Norway football has developed into a highly competitive and closely contested landscape. Each season, the Obos brings together teams with varying styles and ambitions. Our predictions focus on the stats that matter, from team form to goal trends. This allows for a more structured view of performance across all fixtures.
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 10 | ||||
| Bryne Hodd 2026-05-31 |
LLWWD
WLDWW |
Handicap 2 (0:2) | 1:0 |
69.24%
|
| Egersund Stromsgodset 2026-05-31 |
LDLLW
WDWWW |
X2 | 0:5 |
66.08%
|
| Kongsvinger Asane 2026-05-31 |
LWWDW
WWDLL |
1 | 3:1 |
80.2%
|
| Ranheim Sandnes 2026-05-31 |
LWLDW
WLWLL |
X2 | 5:1 |
55.67%
|
| Raufoss Haugesund 2026-05-31 |
LWWLL
WWDLW |
2 DNB | 3:4 |
87.99%
|
| Strommen Sogndal 2026-05-31 |
LLLLL
LDDWD |
X2 | 2:3 |
82.97%
|
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 9 | ||||
| Asane Raufoss 2026-05-25 |
WDLLL
WWLLL |
X2 | 3:0 |
60.85%
|
| Haugesund Moss 2026-05-25 |
WDLWL
DWLLW |
1 DNB | 3:1 |
74.51%
|
| Hodd Egersund 2026-05-25 |
LDWWD
DLLWL |
X2 | 3:1 |
60.27%
|
| Lyn Strommen 2026-05-25 |
DLWLL
LLLLD |
1 DNB | 1:0 |
80.85%
|
| Odd Ranheim 2026-05-25 |
LLWWW
WLDWL |
1 DNB | 3:1 |
65.81%
|
| Sandnes Sogndal 2026-05-25 |
LWLLD
DDWDW |
X2 | 2:0 |
62.83%
|
| Stabaek Kongsvinger 2026-05-25 |
DLWWW
WWDWD |
X2 | 2:0 |
56.22%
|
| Stromsgodset Bryne 2026-05-25 |
DWWWD
LWWDL |
1 | 1:0 |
83.34%
|
| Round 7 | ||||
| Bryne Strommen 2026-05-16 |
WDLLL
LLDWL |
1 DNB | 4:2 |
77.01%
|
| Haugesund Asane 2026-05-16 |
LWLWW
LLLLL |
1 | 1:1 |
98.15%
|
| Hodd Sogndal 2026-05-16 |
WWDLL
WDWLL |
1X | 2:2 |
59.87%
|
| Lyn Kongsvinger 2026-05-16 |
WLLLW
DWDWW |
2 DNB | 0:3 |
89.72%
|
| Odd Moss 2026-05-16 |
WWWDW
LLWLW |
1 | 2:3 |
95.96%
|
| Stabaek Raufoss 2026-05-16 |
WWWDW
LLLDL |
1 | 1:2 |
97.95%
|
| Stromsgodset Ranheim 2026-05-16 |
WWDWL
DWLWW |
1 DNB | 5:4 |
79.06%
|
| Sandnes Egersund 2026-05-15 |
LLDWL
LWLWW |
X2 | 4:2 |
84.8%
|
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";Our methodology
Each Obos prediction is based on a model-driven evaluation of team performance. Models like Linear Regression are used to identify relationships within performance data. Team form, scoring efficiency, and defensive organization are key inputs. This provides a structured and objective view of each matchup.
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