Tukums 0-2 Riga
Model accuracy
Retrospective model estimate · generated after the match, not in the track recordThere was no estimate locked before kick-off for this match. The model was run after the match using only data known before kick-off (the result was not used); it is not counted in the track record or accuracy stats.
The 3 clearest pre-match estimates and how they turned out. How are these chosen?
Statistical model estimate; for information and entertainment only.
Match stats
Standouts: K. Wassom 8.0 · A. Salazar 7.9 · A. Ankrah 7.9
Match analysis
5 sections14:1710 October 2026: the Virsliga matchday 32 match ended Tukums 0-2 Riga.
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Result and story of the match
It was 0-1 at half-time. Riga scored first, in the 9th minute.
Goals and key moments
Goals: 9' Reginaldo Ramires → 0-1; 62' M. Badamosi, assisted by S. Oulad M'Hand → 0-2.
Reginaldo Ramires missed a penalty in the 9th minute; VAR intervened 1 time; yellow cards: Tukums 3, Riga 4.
Statistical comparison
Expected goals (xG) Tukums 0.79, Riga 2.97; shots 8-14 (2-7 on target); possession 32%-68%; corners 4-5; pass accuracy 72%-89%.
Chance quality backed up the result: an xG gap of 2.18.
Goals by half: 0-1 in the first, 0-1 in the second.
Standout players
A. Kuprins was Tukums's highest-rated player at 7.5 in 90 minutes: 5 saves.
K. Wassom was Riga's highest-rated player at 8.0 in 90 minutes: won 12 of 13 duels.
How the model did
There was no model estimate locked before kick-off for this match, so it is not part of the track record.
Retrospective model estimate (generated after the match from pre-match data only; not part of the track record): Tukums 6%, draw 11%, Riga 84%.
Statistical model estimate; for information and entertainment only.
Match summary
Tukums vs Riga (Virsliga matchday 32, 10 October 2026) finished 0-2. Goals: Reginaldo Ramires 9', M. Badamosi 62'. Match stats: possession Tukums 32%, Riga 68%; shots on target 2-7; expected goals (xG) 0.79-2.97.
Updated: · Sources: Methodology, Track record
Points are only for rankings and badges; they have no other value.The model's score is now the “predicted score”: a pick that agrees with the match outcome and follows the real score distribution over a season (previously the single most likely score). Past points are unchanged.