KRA vs Tusker: result 0-2 and analysis
FKF Premier LeagueRegular Season - 28Kick-off on October 24, 2014 at 03:00 PM (UTC)Moi International Sports Centre, Nairobi
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What was the result of KRA vs Tusker?
Tusker won 0-2 on October 24, 2014.
What is the recent form of both teams?
Over their last 15 matches in all competitions, KRA have 4 wins, 1 draw and 10 defeats; Tusker have 7, 6 and 2.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 16, 2014 | Sofapaka(away) | 2-3 | FKF Premier League |
| Oct 4, 2014 | Muhoroni Youth(away) | 1-3 | FKF Premier League |
| Sep 19, 2014 | Sony Sugar(home) | 0-1 | FKF Premier League |
| Sep 14, 2014 | Mathare United(away) | 0-2 | FKF Premier League |
| Aug 22, 2014 | KCB(away) | 0-1 | FKF Premier League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 19, 2014 | Muhoroni Youth(home) | 2-0 | FKF Premier League |
| Oct 4, 2014 | Mathare United(away) | 1-0 | FKF Premier League |
| Sep 25, 2014 | Ulinzi Stars(away) | 1-2 | FKF Premier League |
| Sep 19, 2014 | Nairobi City Stars(home) | 1-1 | FKF Premier League |
| Sep 14, 2014 | GOR Mahia(home) | 1-1 | FKF Premier League |
What does the head-to-head say?
In the last meeting: 0 KRA wins, 0 draws, 1 Tusker win.
| Date | Score | Competition |
|---|---|---|
| May 11, 2014 | Tusker 3-1 KRA | FKF Premier League |
How many goals should we expect?
KRA score 0.9 and concede 1.5 goals per match over their last 15; Tusker score 1.2 and concede 0.5.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| KRA | 0.9 | 1.5 | 2 | 7/15 | 7/15 |
| Tusker | 1.2 | 0.5 | 9 | 5/15 | 3/15 |
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 | Tusker | 1 | 1 | 0 | 0 | 2 | 0 | 3 |
A question about KRA vs Tusker?
Ask Footlab AI: lineups, absences, player form, stats, how the match could unfold.
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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