Seoul vs Daejeon
K League 1, South-Korea · 2026-08-15 · 12:30 CET
WWLWL
LWWDDThe score model made a Seoul win the most likely outcome at 45%, split on goals (Over 2.5 at 48%). It finished 4:2 (HT 0:1) — 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: Seoul 1.47 – 1.11 Daejeon.
| Market | Data % | Implied | Divergence | Result |
|---|---|---|---|---|
| Seoul win | 44.6% | 48.5% | -3.8% | ✓ |
| Draw | 27.7% | 26.9% | +0.8% | ✗ |
| Daejeon win | 27.7% | 24.6% | +3.1% | ✗ |
| Over 1.5 goals | 73.9% | — | — | ✓ |
| Over 2.5 goals | 47.8% | 50.5% | -2.7% | ✓ |
| Over 3.5 goals | 26.1% | 30.1% | -4.0% | ✓ |
| Both teams to score | 52.6% | 54.0% | -1.4% | ✓ |
| HT over 0.5 goals | 61.7% | — | — | ✓ |
| HT over 1.5 goals | 27.0% | 34.1% | -7.2% | ✗ |
Model dc_v2.0 · rating sample: Seoul 44.8 / Daejeon 44.3 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.