Thun vs Lausanne
Super League, Switzerland · 2026-09-02 · 20:30 CET
DLLLW
LDLLLThe score model made a Thun win the most likely outcome at 61%, with goals more likely than not (Over 2.5 at 71%). It finished 0:0 (HT 0:0) — the most likely outcome did not come 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: Thun 2.37 – 1.29 Lausanne.
| Market | Data % | Implied | Divergence | Result |
|---|---|---|---|---|
| Thun win | 61.2% | 45.5% | +15.7% | ✗ |
| Draw | 19.3% | 26.4% | -7.1% | ✓ |
| Lausanne win | 19.5% | 28.1% | -8.6% | ✗ |
| Over 1.5 goals | 88.3% | — | — | ✗ |
| Over 2.5 goals | 70.9% | 58.5% | +12.4% | ✗ |
| Over 3.5 goals | 49.8% | 37.6% | +12.2% | ✗ |
| Both teams to score | 66.0% | 61.5% | +4.4% | ✗ |
| HT over 0.5 goals | 76.2% | — | — | ✗ |
| HT over 1.5 goals | 41.9% | 39.9% | +1.9% | ✗ |
Model dc_v2.0 · rating sample: Thun 28 / Lausanne 44.2 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.