Sileks vs Pobeda: result 2-0 and analysis
First LeagueRegular Season - 20Kick-off on February 26, 2017 at 01:00 PM (UTC)Gradski stadion Kratovo, Kratovo
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What was the result of Sileks vs Pobeda?
Sileks won 2-0 on February 26, 2017.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Sileks have 3 wins, 8 draws and 4 defeats; Pobeda have 3, 7 and 5.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Feb 19, 2017 | Shkendija(away) | 0-2 | First League |
| Dec 4, 2016 | Vardar Skopje(away) | 1-1 | First League |
| Nov 30, 2016 | Shkupi 1927(home) | 1-0 | First League |
| Nov 27, 2016 | Pelister(home) | 0-0 | First League |
| Nov 20, 2016 | Renova(away) | 2-2 | First League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Feb 19, 2017 | Pelister(home) | 2-2 | First League |
| Dec 4, 2016 | Renova(home) | 0-2 | First League |
| Nov 30, 2016 | FK Rabotnicki(away) | 1-3 | First League |
| Nov 27, 2016 | Makedonija GjP(home) | 0-0 | First League |
| Nov 20, 2016 | Bregalnica Štip(away) | 3-2 | First League |
What does the head-to-head say?
Across the last 2 meetings: 1 Sileks win, 1 draw, 0 Pobeda wins.
| Date | Score | Competition |
|---|---|---|
| Oct 23, 2016 | Pobeda 1-1 Sileks | First League |
| Aug 14, 2016 | Sileks 2-1 Pobeda | First League |
How many goals should we expect?
Sileks score 0.9 and concede 1.2 goals per match over their last 15; Pobeda score 1.1 and concede 1.4.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Sileks | 0.9 | 1.2 | 5 | 9/15 | 5/15 |
| Pobeda | 1.1 | 1.4 | 2 | 11/15 | 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 |
|---|---|---|---|---|---|---|---|---|
| 6 | Sileks | 10 | 3 | 4 | 3 | 17 | 11 | 13 |
A question about Sileks vs Pobeda?
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