Djoliba vs USC Kita: result 2-2 and analysis
Première DivisionMatchday 14Kick-off on July 30, 2016 at 04:00 PM (UTC)Stade Omnisports Modibo Keïta, Bamako
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What was the result of Djoliba vs USC Kita?
Djoliba and USC Kita drew 2-2 on July 30, 2016.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Djoliba have 7 wins, 7 draws and 1 defeat; USC Kita have 3, 6 and 4.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 3, 2016 | USFAS Bamako(away) | 0-1 | Première Division |
| Jun 27, 2016 | Réal Bamako(home) | 2-0 | Première Division |
| Jun 18, 2016 | Mamahira(home) | 3-0 | Première Division |
| Jun 13, 2016 | CSK(away) | 5-0 | Première Division |
| May 15, 2016 | Police(away) | 0-0 | Première Division |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 3, 2016 | Réal Bamako(home) | 3-0 | Première Division |
| Jun 24, 2016 | CSK(away) | 0-1 | Première Division |
| Jun 17, 2016 | Bakaridjan(home) | 2-0 | Première Division |
| Jun 12, 2016 | Debo(home) | 0-0 | Première Division |
| May 14, 2016 | USFAS Bamako(away) | 1-1 | Première Division |
What does the head-to-head say?
In the last meeting: 0 Djoliba wins, 1 draw, 0 USC Kita wins.
| Date | Score | Competition |
|---|---|---|
| Apr 17, 2016 | USC Kita 1-1 Djoliba | Première Division |
How many goals should we expect?
Djoliba score 1.3 and concede 0.3 goals per match over their last 15; USC Kita score 0.8 and concede 0.8.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Djoliba | 1.3 | 0.3 | 10 | 4/15 | 4/15 |
| USC Kita | 0.8 | 0.8 | 5 | 5/13 | 3/13 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 4 | Djoliba | 2 | 1 | 1 | 0 | 2 | 0 | 4 |
A question about Djoliba vs USC Kita?
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