Metallurg Vidnoye vs Krasnyy-SGAFKST: result 1-4 and analysis
Second League - Group 3Group 3 - 13Kick-off on October 29, 2020 at 03:00 PM (UTC)Stadion Metallurg Vidnoe, Vidnoye
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What was the result of Metallurg Vidnoye vs Krasnyy-SGAFKST?
Krasnyy-SGAFKST won 1-4 on October 29, 2020.
Goalscorers
- 15′Damir TregulovMetallurg Vidnoye
- 34′Kirill PomelnikovKrasnyy-SGAFKST
- 51′Vasili MeshkovskiyKrasnyy-SGAFKST
- 55′Vasili MeshkovskiyKrasnyy-SGAFKST (pen.)
- 90′Y. MarinKrasnyy-SGAFKST
What is the recent form of both teams?
Over their last 14 matches in all competitions, Metallurg Vidnoye have 5 wins, 0 draws and 9 defeats; Krasnyy-SGAFKST have 6, 4 and 5.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 24, 2020 | Metallurg Lipetsk(away) | 0-7 | Second League - Group 3 |
| Oct 19, 2020 | Strogino(home) | 4-2 | Second League - Group 3 |
| Oct 11, 2020 | Salyut-Belgorod(away) | 2-1 | Second League - Group 3 |
| Oct 5, 2020 | Znamya Noginsk(home) | 1-2 | Second League - Group 3 |
| Sep 27, 2020 | Fakel II(away) | 1-2 | Second League - Group 3 |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 24, 2020 | Sakhalin(away) | 0-4 | Second League - Group 3 |
| Oct 19, 2020 | Khimki II(away) | 2-1 | Second League - Group 3 |
| Oct 11, 2020 | Kvant(home) | 4-1 | Second League - Group 3 |
| Oct 4, 2020 | Khimik-Arsenal(away) | 3-0 | Second League - Group 3 |
| Sep 26, 2020 | Ryazan(home) | 1-0 | Second League - Group 3 |
How many goals should we expect?
Metallurg Vidnoye score 1.1 and concede 1.9 goals per match over their last 14; Krasnyy-SGAFKST score 1.3 and concede 1.1.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Metallurg Vidnoye | 1.1 | 1.9 | 3 | 5/14 | 10/14 |
| Krasnyy-SGAFKST | 1.3 | 1.1 | 5 | 6/15 | 8/15 |
Over the last 14 matches in all competitions.
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