· AFC Champions League Elite
Pohang Steelers
ATT
5/10
DEF
5/10
vs
Johor Darul Ta'zim
4/10
ATT
5/10
DEF
Запланирован· Tue 13 Oct · 10:00
Лучший выбор
ОБЕ ЗАБЬЮТ
53%
Точный счёт
Отличная
1-1
11.7%
8.54
Отличная
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
Составы ещё не объявлены — полный анализ игроков ниже
Pohang Steelers12 игроков
50.5/ 99Средний
Лучшие
1Si-woo JinDEF60
2Jakob TranziskaFWD58
3Jorge TeixeiraFWD58
Johor Darul Ta'zim27 игроков
55.7/ 99Сильный
Лучшие
1Jon IrazábalDEF92
2Celso BermejoFWD90
3Natxo InsaMID77
Pohang Steelers40
PlayerОбщий РейтингВлияниеАгрессияДисциплина
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 Ta'zim46
PlayerОбщий РейтингВлияниеАгрессияДисциплина
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
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
BergsonJohor Darul Ta'zim
99
2
Celso BermejoJohor Darul Ta'zim
99
3
Manuel HidalgoJohor Darul Ta'zim
57
4
Jorge TeixeiraPohang Steelers
54
5
Arif Aiman HanapiJohor Darul Ta'zim
54
Стена
Лучшие защитники
1
Jon IrazábalJohor Darul Ta'zim
99
2
Shahrul SaadJohor Darul Ta'zim
63
3
Eddy İsrafilovJohor Darul Ta'zim
61
4
Cristian GlauderJohor Darul Ta'zim
59
5
Shane LowryJohor Darul Ta'zim
58
Контроль
Лучшие плеймейкеры
1
Natxo InsaJohor Darul Ta'zim
69
2
Dong-jin Kim Pohang Steelers
54
3
Kim Seung-HoPohang Steelers
53
4
Dejan PetrovicJohor Darul Ta'zim
52
5
Hector HevelJohor Darul Ta'zim
51
Хулиганы
Склонны к карточкам
1
BergsonJohor Darul Ta'zim
1
2
Juninho RochaPohang Steelers
10
3
Eddy İsrafilovJohor Darul Ta'zim
12
4
S. ZahediJohor Darul Ta'zim
21
5
Sung-wook Jo Pohang Steelers
31
Рейтинг силы
Сильнейшие в целом
1
Jon IrazábalJohor Darul Ta'zim
92
2
Celso BermejoJohor Darul Ta'zim
90
3
Natxo InsaJohor Darul Ta'zim
77
4
BergsonJohor Darul Ta'zim
70
5
Nacho MéndezJohor Darul Ta'zim
62

Часто задаваемые вопросы

Our Poisson model calculates the probability of every possible scoreline for Pohang Steelers vs Johor Darul Ta'zim 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 Johor Darul Ta'zim 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 Johor Darul Ta'zim 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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