· AFC Champions League
Pohang
ATT
5/10
DEF
5/10
vs
Johor Darul Takzim
4/10
ATT
5/10
DEF
Scheduled· Tue 13 Oct · 10:00
Best Pick
BTTS
53%
Correct Score
Top
1-1
11.7%
8.54
Top
1-0
11%
9.07
2-1
9.6%
10.45
2-0
9%
11.10
0-1
7.2%
13.95
0-0
6.7%
14.82
1-2
6.2%
16.08
3-1
5.2%
19.19
151%
1.97TOP
X25%
4.06
225%
4.05
1X76%
X250%
1276%
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
Pohang12 players
50.5/ 99Average
Top Rated
1Si-woo JinDEF60
2Jakob TranziskaFWD58
3Jorge TeixeiraFWD58
Johor Darul Takzim27 players
55.7/ 99Strong
Top Rated
1Jon IrazábalDEF92
2Celso BermejoFWD90
3Natxo InsaMID77
Pohang40
PlayerOverall RatingImpactAggressionDiscipline
Si-woo Jin60575261
Jakob Tranziska58505672
Jorge Teixeira58545961
Kento Nishiya53495456
Kim Ye-Sung51534850
Dong-jin Kim 51544550
Kwang-hoon Shin50534550
Kim Seung-Ho50534550
Pyeong-guk Yun48484550
Chang-woo Lee47464750
Sung-wook Jo 44555231
Juninho Rocha36475010
In-jae Hwang————
Johor Darul Takzim46
PlayerOverall RatingImpactAggressionDiscipline
Jon Irazábal92998756
Celso Bermejo90997656
Natxo Insa77697872
Bergson7099301
Nacho Méndez62504283
Jairo da Silva61529664
Cristian Glauder60596154
João Figueiredo60515778
Arif Aiman Hanapi58544967
Manuel Hidalgo57576650
Yago César56497161
Shahrul Saad55634550
Syihan Hazmi52533950
Player Rankings
Snipers
Top goal threats
1
BergsonJohor Darul Takzim
99
2
Celso BermejoJohor Darul Takzim
99
3
Manuel HidalgoJohor Darul Takzim
57
4
Jorge TeixeiraPohang
54
5
Arif Aiman HanapiJohor Darul Takzim
54
Wall
Top defenders
1
Jon IrazábalJohor Darul Takzim
99
2
Shahrul SaadJohor Darul Takzim
63
3
Eddy İsrafilovJohor Darul Takzim
61
4
Cristian GlauderJohor Darul Takzim
59
5
Shane LowryJohor Darul Takzim
58
Control
Top playmakers
1
Natxo InsaJohor Darul Takzim
69
2
Dong-jin Kim Pohang
54
3
Kim Seung-HoPohang
53
4
Dejan PetrovicJohor Darul Takzim
52
5
Hector HevelJohor Darul Takzim
51
Bad Boys
Most likely to get booked
1
BergsonJohor Darul Takzim
1
2
Juninho RochaPohang
10
3
Eddy İsrafilovJohor Darul Takzim
12
4
S. ZahediJohor Darul Takzim
21
5
Sung-wook Jo Pohang
31
Power Rankings
Strongest overall
1
Jon IrazábalJohor Darul Takzim
92
2
Celso BermejoJohor Darul Takzim
90
3
Natxo InsaJohor Darul Takzim
77
4
BergsonJohor Darul Takzim
70
5
Nacho MéndezJohor Darul Takzim
62

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Pohang vs Johor Darul Takzim 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 Johor Darul Takzim 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 Johor Darul Takzim 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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