Kenya's football scene has become increasingly competitive in recent seasons. Each season, the Super League brings together teams with varying styles and ambitions. Each prediction is built around form, consistency, and key statistical indicators. This allows for a more structured view of performance across all fixtures.
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 34 | ||||
| Darajani Gogo Vihiga United 2026-05-23 |
LLLWL
WLLDL |
X2 | 1:0 |
57.38%
|
| Gucha Stars Luanda Villa 2026-05-23 |
DLLLL
DWWLD |
X2 | 2:1 |
93.92%
|
| Nairobi City MCF 2026-05-23 |
LWLLD
LLDWL |
1X | 0:0 |
84.41%
|
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 31 | ||||
| Mombasa United Migori 2026-05-03 |
LWWWW
WWWWW |
X2 | 0:0 |
96.91%
|
| Luanda Villa Darajani Gogo 2026-05-03 |
WLDLW
LWLDL |
1 DNB | 1:0 |
77.29%
|
| Round 25 | ||||
| Kibera Kisumu All-Stars 2026-03-21 |
LWWLW
LLLLD |
1 DNB | 1:0 |
70.87%
|
| Nzoia Sugar Mwatate United 2026-03-21 |
WDW
LWLDD |
1 | 3:1 |
84.11%
|
Our methodology
We generate Super League predictions using structured statistical models and match data. We evaluate performance using a combination of offensive and defensive data. This ensures each prediction is supported by measurable trends.
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