Palanga vs Lokomotyvas Radviliškis: result 3-1 and analysis
1 LygaRegular Season - 11Kick-off on June 7, 2014 at 02:00 PM (UTC)Centrinis miesto stadionas, Palanga
Full time
What was the result of Palanga vs Lokomotyvas Radviliškis?
Palanga won 3-1 on June 7, 2014.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Palanga have 4 wins, 1 draws and 10 defeats; Lokomotyvas Radviliškis have 4, 2 and 9.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jun 3, 2014 | Žalgirietis(home) | 0-2 | 1 Lyga |
| May 31, 2014 | Šilutė(away) | 1-0 | 1 Lyga |
| May 21, 2014 | Baltija(away) | 1-2 | 1 Lyga |
| May 18, 2014 | Utenis Utena(home) | 0-3 | 1 Lyga |
| May 10, 2014 | Stumbras(away) | 1-4 | 1 Lyga |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jun 3, 2014 | Stumbras(away) | 0-6 | 1 Lyga |
| May 30, 2014 | MRU(home) | 1-2 | 1 Lyga |
| May 24, 2014 | Tauras(home) | 1-1 | 1 Lyga |
| May 21, 2014 | Nevėžis(home) | 0-2 | 1 Lyga |
| May 18, 2014 | Šilas(away) | 0-3 | 1 Lyga |
What does the head-to-head say?
Across the last 3 meetings: 0 Palanga win(s), 1 draw(s), 2 Lokomotyvas Radviliškis win(s).
| Date | Score | Competition |
|---|---|---|
| Oct 27, 2013 | Lokomotyvas Radviliškis 6-0 Palanga | 1 Lyga |
| 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 Oct 27, 2013
How many goals should we expect?
Palanga score 1.0 and concede 3.1 goals per match over their last 15; Lokomotyvas Radviliškis score 1.5 and concede 2.1.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Palanga | 1.0 | 3.1 | 1 | 8/15 | 12/15 |
| Lokomotyvas Radviliškis | 1.5 | 2.1 | 1 | 6/15 | 8/15 |
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