Dainava vs NFA Kaunas: result 1-0 and analysis
1 LygaRegular Season - 20Kick-off on September 25, 2018 at 03:30 PM (UTC)Alytaus m. centrinis stadionas, Marijampolė
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What was the result of Dainava vs NFA Kaunas?
Dainava won 1-0 on September 25, 2018.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Dainava have 8 wins, 4 draws and 3 defeats; NFA Kaunas have 2, 2 and 11.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 21, 2018 | Džiugas Telšiai(away) | 3-0 | 1 Lyga |
| Aug 26, 2018 | Koralas(away) | 3-1 | 1 Lyga |
| Aug 17, 2018 | Žalgiris II(home) | 1-0 | 1 Lyga |
| Aug 10, 2018 | Panevėžys(away) | 1-1 | 1 Lyga |
| Aug 2, 2018 | Stumbras II(home) | 2-0 | 1 Lyga |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 22, 2018 | Žalgiris II(away) | 1-2 | 1 Lyga |
| Sep 15, 2018 | Džiugas Telšiai(home) | 2-3 | 1 Lyga |
| Aug 25, 2018 | Pakruojis(away) | 0-0 | 1 Lyga |
| Aug 21, 2018 | Koralas(away) | 1-2 | 1 Lyga |
| Aug 18, 2018 | Kupiškis(away) | 2-0 | 1 Lyga |
What does the head-to-head say?
In the last meeting: 0 Dainava wins, 1 draw, 0 NFA Kaunas wins.
| Date | Score | Competition |
|---|---|---|
| May 11, 2018 | NFA Kaunas 2-2 Dainava | 1 Lyga |
Last archived meeting: NFA Kaunas vs Dainava on May 11, 2018
How many goals should we expect?
Dainava score 2.1 and concede 1.1 goals per match over their last 15; NFA Kaunas score 1.5 and concede 2.2.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Dainava | 2.1 | 1.1 | 6 | 8/15 | 9/15 |
| NFA Kaunas | 1.5 | 2.2 | 3 | 10/15 | 11/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 6 | Dainava | 29 | 13 | 8 | 8 | 44 | 29 | 47 |
A question about Dainava vs NFA Kaunas?
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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