Cherkashchyna-Akademiya vs Chornomorets: result 1-3 and analysis
Persha LigaRegular Season - 1Kick-off on July 27, 2019 at 02:00 PM (UTC)Tsentralnyi miskyi Stadion, Cherkasy
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What was the result of Cherkashchyna-Akademiya vs Chornomorets?
Chornomorets won 1-3 on July 27, 2019.
Goalscorers
- 32′Volodymyr ArzhanovChornomorets
- 45′Tymur PogranichnyiCherkashchyna-Akademiya
- 54′V. PavlovChornomorets
- 77′K. YaroshenkoChornomorets
What is the recent form of both teams?
Over their last 15 matches in all competitions, Cherkashchyna-Akademiya have 9 wins, 2 draws and 4 defeats; Chornomorets have 5, 4 and 6.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 25, 2019 | Veres Rivne(away) | 1-2 | Druha Liga |
| May 19, 2019 | Kalush(home) | 3-1 | Druha Liga |
| May 15, 2019 | Chayka(home) | 4-1 | Druha Liga |
| May 11, 2019 | Bukovyna(home) | 2-0 | Druha Liga |
| May 5, 2019 | Nyva Vinnytsya(away) | 0-1 | Druha Liga |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 29, 2019 | Desna(home) | 3-0 | Premier League |
| May 25, 2019 | Vorskla Poltava(away) | 2-1 | Premier League |
| May 21, 2019 | Karpaty(home) | 3-1 | Premier League |
| May 18, 2019 | Olimpik Donetsk(away) | 1-2 | Premier League |
| May 12, 2019 | Arsenal Kyiv(away) | 3-3 | Premier League |
How many goals should we expect?
Cherkashchyna-Akademiya score 1.9 and concede 0.8 goals per match over their last 15; Chornomorets score 1.3 and concede 1.3.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Cherkashchyna-Akademiya | 1.9 | 0.8 | 5 | 7/15 | 8/15 |
| Chornomorets | 1.3 | 1.3 | 4 | 8/15 | 9/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 2 | Chornomorets | 30 | 19 | 8 | 3 | 44 | 20 | 65 |
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