KRA vs Chemelil Sugar: result 1-0 and analysis
FKF Premier LeagueRegular Season - 12Kick-off on September 11, 2015 at 11:00 AM (UTC)Nyayo National Stadium, Nairobi
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What was the result of KRA vs Chemelil Sugar?
KRA won 1-0 on September 11, 2015.
What is the recent form of both teams?
Over their last 15 matches in all competitions, KRA have 5 wins, 5 draws and 5 defeats; Chemelil Sugar have 3, 8 and 4.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Aug 28, 2015 | Tusker(home) | 0-0 | FKF Premier League |
| Aug 23, 2015 | Thika United(away) | 0-2 | FKF Premier League |
| Aug 16, 2015 | Mathare United(away) | 3-1 | FKF Premier League |
| Aug 12, 2015 | GOR Mahia(away) | 0-2 | FKF Premier League |
| Aug 7, 2015 | Sofapaka(home) | 1-1 | FKF Premier League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Aug 29, 2015 | AFC Leopards(away) | 2-1 | FKF Premier League |
| Aug 22, 2015 | Nakuru AllStars(home) | 1-0 | FKF Premier League |
| Aug 16, 2015 | Sony Sugar(home) | 0-0 | FKF Premier League |
| Aug 9, 2015 | Bandari(away) | 0-0 | FKF Premier League |
| Aug 2, 2015 | Muhoroni Youth(home) | 0-2 | FKF Premier League |
What does the head-to-head say?
Across the last 3 meetings: 0 KRA win(s), 1 draw(s), 2 Chemelil Sugar win(s).
| Date | Score | Competition |
|---|---|---|
| May 3, 2015 | Chemelil Sugar 1-0 KRA | FKF Premier League |
| Nov 1, 2014 | KRA 0-2 Chemelil Sugar | FKF Premier League |
| Mar 29, 2014 | Chemelil Sugar 0-0 KRA | FKF Premier League |
How many goals should we expect?
KRA score 0.9 and concede 0.9 goals per match over their last 15; Chemelil Sugar score 0.7 and concede 1.1.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| KRA | 0.9 | 0.9 | 5 | 5/15 | 3/15 |
| Chemelil Sugar | 0.7 | 1.1 | 7 | 6/15 | 4/15 |
Over the last 15 matches in all competitions.
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