Qatar's football scene has become increasingly competitive in recent seasons. The Qsl plays a central role in shaping the national football landscape. Our approach looks at both attacking output and defensive stability across fixtures. This helps build a clearer picture of how teams perform over time.
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 22 | ||||
| Al Rayyan Al Arabi 2026-04-27 |
WWWWD
LDWLW |
1 DNB | 4:0 |
85.77%
|
| Al Wakra Al Sailiya 2026-04-27 |
DL
WLD |
X2 | 3:1 |
90.47%
|
| Umm-Salal Al Ahli Doha 2026-04-27 |
LLLWD
LWWD |
X2 | 1:3 |
66.7%
|
| Al-Sadd Shamal 2026-04-27 |
LWWD
WLW |
1 | 3:2 |
83.89%
|
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 21 | ||||
| Al Rayyan Umm-Salal 2026-04-08 |
DWWWL
LLWDW |
1 | 3:0 |
58.93%
|
| Round 20 | ||||
| Al Rayyan Al-Sadd 2026-04-04 |
WWWLL
LLWWL |
1X | 1:1 |
87.48%
|
| Shamal Umm-Salal 2026-04-04 |
LW
LWDWD |
Handicap 2 (0:2) | 3:2 |
93.7%
|
| Al-Shahaniya Al Arabi 2026-04-03 |
LLWWL
LWWD |
2 DNB | 0:2 |
95.15%
|
| Qatar SC Al Wakra 2026-04-03 |
DW
LLDLW |
1X | 1:4 |
69.65%
|
| Al Ahli Doha Al Sailiya 2026-04-03 |
WDLLW
WL |
1 DNB | 2:2 |
76.15%
|
Our methodology
Our Qsl predictions are built using a data-driven approach based on statistical modeling. This approach includes Linear Regression models to quantify team performance. Key variables include goal-scoring efficiency, defensive strength, and recent form. The goal is to offer a clearer and more objective view of each fixture.
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