Seoul vs Daejeon
K League 1, South-Korea · 2026-08-15 · 12:30 CET
WWLDD
DDLWWThe score model makes a Seoul win the most likely outcome at 45%, split on goals (Over 2.5 at 48%). The data's biggest gap to the market: HT under 1.5 goals — 73.0% vs 65.6% implied (+7.4 pts). 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 1.48 – 1.11 Daejeon.
| Market | Data % | Implied | Divergence |
|---|---|---|---|
| Seoul win | 44.7% | 48.3% | -3.7% |
| Draw | 27.7% | 26.9% | +0.8% |
| Daejeon win | 27.7% | 24.8% | +2.9% |
| Over 1.5 goals | 73.8% | — | — |
| Over 2.5 goals | 47.8% | 50.5% | -2.7% |
| Over 3.5 goals | 26.1% | 29.9% | -3.8% |
| Both teams to score | 52.6% | 53.9% | -1.3% |
| HT over 0.5 goals | 61.7% | — | — |
| HT over 1.5 goals | 27.0% | 34.4% | -7.4% |
Model dc_v2.0 · rating sample: Seoul 45.1 / Daejeon 44.4 weighted matches · positive divergence means the data leans above the market — the market usually knows why.
Track your pick
My record →Flat 1u, average market odds, settled either way. Not betting advice — a record, like ours.
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.