· K3 League
Yangpyeong
ATT
6/10
DEF
1/10
vs
Ulsan Citize
5/10
ATT
6/10
DEF
Запланирован· Fri 9 Oct · 05:00
Лучший выбор
ОБЕ ЗАБЬЮТ
54%
Точный счёт
Отличная
1-1
11.8%
8.45
Отличная
1-0
10.4%
9.65
2-1
9.4%
10.65
2-0
8.2%
12.16
0-1
7.5%
13.40
1-2
6.8%
14.79
0-0
6.5%
15.30
2-2
5.4%
18.65
148%
2.10TOP
X25%
4.01
227%
3.65
1X73%
X252%
1275%
Probable XI4-4-2
Park
In-Seok
Kim
Kim
Su-Hwan
Lee
Jang
Kim
Hwang
Brenno
Denzel
Impact Player Hot Head
Составы ещё не объявлены — полный анализ игроков ниже
Yangpyeong17 игроков
50.2/ 99Средний
Лучшие
1DenzelFWD67
2Yun Sang-EunDEF61
3Kim Dong-UkFWD54
Ulsan Citize11 игроков
48.6/ 99Средний
Лучшие
1Kim Tae-HwanDEF59
2Kim Jae-CheolFWD54
3Shin Seong-HakDEF53
Yangpyeong27
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Denzel67745150
Yun Sang-Eun61508750
Kim Dong-Uk54555150
Cha In-Seok53515950
Jeong Su-Hwan53584450
Se-Yoon Cheon51446850
Dong-Heui Lee50602950
Dong-hyuk Jang50475650
Dahniel Park49483950
Jae-Min Shin49493450
Hyeong-Kyeom Kim48474850
Brenno48484350
Kang Sung-Hwa46454750
Ulsan Citize13
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Kim Tae-Hwan59743650
Kim Jae-Cheol54574550
Shin Seong-Hak53623650
Kim Ki-Young51544650
Ki-Joon Lee49485350
Lee Seon-Il4847950
Min-jae Kang48475150
Lee Han-Sae47386350
Sang-Hyun Park46464550
Kim Dong-Yun41393950
Yun Dae-Won39451550
Kim Yong-Min————
Dong-Hyuk Park————
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
DenzelYangpyeong
74
2
Kim Jae-CheolUlsan Citize
57
3
Kim Dong-UkYangpyeong
55
4
BrennoYangpyeong
48
5
Ki-Joon LeeUlsan Citize
48
Стена
Лучшие защитники
1
Kim Tae-HwanUlsan Citize
74
2
Shin Seong-HakUlsan Citize
62
3
Jeong Su-HwanYangpyeong
58
4
Kim Ki-YoungUlsan Citize
54
5
Cha In-SeokYangpyeong
51
Контроль
Лучшие плеймейкеры
1
Dong-Heui LeeYangpyeong
60
2
Dong-hyuk JangYangpyeong
47
3
Lee Tae-HyungYangpyeong
45
4
Yun Dae-WonUlsan Citize
45
5
Se-Yoon CheonYangpyeong
44
Хулиганы
Склонны к карточкам
1
Dahniel ParkYangpyeong
50
2
Jae-Min ShinYangpyeong
50
3
Cha In-SeokYangpyeong
50
4
Hyeong-Kyeom KimYangpyeong
50
5
Jeong Su-HwanYangpyeong
50
Рейтинг силы
Сильнейшие в целом
1
DenzelYangpyeong
67
2
Yun Sang-EunYangpyeong
61
3
Kim Tae-HwanUlsan Citize
59
4
Kim Dong-UkYangpyeong
54
5
Kim Jae-CheolUlsan Citize
54

Часто задаваемые вопросы

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