· K3 League
Yangpyeong FC
ATT
6/10
DEF
1/10
vs
Ulsan Citizen FC
5/10
ATT
6/10
DEF
Naplánováno· Fri 9 Oct · 05:00
Nejlepší tip
OBÁ SKÓRUJÍ
54%
Přesný výsledek
Výborná
1-1
11.8%
8.45
Výborná
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
Sestavy ještě nebyly oznámeny — níže kompletní analýza kádru
Yangpyeong FC17 hráči
50.2/ 99Průměrný
Nejlepší
1DenzelFWD67
2Yun Sang-EunDEF61
3Kim Dong-UkFWD54
Ulsan Citizen FC11 hráči
48.6/ 99Průměrný
Nejlepší
1Kim Tae-HwanDEF59
2Kim Jae-CheolFWD54
3Shin Seong-HakDEF53
Yangpyeong FC27
PlayerCelkové HodnoceníImpaktAgreseDisciplína
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 Citizen FC13
PlayerCelkové HodnoceníImpaktAgreseDisciplína
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————
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
DenzelYangpyeong FC
74
2
Kim Jae-CheolUlsan Citizen FC
57
3
Kim Dong-UkYangpyeong FC
55
4
BrennoYangpyeong FC
48
5
Ki-Joon LeeUlsan Citizen FC
48
Zeď
Nejlepší obránci
1
Kim Tae-HwanUlsan Citizen FC
74
2
Shin Seong-HakUlsan Citizen FC
62
3
Jeong Su-HwanYangpyeong FC
58
4
Kim Ki-YoungUlsan Citizen FC
54
5
Cha In-SeokYangpyeong FC
51
Kontrola
Nejlepší tvůrci hry
1
Dong-Heui LeeYangpyeong FC
60
2
Dong-hyuk JangYangpyeong FC
47
3
Lee Tae-HyungYangpyeong FC
45
4
Yun Dae-WonUlsan Citizen FC
45
5
Se-Yoon CheonYangpyeong FC
44
Zlobivci
Nejpravděpodobnější žlutá karta
1
Dahniel ParkYangpyeong FC
50
2
Jae-Min ShinYangpyeong FC
50
3
Cha In-SeokYangpyeong FC
50
4
Hyeong-Kyeom KimYangpyeong FC
50
5
Jeong Su-HwanYangpyeong FC
50
Žebříček síly
Nejsilnější celkově
1
DenzelYangpyeong FC
67
2
Yun Sang-EunYangpyeong FC
61
3
Kim Tae-HwanUlsan Citizen FC
59
4
Kim Dong-UkYangpyeong FC
54
5
Kim Jae-CheolUlsan Citizen FC
54

Frequently Asked Questions

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