Lokomotyvas Radviliškis vs Žalgirietis: result 0-6 and analysis
1 LygaRelegation Round - 4Kick-off on October 19, 2013 at 09:00 AM (UTC)Radviliškio miesto centrinis stadionas, Radviliškis
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What was the result of Lokomotyvas Radviliškis vs Žalgirietis?
Žalgirietis won 0-6 on October 19, 2013.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Lokomotyvas Radviliškis have 7 wins, 3 draws and 5 defeats; Žalgirietis have 7, 3 and 5.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 12, 2013 | Šilutė(away) | 1-1 | 1 Lyga |
| Oct 6, 2013 | Baltija(home) | 6-2 | 1 Lyga |
| Sep 29, 2013 | Polonija(away) | 5-1 | 1 Lyga |
| Sep 21, 2013 | Palanga(home) | 3-0 | 1 Lyga |
| Sep 15, 2013 | Baltija(home) | 1-3 | 1 Lyga |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 13, 2013 | Palanga(home) | 9-0 | 1 Lyga |
| Oct 5, 2013 | Šilutė(home) | 1-1 | 1 Lyga |
| Sep 29, 2013 | Baltija(away) | 3-2 | 1 Lyga |
| Sep 14, 2013 | Palanga(away) | 4-0 | 1 Lyga |
| Sep 7, 2013 | Lokomotyvas Radviliškis(home) | 3-0 | 1 Lyga |
What does the head-to-head say?
Across the last 2 meetings: 1 Lokomotyvas Radviliškis win(s), 0 draw(s), 1 Žalgirietis win(s).
| Date | Score | Competition |
|---|---|---|
| Sep 7, 2013 | Žalgirietis 3-0 Lokomotyvas Radviliškis | 1 Lyga |
| Jun 5, 2013 | Lokomotyvas Radviliškis 2-0 Žalgirietis | 1 Lyga |
Last archived meeting: Žalgirietis vs Lokomotyvas Radviliškis on Sep 7, 2013
How many goals should we expect?
Lokomotyvas Radviliškis score 2.2 and concede 2.0 goals per match over their last 15; Žalgirietis score 2.4 and concede 1.4.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Lokomotyvas Radviliškis | 2.2 | 2.0 | 3 | 10/15 | 12/15 |
| Žalgirietis | 2.4 | 1.4 | 6 | 7/15 | 11/15 |
Over the last 15 matches in all competitions.
How does Footlab compute this prediction?
The model combines both teams' Elo ratings (anchored by competition pool), their recent form weighted by opponent strength, home advantage and a Poisson matrix for scorelines. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy