TOM Tomsk vs Fakel: result 1-0 and analysis
First LeagueRegular Season - 18Kick-off on October 14, 2017 at 10:00 AM (UTC)Stadion Trud, Tomsk
Full time
What was the result of TOM Tomsk vs Fakel?
TOM Tomsk won 1-0 on October 14, 2017.
Goalscorers
- 32′A. PuljićTOM Tomsk
Official lineup for TOM Tomsk vs Fakel
The starting elevens fielded at kick-off.
TOM Tomsk
- 10Anzor Sanaya
- 7D. Sasin
- 11Pavel Deobald
- 16A. Pomerko
- 20R. Voydel
- 98Nikita Gvineyskiy
- 2Andrey Ivanov
- 5T. Mikuckis
- 6A. Puljić
- 23S. Zuykov
- 1Yuri Shafinskiy
Fakel
- 11A. Serdyuk
- 19D. Michurenkov
- 6Dmitri Kayumov
- 10A. Ambartsumyan
- 37E. Ushakov
- 23I. Lukjanovs
- 3I. Khaymanov
- 27A. Molodtsov
- 55M. Osipenko
- 63Temur Mustafin
- 16A. Fedorov
What is the recent form of both teams?
Over their last 15 matches in all competitions, TOM Tomsk have 4 wins, 3 draws and 8 defeats; Fakel have 4, 2 and 9.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 7, 2017 | Zenit 2(away) | 0-2 | First League |
| Sep 30, 2017 | Enisey(home) | 1-1 | First League |
| Sep 24, 2017 | Baltika(away) | 1-2 | First League |
| Sep 16, 2017 | Spartak Moscow 2(home) | 1-2 | First League |
| Sep 10, 2017 | Tambov(away) | 1-3 | First League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 7, 2017 | Krylia Sovetov(home) | 1-0 | First League |
| Sep 30, 2017 | Nizhny Novgorod(away) | 1-2 | First League |
| Sep 24, 2017 | Luch-Energiya(away) | 0-0 | First League |
| Sep 16, 2017 | Kuban(away) | 2-2 | First League |
| Sep 10, 2017 | Rotor Volgograd(home) | 2-0 | First League |
How many goals should we expect?
TOM Tomsk score 0.9 and concede 1.4 goals per match over their last 15; Fakel score 0.7 and concede 1.6.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| TOM Tomsk | 0.9 | 1.4 | 3 | 9/15 | 8/15 |
| Fakel | 0.7 | 1.6 | 5 | 4/15 | 6/15 |
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 |
A question about TOM Tomsk vs Fakel?
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