· K League 2
Gimpo
ATT
5/10
DEF
5/10
1:2
Gimhae City
3/10
ATT
3/10
DEF
Fine
Migliore scelta
Vittoria in casa
58%
Risultato esatto94′
Eccellente
1-0
12.3%
7.00
Eccellente
1-1
11%
7.00
2-0
11%
8.00
2-1
9.8%
8.50
0-0
6.9%
11.00
3-0
6.5%
13.00
0-1
6.1%
12.00
3-1
5.8%
15.00
158%
1.71TOP
X23%
4.34
218%
5.41
1X81%
X241%
1276%
xG — Gol attesi
Gimpo
Gimpo att.
Gimhae City
Gimhae City att.
goal
ogni 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Gimpo
Gimhae City
Probable XI4-4-2
Yun
Jang
Lee
Ahn
Kim
Chapman
Djalma
Kim
Choi
Muhsin
Park
Impact Player Hot Head
Formazioni non ancora annunciate — analisi completa della rosa sotto
Gimpo28 giocatori
52.7/ 99Medio
Migliori
1Fabián MinaFWD95
2Luan DiasMID81
3PaulinhoFWD76
Gimhae City28 giocatori
47.3/ 99Medio
Migliori
1Min-young LeeMID80
2Jun-yeong ChoiDEF62
3Uk-hyeon HongDEF60
Gimpo36
PlayerValutazioneImpattoAggressivitàDisciplina
Fabián Mina95997071
Luan Dias81715589
Paulinho76924044
Chan-Hyung Lee69487885
Lee Hak-Min67436989
Do-hyeok Kim64585170
Kim Sung-Joon62853840
Tae-han Kim60706041
Jae-young Choi58288491
Bu-Seong Jang55425667
Jun-seo Kim55584550
Lee Si-Heon55424691
Yong-hyeok Lee54494861
Gimhae City38
PlayerValutazioneImpattoAggressivitàDisciplina
Min-young Lee80769967
Jun-yeong Choi62814840
Uk-hyeon Hong60595954
Cho Young-Kwang58545961
Yeo Jae-Yul55475561
Seol Hyeon-Jin54439758
Lee Kang-Uk53408469
Lee Rae-Jun52733434
Jun-gyu Lee51492672
Gwak Seong-Uk50604740
Jin-uk Jeong48481750
Kim Hyeon-Deok47487434
Miguel Baggio47379930
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Fabián MinaGimpo
99
2
PaulinhoGimpo
92
3
Jun-seo KimGimpo
58
4
Cho Young-KwangGimhae City
54
5
Maissa FallGimhae City
48
Muro
Migliori difensori
1
Jun-yeong ChoiGimhae City
81
2
Lee Rae-JunGimhae City
73
3
Tae-han KimGimpo
70
4
Uk-hyeon HongGimhae City
59
5
Jun-young ChaGimhae City
58
Controllo
Migliori registi
1
Kim Sung-JoonGimpo
85
2
Bruno CostaGimhae City
84
3
Min-young LeeGimhae City
76
4
Luan DiasGimpo
71
5
Gwak Seong-UkGimhae City
60
Cattivi ragazzi
Più propensi ai cartellini
1
Jun-young ChaGimhae City
1
2
Bruno CostaGimhae City
1
3
Seung-min MoonGimhae City
9
4
Gyeol KimGimpo
11
5
Kyung-rok ParkGimpo
18
Classifica potenza
Più forti in assoluto
1
Fabián MinaGimpo
95
2
Luan DiasGimpo
81
3
Min-young LeeGimhae City
80
4
PaulinhoGimpo
76
5
Chan-Hyung LeeGimpo
69

Domande frequenti

Our Poisson model calculates the probability of every possible scoreline for Gimpo vs Gimhae City 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 Gimpo vs Gimhae City 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 Gimpo 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 Gimpo vs Gimhae City 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 Gimpo 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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