Dynamo Makhachkala II vs SKA Rostov: result 1-2 and analysis
Second League - Group 1Group 1 - 2nd Phase - 1Kick-off on October 21, 2023 at 01:00 PM (UTC)
Full time
What was the result of Dynamo Makhachkala II vs SKA Rostov?
SKA Rostov won 1-2 on October 21, 2023.
Goalscorers
- 2′V. NazarovSKA Rostov
- 33′E. MakarovSKA Rostov (pen.)
- 55′A. KurbanalievDynamo Makhachkala II
What is the recent form of both teams?
Over their last 12 matches in all competitions, Dynamo Makhachkala II have 2 wins, 5 draws and 5 defeats; SKA Rostov have 5, 2 and 8.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 7, 2023 | Mashuk-KMV(home) | 0-1 | Second League - Group 1 |
| Oct 2, 2023 | Legion Dynamo(away) | 1-2 | Second League - Group 1 |
| Sep 24, 2023 | Alaniya Vladikavkaz II(home) | 0-0 | Second League - Group 1 |
| Sep 17, 2023 | Spartak Nalchik(away) | 1-1 | Second League - Group 1 |
| Sep 9, 2023 | Astrakhan(home) | 1-0 | Second League - Group 1 |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 14, 2023 | Sevastopol(home) | 0-1 | Second League - Group 1 |
| Oct 7, 2023 | Dinamo Stavropol(away) | 2-0 | Second League - Group 1 |
| Sep 27, 2023 | FK Neftekhimik(home) | 0-3 | Cup |
| Sep 23, 2023 | Rubin Yalta(home) | 1-3 | Second League - Group 1 |
| Sep 17, 2023 | Druzhba(away) | 0-1 | Second League - Group 1 |
How many goals should we expect?
Dynamo Makhachkala II score 0.8 and concede 1.1 goals per match over their last 12; SKA Rostov score 0.9 and concede 1.3.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Dynamo Makhachkala II | 0.8 | 1.1 | 4 | 6/12 | 4/12 |
| SKA Rostov | 0.9 | 1.3 | 3 | 6/15 | 5/15 |
Over the last 12 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 15 | Dynamo Makhachkala II | 20 | 3 | 4 | 13 | 16 | 35 | 13 |
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