Chernomorets Balchik vs Nesebar: result 3-2 and analysis
Second LeagueRegular Season - 11Kick-off on October 14, 2017 at 01:00 PM (UTC)Gradski stadion, Balchik
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What was the result of Chernomorets Balchik vs Nesebar?
Chernomorets Balchik won 3-2 on October 14, 2017.
Goalscorers
- 35′Daniel BenchevNesebar
- 41′Vladislav MirchevChernomorets Balchik
- 43′V. MitevChernomorets Balchik
- 48′D. MoldovanovNesebar
- 90′10834Chernomorets Balchik
What is the recent form of both teams?
Over their last 11 matches in all competitions, Chernomorets Balchik have 4 wins, 3 draws and 4 defeats; Nesebar have 5, 3 and 7.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 30, 2017 | Strumska Slava(away) | 2-0 | Second League |
| Sep 24, 2017 | Neftochimic Burgas(away) | 1-1 | Second League |
| Sep 20, 2017 | Slavia Sofia(home) | 0-2 | Cup |
| Sep 16, 2017 | Lokomotiv Sofia(home) | 2-2 | Second League |
| Sep 11, 2017 | Maritsa Plovdiv(away) | 1-0 | Second League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 30, 2017 | Neftochimic Burgas(home) | 2-1 | Second League |
| Sep 24, 2017 | Lokomotiv Sofia(away) | 1-4 | Second League |
| Sep 19, 2017 | CSKA Sofia(home) | 1-4 | Cup |
| Sep 15, 2017 | Maritsa Plovdiv(home) | 4-0 | Second League |
| Sep 9, 2017 | Montana(away) | 3-1 | Second League |
How many goals should we expect?
Chernomorets Balchik score 1.2 and concede 1.1 goals per match over their last 11; Nesebar score 1.5 and concede 1.9.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Chernomorets Balchik | 1.2 | 1.1 | 3 | 4/11 | 3/11 |
| Nesebar | 1.5 | 1.9 | 2 | 11/15 | 12/15 |
Over the last 11 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 3 | Nesebar | 5 | 3 | 0 | 2 | 12 | 8 | 9 |
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