Ljungskile vs Oster
Superettan, Sweden · 2026-08-15 · 13:00 CET
LDWLL
DLLWLThe score model made an Oster win the most likely outcome at 39%, split on goals (Over 2.5 at 53%). It finished 2:3 (HT 2:2) — the most likely outcome came in. Generated from the data — the market usually knows why it disagrees.
Scoreline probabilities
Priced from the same score distribution as every market below.
First goal · head-to-head
Full Market Baseline (Dixon-Coles)
One statistical model of the final score — every probability below derives from the same score distribution, so they can never contradict each other. Expected goals: Ljungskile 1.37 – 1.44 Oster.
| Market | Data % | Implied | Divergence | Result |
|---|---|---|---|---|
| Ljungskile win | 35.8% | 50.3% | -14.5% | ✗ |
| Draw | 25.3% | 25.4% | -0.1% | ✗ |
| Oster win | 38.9% | 24.2% | +14.7% | ✓ |
| Over 1.5 goals | 77.0% | — | — | ✓ |
| Over 2.5 goals | 53.2% | 61.3% | -8.1% | ✓ |
| Over 3.5 goals | 31.0% | 41.3% | -10.3% | ✓ |
| Both teams to score | 56.9% | 62.6% | -5.7% | ✓ |
| HT over 0.5 goals | 66.0% | — | — | ✓ |
| HT over 1.5 goals | 30.9% | 42.0% | -11.1% | ✓ |
Model dc_v2.0 · rating sample: Ljungskile 15.7 / Oster 27.3 weighted matches · positive divergence means the model sits above the market — descriptive, not a recommendation; the market usually knows why.
How to read this page: probabilities are what this league's historical first-goal-time and scoring data alone imply — they know nothing about injuries, motivation or weather, and the market usually does. We publish every output, including when the data and the market agree. Statistics, not betting advice — never bet more than you can afford to lose.