Babīte vs FK Liepaja: prediction and stats
VirsligaRegular Season - 17Kick-off on August 4, 2017 at 12:00 AM (UTC)
Match cancelled
What is the recent form of both teams?
Over their last 15 matches in all competitions, Babīte have 2 wins, 2 draws and 11 defeats; FK Liepaja have 9, 1 and 5.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jun 16, 2017 | Spartaks Jurmala(home) | 0-1 | Virsliga |
| Jun 2, 2017 | FK Ventspils(away) | 0-1 | Virsliga |
| May 26, 2017 | FK Liepaja(home) | 1-2 | Virsliga |
| May 20, 2017 | Rīgas FS(away) | 1-2 | Virsliga |
| May 12, 2017 | Jelgava(home) | 0-5 | Virsliga |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 31, 2017 | Jelgava(away) | 3-1 | Virsliga |
| Jul 24, 2017 | Riga(home) | 2-1 | Virsliga |
| Jul 20, 2017 | Suduva Marijampole(away) | 1-0 | UEFA Europa League |
| Jul 16, 2017 | Metta / LU(home) | 0-1 | Virsliga |
| Jul 13, 2017 | Suduva Marijampole(home) | 0-2 | UEFA Europa League |
What does the head-to-head say?
Across the last 2 meetings: 0 Babīte wins, 0 draws, 2 FK Liepaja wins.
| Date | Score | Competition |
|---|---|---|
| May 26, 2017 | Babīte 1-2 FK Liepaja | Virsliga |
| Apr 1, 2017 | FK Liepaja 3-1 Babīte | Virsliga |
How many goals should we expect?
Babīte score 1.1 and concede 2.2 goals per match over their last 15; FK Liepaja score 1.5 and concede 1.1.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Babīte | 1.1 | 2.2 | 1 | 8/15 | 11/15 |
| FK Liepaja | 1.5 | 1.1 | 5 | 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 |
|---|---|---|---|---|---|---|---|---|
| 4 | FK Liepaja | 33 | 13 | 7 | 13 | 46 | 46 | 46 |
A question about Babīte vs FK Liepaja?
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