Team comparison
Metrics, head-to-head meetings and a statistical outcome estimate for any two teams side by side.
Form · 10 matches
W
W
W
D
W
D
D
L
D
L
16 of 30
2
league position
VS
10 games
Form · 10 matches
D
W
L
W
D
W
W
D
D
L
16 of 30
3
league position
Attack
Average per match in the sample
2.0
Goals
per match
1.7
0.0
Shots on target
per match
1.5
0.0
Corners
per match
2.5
Possession and passing
Average per match in the sample
0
Possession
% за матч
30
0
Accurate passes
per match
248
Discipline
Lower is better
0.0
Fouls
per match
8.0
0.0
Yellow cards
per match
1.5
Defense
Lower is better
0.9
Goals conceded
per match
1.3
Personal Meetings
NOM ahead
draws
PAI ahead
28.06.2026
Kalju Nomme
1 : 1
Paide
14.06.2026
Kalju Nomme
1 : 1
Paide
19.04.2026
Paide
1 : 0
Kalju Nomme
14.02.2026
Paide
2 : 1
Kalju Nomme
08.11.2025
Kalju Nomme
1 : 1
Paide
14.09.2025
Paide
1 : 1
Kalju Nomme
28.05.2025
Kalju Nomme
0 : 2
Paide
09.03.2025
Paide
4 : 0
Kalju Nomme
05.03.2025
Kalju Nomme
1 : 0
Paide
06.10.2024
Paide
1 : 1
Kalju Nomme
Statistical estimate
1/X/2 by the Poisson model based on average goals
1X2
The home team scores an average of 2.2 goals per match — more than the opponent (2.1).
The away team concedes an average of 1.1 goals per match — fewer than the home team (1.2).
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.