Dunajská Lužná vs Púchov: prediction and stats
3. liga - WestWest - 15Kick-off on November 14, 2026 at 12:00 PM (UTC)Športový areál OFK, Dunajská Lužná
Probable lineup for Dunajská Lužná vs Púchov
Computed by Footlab AI from our own data only: recent lineups, squads and known absences. It does not take the press or last-minute absences into account.
Dunajská Lužná
Not enough data to predict the Dunajská Lužná lineup.
Púchov
4-3-3- 16N. Martinek
- 9T. Vavrík
- 10A. Goljan
- 23N. Kapuš
- 20R. Poboril
- 8Ľ. Lévai
- 3P. Leitner
- 5M. Mynář
- 12S. Marcanik
- 18M. Obsivan
- 30S. Vavrus
Computed on November 14, 2026 at 07:03 PM (UTC).
What is the recent form of both teams?
Over their last 15 matches in all competitions, Dunajská Lužná have 9 wins, 3 draws and 3 defeats; Púchov have 9, 3 and 3.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 7, 2026 | Nitra(away) | 0-1 | 3. liga - West |
| Oct 30, 2026 | Komárno II(away) | 2-0 | 3. liga - West |
| Oct 24, 2026 | Rača(home) | 2-1 | 3. liga - West |
| Oct 17, 2026 | Slovan Duslo Šaľa(away) | 1-0 | 3. liga - West |
| Oct 10, 2026 | DAC II(home) | 3-0 | 3. liga - West |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 7, 2026 | Komárno II(home) | 5-1 | 3. liga - West |
| Nov 1, 2026 | Rača(away) | 8-1 | 3. liga - West |
| Oct 24, 2026 | Slovan Duslo Šaľa(home) | 4-0 | 3. liga - West |
| Oct 18, 2026 | DAC II(away) | 0-2 | 3. liga - West |
| Oct 10, 2026 | Baník Prievidza(home) | 1-1 | 3. liga - West |
How many goals should we expect?
Dunajská Lužná score 1.5 and concede 0.7 goals per match over their last 15; Púchov score 2.7 and concede 0.9.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Dunajská Lužná | 1.5 | 0.7 | 7 | 6/15 | 6/15 |
| Púchov | 2.7 | 0.9 | 5 | 7/15 | 9/15 |
Over the last 15 matches in all competitions.
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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