Minija vs Panevėžys: result 2-4 and analysis
1 LygaRegular Season - 17Kick-off on July 30, 2016 at 02:00 PM (UTC)Kretingos dirbtinės dangos aikštė, Kretinga
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What was the result of Minija vs Panevėžys?
Panevėžys won 2-4 on July 30, 2016.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Minija have 2 wins, 1 draw and 12 defeats; Panevėžys have 8, 5 and 2.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 5, 2016 | Hegelmann Litauen(home) | 1-2 | 1 Lyga |
| Jul 2, 2016 | Lokomotyvas Radviliškis(home) | 2-1 | 1 Lyga |
| Jun 24, 2016 | Vilniaus Vytis(home) | 0-7 | 1 Lyga |
| Jun 18, 2016 | Džiugas Telšiai(away) | 0-2 | 1 Lyga |
| Jun 7, 2016 | Dainava(home) | 1-2 | 1 Lyga |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 6, 2016 | Šilutė(away) | 1-1 | 1 Lyga |
| Jul 2, 2016 | Dainava(away) | 4-0 | 1 Lyga |
| Jun 23, 2016 | Trakai II(home) | 2-1 | 1 Lyga |
| Jun 18, 2016 | Kaunas(away) | 3-1 | 1 Lyga |
| Jun 7, 2016 | Palanga(home) | 3-2 | 1 Lyga |
What does the head-to-head say?
In the last meeting: 0 Minija wins, 0 draws, 1 Panevėžys win.
| Date | Score | Competition |
|---|---|---|
| Apr 2, 2016 | Panevėžys 2-0 Minija | 1 Lyga |
How many goals should we expect?
Minija score 0.7 and concede 3.1 goals per match over their last 15; Panevėžys score 1.9 and concede 0.9.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Minija | 0.7 | 3.1 | 1 | 7/15 | 10/15 |
| Panevėžys | 1.9 | 0.9 | 6 | 7/15 | 7/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 1 | Minija | 25 | 22 | 1 | 2 | 68 | 13 | 67 |
A question about Minija vs Panevėžys?
Ask Footlab AI: lineups, absences, player form, stats, how the match could unfold.
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How does Footlab AI 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. When bookmakers publish odds for the match, their consensus is built into the calculation: that is what proved most accurate on past matches. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy