Sibir vs Fakel: result 4-0 and analysis
First LeagueRegular Season - 5Kick-off on July 30, 2017 at 10:00 AM (UTC)Stadion Spartak, Novosibirsk
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What was the result of Sibir vs Fakel?
Sibir won 4-0 on July 30, 2017.
Goalscorers
- 7′Eugeniu CebotaruSibir
- 35′97564Sibir
- 44′M. ZhitnevSibir
- 77′M. ZhitnevSibir
What is the recent form of both teams?
Over their last 15 matches in all competitions, Sibir have 3 wins, 4 draws and 8 defeats; Fakel have 4, 4 and 7.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 26, 2017 | Zenit 2(away) | 1-2 | First League |
| Jul 22, 2017 | Enisey(home) | 0-1 | First League |
| Jul 15, 2017 | Baltika(away) | 0-0 | First League |
| Jul 8, 2017 | Spartak Moscow 2(home) | 1-2 | First League |
| May 20, 2017 | Spartak Moscow 2(home) | 2-1 | First League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 26, 2017 | Tyumen(home) | 1-0 | First League |
| Jul 22, 2017 | FC Sochi(away) | 0-2 | First League |
| Jul 15, 2017 | Shinnik Yaroslavl(home) | 2-2 | First League |
| Jul 8, 2017 | Volgar Astrakhan(away) | 0-0 | First League |
| May 20, 2017 | Volgar Astrakhan(home) | 0-1 | First League |
What does the head-to-head say?
Across the last 2 meetings: 0 Sibir win(s), 1 draw(s), 1 Fakel win(s).
| Date | Score | Competition |
|---|---|---|
| Mar 19, 2017 | Fakel 2-0 Sibir | First League |
| Aug 21, 2016 | Sibir 1-1 Fakel | First League |
How many goals should we expect?
Sibir score 0.8 and concede 1.5 goals per match over their last 15; Fakel score 0.9 and concede 1.5.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Sibir | 0.8 | 1.5 | 4 | 8/15 | 8/15 |
| Fakel | 0.9 | 1.5 | 4 | 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 |
|---|---|---|---|---|---|---|---|---|
| 2 | Fakel | 34 | 20 | 8 | 6 | 44 | 22 | 68 |
How does Footlab 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. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy