Ağsu vs Səbail: result 3-2 and analysis
Birinci DastaRegular Season - 20Kick-off on March 16, 2017 at 10:00 AM (UTC)Ağsu şəhər stadionu, Ağsu
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What was the result of Ağsu vs Səbail?
Ağsu won 3-2 on March 16, 2017.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Ağsu have 10 wins, 2 draws and 3 defeats; Səbail have 9, 2 and 4.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Mar 9, 2017 | Mingəçevir(away) | 2-0 | Birinci Dasta |
| Mar 3, 2017 | Göyəzən(home) | 7-0 | Birinci Dasta |
| Feb 27, 2017 | Bakılı(home) | 7-0 | Birinci Dasta |
| Feb 22, 2017 | Qaradağ Lökbatan(away) | 0-3 | Birinci Dasta |
| Feb 16, 2017 | MOIK(home) | 1-0 | Birinci Dasta |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Mar 9, 2017 | Turan(home) | 0-0 | Birinci Dasta |
| Mar 3, 2017 | Mingəçevir(home) | 3-0 | Birinci Dasta |
| Feb 27, 2017 | Imisli FK(home) | 1-0 | Birinci Dasta |
| Feb 22, 2017 | Göyəzən(away) | 7-0 | Birinci Dasta |
| Feb 18, 2017 | Qaradağ Lökbatan(home) | 3-1 | Birinci Dasta |
What does the head-to-head say?
In the last meeting: 0 Ağsu wins, 1 draw, 0 Səbail wins.
| Date | Score | Competition |
|---|---|---|
| Oct 21, 2016 | Səbail 1-1 Ağsu | Birinci Dasta |
How many goals should we expect?
Ağsu score 2.9 and concede 0.9 goals per match over their last 15; Səbail score 2.8 and concede 1.3.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Ağsu | 2.9 | 0.9 | 7 | 6/15 | 10/15 |
| Səbail | 2.8 | 1.3 | 6 | 7/15 | 10/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 3 | Səbail | 5 | 3 | 2 | 0 | 8 | 4 | 11 |
A question about Ağsu vs Səbail?
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