FK Rabotnicki vs Akademija Pandev: result 1-2 and analysis
First LeagueRegular Season - 7Kick-off on September 24, 2017 at 01:30 PM (UTC)Toše Proeski Arena, Skopje
Full time
What was the result of FK Rabotnicki vs Akademija Pandev?
Akademija Pandev won 1-2 on September 24, 2017.
What is the recent form of both teams?
Over their last 15 matches in all competitions, FK Rabotnicki have 4 wins, 6 draws and 5 defeats; Akademija Pandev have 3, 2 and 1.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 20, 2017 | Pobeda(away) | 4-2 | First League |
| Sep 17, 2017 | Shkendija(home) | 2-6 | First League |
| Sep 10, 2017 | Shkupi 1927(away) | 1-2 | First League |
| Aug 20, 2017 | Pelister(away) | 1-1 | First League |
| Aug 12, 2017 | Renova(home) | 0-0 | First League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 20, 2017 | Pelister(home) | 2-0 | First League |
| Sep 17, 2017 | Pobeda(away) | 2-2 | First League |
| Sep 10, 2017 | Renova(home) | 1-0 | First League |
| Aug 27, 2017 | Shkendija(away) | 1-2 | First League |
| Aug 20, 2017 | Skopje(home) | 2-0 | First League |
How many goals should we expect?
FK Rabotnicki score 1.7 and concede 1.9 goals per match over their last 15; Akademija Pandev score 1.5 and concede 0.8.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| FK Rabotnicki | 1.7 | 1.9 | 4 | 9/15 | 9/15 |
| Akademija Pandev | 1.5 | 0.8 | 3 | 3/6 | 2/6 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
FK Rabotnicki sit 11th in First League (33 points from 33 matches) and Akademija Pandev sit 9th (40 points from 33 matches).
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 9 | Akademija Pandev | 33 | 10 | 10 | 13 | 46 | 56 | 40 |
| 11 | FK Rabotnicki | 33 | 9 | 6 | 18 | 45 | 58 | 33 |
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