· K3 League
Ulsan Citize
ATT
3/10
DEF
6/10
vs
Siheung Citizen
7/10
ATT
7/10
DEF
Запланирован· Sat 3 Oct · 08:00
Лучший выбор
Меньше 2.5
58%
Точный счёт
Отличная
0-1
14.3%
7.00
Отличная
1-1
12.2%
7.50
0-2
10.7%
8.50
0-0
9.6%
9.00
1-2
9.1%
10.00
1-0
8.1%
12.00
0-3
5.3%
13.00
2-1
5.2%
17.00
121%
4.00
X26%
3.40
252%
1.73TOP
1X47%
X278%
1273%
Probable XI4-4-2
Seon-Il
Ki-Young
Tae-Hwan
Yong-Min
Seong-Hak
Dong-Yun
Han-Sae
Dae-Won
Lee
Jae-Cheol
Kang
Impact Player Hot Head
Составы ещё не объявлены — полный анализ игроков ниже
Ulsan Citize11 игроков
49.5/ 99Средний
Лучшие
1Kim Tae-HwanDEF67
2Kim Jae-CheolFWD54
3Shin Seong-HakDEF53
Siheung Citizen13 игроков
50.2/ 99Средний
Лучшие
1Ahn Ji-HoDEF59
2Min-young LeeMID57
3Joo Hyun-SungGK53
Ulsan Citize13
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Kim Tae-Hwan67854550
Kim Jae-Cheol54574450
Shin Seong-Hak53623650
Kim Ki-Young51544650
Lee Seon-Il5049950
Ki-Joon Lee49485350
Min-jae Kang48475150
Lee Han-Sae47386350
Sang-Hyun Park46464550
Kim Dong-Yun41393950
Yun Dae-Won38421950
Kim Yong-Min————
Dong-Hyuk Park————
Siheung Citizen19
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Ahn Ji-Ho59527750
Min-young Lee57409950
Joo Hyun-Sung5354950
Min-Ho Jo53535450
Kwak Seung-Jo53459950
Kim Dong-Geon52534550
Kim Tae-Heon52544850
Yu-Min Seo52592350
Wan-kyu Kwon50622750
Chang-hwan Oh46464550
Hyung-Jin Park45454250
Lee Seok Hyun41374050
Min-hyeon Kong39364150
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Yu-Min SeoSiheung Citizen
59
2
Kim Jae-CheolUlsan Citize
57
3
Kim Tae-HeonSiheung Citizen
54
4
Ki-Joon LeeUlsan Citize
48
5
Min-jae KangUlsan Citize
47
Стена
Лучшие защитники
1
Kim Tae-HwanUlsan Citize
85
2
Shin Seong-HakUlsan Citize
62
3
Wan-kyu KwonSiheung Citizen
62
4
Kim Ki-YoungUlsan Citize
54
5
Ahn Ji-HoSiheung Citizen
52
Контроль
Лучшие плеймейкеры
1
Min-Ho JoSiheung Citizen
53
2
Chang-hwan OhSiheung Citizen
46
3
Yun Dae-WonUlsan Citize
42
4
Min-young LeeSiheung Citizen
40
5
Kim Dong-YunUlsan Citize
39
Хулиганы
Склонны к карточкам
1
Lee Seon-IlUlsan Citize
50
2
Kim Ki-YoungUlsan Citize
50
3
Kim Tae-HwanUlsan Citize
50
4
Shin Seong-HakUlsan Citize
50
5
Kim Dong-YunUlsan Citize
50
Рейтинг силы
Сильнейшие в целом
1
Kim Tae-HwanUlsan Citize
67
2
Ahn Ji-HoSiheung Citizen
59
3
Min-young LeeSiheung Citizen
57
4
Kim Jae-CheolUlsan Citize
54
5
Shin Seong-HakUlsan Citize
53

Часто задаваемые вопросы

Our Poisson model calculates the probability of every possible scoreline for Ulsan Citize vs Siheung Citizen 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 Ulsan Citize vs Siheung Citizen 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 Ulsan Citize 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 Ulsan Citize vs Siheung Citizen 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 Ulsan Citize 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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