Kuban vs Fakel: result 2-2 and analysis
First LeagueRegular Season - 14Kick-off on September 16, 2017 at 03:00 PM (UTC)Stadion Kuban', Krasnodar
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What was the result of Kuban vs Fakel?
Kuban and Fakel drew 2-2 on September 16, 2017.
Goalscorers
- 20′Oleg AleynikKuban
- 34′97556Kuban
- 44′A. SerdyukFakel
- 50′D. MichurenkovFakel
What is the recent form of both teams?
Over their last 15 matches in all competitions, Kuban have 4 wins, 4 draws and 7 defeats; Fakel have 3, 3 and 9.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 10, 2017 | Zenit 2(away) | 3-3 | First League |
| Sep 6, 2017 | Enisey(home) | 1-1 | First League |
| Sep 2, 2017 | Baltika(away) | 1-5 | First League |
| Aug 27, 2017 | Spartak Moscow 2(home) | 2-3 | First League |
| Aug 19, 2017 | Tambov(away) | 1-2 | First League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 10, 2017 | Rotor Volgograd(home) | 2-0 | First League |
| Sep 6, 2017 | Avangard Kursk(away) | 0-2 | First League |
| Sep 2, 2017 | Zenit 2(away) | 1-4 | First League |
| Aug 27, 2017 | Enisey(home) | 1-3 | First League |
| Aug 19, 2017 | Baltika(away) | 1-0 | First League |
What does the head-to-head say?
Across the last 2 meetings: 0 Kuban win(s), 2 draw(s), 0 Fakel win(s).
| Date | Score | Competition |
|---|---|---|
| Apr 1, 2017 | Fakel 2-2 Kuban | First League |
| Sep 3, 2016 | Kuban 0-0 Fakel | First League |
How many goals should we expect?
Kuban score 1.6 and concede 1.9 goals per match over their last 15; Fakel score 0.6 and concede 1.6.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Kuban | 1.6 | 1.9 | 1 | 11/15 | 10/15 |
| Fakel | 0.6 | 1.6 | 4 | 4/15 | 5/15 |
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