Mamahira vs USC Kita: result 1-2 and analysis
Première DivisionMatchday 18Kick-off on August 2, 2017 at 04:00 PM (UTC)Stade Mamadou Konaté, Bamako
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What was the result of Mamahira vs USC Kita?
USC Kita won 1-2 on August 2, 2017.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Mamahira have 4 wins, 6 draws and 5 defeats; USC Kita have 10, 1 and 4.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 29, 2017 | COB(home) | 1-1 | Première Division |
| Jul 24, 2017 | LC Ba(away) | 1-6 | Première Division |
| Jul 9, 2017 | ASOM(home) | 1-2 | Première Division |
| Jun 22, 2017 | Duguwolofila(home) | 0-0 | Première Division |
| Jun 16, 2017 | AS Bamako(away) | 1-0 | Première Division |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 29, 2017 | Duguwolofila(away) | 1-0 | Première Division |
| Jul 25, 2017 | Onze Créateurs(home) | 0-2 | Première Division |
| Jul 8, 2017 | Bakaridjan(home) | 2-0 | Première Division |
| Jun 22, 2017 | Nianan(away) | 2-1 | Première Division |
| Jun 17, 2017 | LC Ba(home) | 1-0 | Première Division |
What does the head-to-head say?
Across the last 3 meetings: 0 Mamahira wins, 1 draw, 2 USC Kita wins.
| Date | Score | Competition |
|---|---|---|
| Feb 5, 2017 | USC Kita 2-0 Mamahira | Première Division |
| Aug 27, 2016 | Mamahira 0-0 USC Kita | Première Division |
| May 8, 2016 | Mamahira 0-1 USC Kita | Première Division |
How many goals should we expect?
Mamahira score 1.1 and concede 1.4 goals per match over their last 15; USC Kita score 1.1 and concede 0.8.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Mamahira | 1.1 | 1.4 | 5 | 8/15 | 8/15 |
| USC Kita | 1.1 | 0.8 | 8 | 3/15 | 4/15 |
Over the last 15 matches in all competitions.
A question about Mamahira 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