Team comparison
Metrics, head-to-head meetings and a statistical outcome estimate for any two teams side by side.
Form · 10 matches
W
D
W
L
W
D
L
D
L
D
13 of 30
5
league position
VS
10 games
Form · 10 matches
D
D
L
W
L
W
L
L
L
W
11 of 30
6
league position
Attack
Average per match in the sample
1.3
Goals
per match
1.1
0.0
Shots on target
per match
0.0
0.0
Corners
per match
0.0
Possession and passing
Average per match in the sample
0
Possession
% за матч
0
0
Accurate passes
per match
0
Discipline
Lower is better
0.0
Fouls
per match
0.0
0.0
Yellow cards
per match
0.0
Defense
Lower is better
1.4
Goals conceded
per match
1.5
Personal Meetings
GUM ahead
draws
SEO ahead
24.07.2026
Seoul W
0 : 3
Gumi Sportstoto W
31.05.2026
Gumi Sportstoto W
3 : 1
Seoul W
17.04.2026
Seoul W
2 : 1
Gumi Sportstoto W
29.09.2025
Seoul W
2 : 0
Gumi Sportstoto W
21.08.2025
Gumi Sportstoto W
2 : 1
Seoul W
22.05.2025
Seoul W
1 : 1
Gumi Sportstoto W
17.04.2025
Gumi Sportstoto W
0 : 1
Seoul W
12.09.2024
Seoul W
0 : 1
Gumi Sportstoto W
05.07.2024
Gumi Sportstoto W
0 : 0
Seoul W
20.05.2024
Seoul W
2 : 2
Gumi Sportstoto W
Statistical estimate
1/X/2 by the Poisson model based on average goals
1X2
The away team scores an average of 1.2 goals per match — more than the home team (1.1).
The home team concedes an average of 1.1 goals per match — fewer than the opponent (1.6).
Estimate — a statistical Poisson model based on average goals, not an AI-model prediction.
This is not an AI model prediction — it's a simple statistical estimate based on average figures, without accounting for lineups, injuries or opponents' form.