Fakel vs TOM Tomsk: result 0-1 and analysis
First LeagueRegular Season - 36Kick-off on May 2, 2018 at 01:00 PM (UTC)Central'nyi Stadion Profsoyuzov, Voronezh
Full time
What was the result of Fakel vs TOM Tomsk?
TOM Tomsk won 0-1 on May 2, 2018.
Goalscorers
- 61′I. KukharchukTOM Tomsk
What is the recent form of both teams?
Over their last 15 matches in all competitions, Fakel have 2 wins, 4 draws and 9 defeats; TOM Tomsk have 3, 6 and 6.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Apr 28, 2018 | Krylia Sovetov(away) | 1-2 | First League |
| Apr 21, 2018 | Nizhny Novgorod(home) | 4-1 | First League |
| Apr 15, 2018 | Luch-Energiya(home) | 3-1 | First League |
| Apr 11, 2018 | Kuban(home) | 0-2 | First League |
| Apr 7, 2018 | Rotor Volgograd(away) | 0-0 | First League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Apr 28, 2018 | Zenit 2(home) | 1-2 | First League |
| Apr 21, 2018 | Enisey(away) | 2-3 | First League |
| Apr 15, 2018 | Baltika(home) | 1-0 | First League |
| Apr 11, 2018 | Spartak Moscow 2(away) | 1-2 | First League |
| Apr 7, 2018 | Tambov(home) | 0-0 | First League |
What does the head-to-head say?
Across the last 1 meetings: 0 Fakel win(s), 0 draw(s), 1 TOM Tomsk win(s).
| Date | Score | Competition |
|---|---|---|
| Oct 14, 2017 | TOM Tomsk 1-0 Fakel | First League |
How many goals should we expect?
Fakel score 0.6 and concede 1.4 goals per match over their last 15; TOM Tomsk score 1.1 and concede 1.5.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Fakel | 0.6 | 1.4 | 4 | 4/15 | 6/15 |
| TOM Tomsk | 1.1 | 1.5 | 5 | 8/15 | 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