nakatani51513861
Ryoya Yamashita51456956
Shinya Nakano48434856
Issam Jebali48414967
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
Yusei ToshidaGamba Osaka
70
2
WeltonGamba Osaka
64
3
Koshiro SumiGamba Osaka
63
4
Takashi UsamiGamba Osaka
62
5
Jorge TeixeiraPohang Steelers
49
Duvar
En iyi savunmacılar
1
Si-woo JinPohang Steelers
70
2
Chan-yong ParkPohang Steelers
59
3
Sung-wook Jo Pohang Steelers
57
4
Kwang-hoon ShinPohang Steelers
53
5
Shota FukuokaGamba Osaka
52
Kontrol
En iyi oyun kurucular
1
JuanpeGamba Osaka
63
2
Shuto AbeGamba Osaka
62
3
Tokuma SuzukiGamba Osaka
57
4
Kim Seung-HoPohang Steelers
54
5
Dong-jin Kim Pohang Steelers
50
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Juninho RochaPohang Steelers
1
2
Takeru KishimotoGamba Osaka
1
3
Rin MitoGamba Osaka
22
4
Tokuma SuzukiGamba Osaka
27
5
Sung-wook Jo Pohang Steelers
30
Güç sıralaması
Genel olarak en güçlüler
1
Yusei ToshidaGamba Osaka
71
2
Shuto AbeGamba Osaka
69
3
WeltonGamba Osaka
64
4
Si-woo JinPohang Steelers
59
5
Koshiro SumiGamba Osaka
59

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Pohang Steelers 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 Steelers 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 Steelers 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 Steelers 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 Steelers 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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