Lokomotyvas Radviliškis vs Palanga: result 6-0 and analysis
1 LygaRelegation Round - 5Kick-off on October 27, 2013 at 11:00 AM (UTC)Radviliškio miesto centrinis stadionas, Radviliškis
Full time
What was the result of Lokomotyvas Radviliškis vs Palanga?
Lokomotyvas Radviliškis won 6-0 on October 27, 2013.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Lokomotyvas Radviliškis have 7 wins, 2 draws and 6 defeats; Palanga have 2, 1 and 12.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 19, 2013 | Žalgirietis(home) | 0-6 | 1 Lyga |
| 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 |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 19, 2013 | Baltija(home) | 2-1 | 1 Lyga |
| Oct 13, 2013 | Žalgirietis(away) | 0-9 | 1 Lyga |
| Oct 6, 2013 | Polonija(home) | 4-2 | 1 Lyga |
| Sep 28, 2013 | Šilutė(away) | 2-4 | 1 Lyga |
| Sep 21, 2013 | Lokomotyvas Radviliškis(away) | 0-3 | 1 Lyga |
What does the head-to-head say?
Across the last 2 meetings: 1 Lokomotyvas Radviliškis win(s), 1 draw(s), 0 Palanga win(s).
| Date | Score | Competition |
|---|---|---|
| Sep 21, 2013 | Lokomotyvas Radviliškis 3-0 Palanga | 1 Lyga |
| Jun 23, 2013 | Palanga 2-2 Lokomotyvas Radviliškis | 1 Lyga |
Last archived meeting: Lokomotyvas Radviliškis vs Palanga on Sep 21, 2013
How many goals should we expect?
Lokomotyvas Radviliškis score 2.1 and concede 2.3 goals per match over their last 15; Palanga score 0.9 and concede 3.5.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Lokomotyvas Radviliškis | 2.1 | 2.3 | 3 | 9/15 | 12/15 |
| Palanga | 0.9 | 3.5 | 0 | 7/15 | 12/15 |
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