Znamya Noginsk vs Saturn Ramenskoye: result 1-0 and analysis
Second League - Group 3Group 3 - 14Kick-off on November 3, 2020 at 11:00 AM (UTC)Stadion Znamya, Noginsk
Full time
What was the result of Znamya Noginsk vs Saturn Ramenskoye?
Znamya Noginsk won 1-0 on November 3, 2020.
Goalscorers
- 29′Roman PavlyuchenkoZnamya Noginsk
What is the recent form of both teams?
Over their last 15 matches in all competitions, Znamya Noginsk have 6 wins, 5 draws and 4 defeats; Saturn Ramenskoye have 9, 3 and 3.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 29, 2020 | Fakel II(away) | 1-0 | Second League - Group 3 |
| Oct 24, 2020 | Kaluga(home) | 1-1 | Second League - Group 3 |
| Oct 18, 2020 | Avangard Kursk(away) | 1-1 | Second League - Group 3 |
| Oct 11, 2020 | FK Sokol Saratov(home) | 1-1 | Second League - Group 3 |
| Oct 5, 2020 | Metallurg Vidnoye(away) | 2-1 | Second League - Group 3 |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 29, 2020 | Kvant(away) | 5-1 | Second League - Group 3 |
| Oct 24, 2020 | Fakel II(home) | 2-0 | Second League - Group 3 |
| Oct 18, 2020 | Khimik-Arsenal(away) | 3-0 | Second League - Group 3 |
| Oct 11, 2020 | Kaluga(home) | 2-0 | Second League - Group 3 |
| Oct 4, 2020 | Ryazan(away) | 4-1 | Second League - Group 3 |
How many goals should we expect?
Znamya Noginsk score 1.5 and concede 1.1 goals per match over their last 15; Saturn Ramenskoye score 2.1 and concede 0.8.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Znamya Noginsk | 1.5 | 1.1 | 5 | 9/15 | 9/15 |
| Saturn Ramenskoye | 2.1 | 0.8 | 7 | 6/15 | 8/15 |
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 | Saturn Ramenskoye | 20 | 10 | 4 | 6 | 36 | 30 | 34 |
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