Sevastopol vs Khimik Dzerzhinsk: result 3-2 and analysis
Second League - Final FourSemi-finalsKick-off on November 6, 2026 at 04:00 PM (UTC)SK Sevastopol', Sevastopol'
Full time
What was the result of Sevastopol vs Khimik Dzerzhinsk?
Sevastopol won 3-2 on November 6, 2026.
Goalscorers
- 38′M. SuprunSevastopol
- 45′K. MyrzakovKhimik Dzerzhinsk
- 71′A. StepanovichSevastopol
- 83′Ruslan GavrilovKhimik Dzerzhinsk
- 84′A. DuguSevastopol
What is the recent form of both teams?
Over their last 15 matches in all competitions, Sevastopol have 11 wins, 1 draw and 3 defeats; Khimik Dzerzhinsk have 11, 0 and 4.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 31, 2026 | Shahter Taganrog(home) | 0-2 | Second League - Group 1 |
| Oct 24, 2026 | FK Chayka 2(away) | 4-1 | Second League - Group 1 |
| Oct 17, 2026 | Izberbash(home) | 2-2 | Second League - Group 1 |
| Oct 11, 2026 | Dynamo Makhachkala II(away) | 6-0 | Second League - Group 1 |
| Oct 3, 2026 | Lugansk(home) | 2-0 | Second League - Group 1 |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 1, 2026 | Dinamo Barnaul(away) | 2-1 | Second League - Group 4 |
| Oct 25, 2026 | Krylya Sovetov II(home) | 3-1 | Second League - Group 4 |
| Oct 18, 2026 | Akron II(away) | 3-4 | Second League - Group 4 |
| Oct 11, 2026 | Pobeda Nizhniy Novgorod(home) | 5-0 | Second League - Group 4 |
| Oct 4, 2026 | KDV(away) | 2-0 | Second League - Group 4 |
How many goals should we expect?
Sevastopol score 2.3 and concede 1.1 goals per match over their last 15; Khimik Dzerzhinsk score 2.3 and concede 1.0.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Sevastopol | 2.3 | 1.1 | 5 | 9/15 | 10/15 |
| Khimik Dzerzhinsk | 2.3 | 1.0 | 6 | 8/15 | 11/15 |
Over the last 15 matches in all competitions.
A question about Sevastopol vs Khimik Dzerzhinsk?
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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