Anyang vs Seoul
K League 1, South-Korea · 2026-08-22 · 12:30 CET
DLLDW
WWLWLThe score model made a Seoul win the most likely outcome at 43%, split on goals (Over 2.5 at 50%). It finished 0:7 (HT 0:5) — 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: Anyang 1.18 – 1.49 Seoul.
| Market | Data % | Implied | Divergence | Result |
|---|---|---|---|---|
| Anyang win | 29.0% | 23.5% | +5.6% | ✗ |
| Draw | 27.5% | 27.0% | +0.5% | ✗ |
| Seoul win | 43.5% | 49.6% | -6.1% | ✓ |
| Over 1.5 goals | 75.5% | — | — | ✓ |
| Over 2.5 goals | 49.8% | 50.5% | -0.7% | ✓ |
| Over 3.5 goals | 27.9% | 29.6% | -1.7% | ✓ |
| Both teams to score | 54.5% | 53.5% | +1.0% | ✗ |
| HT over 0.5 goals | 61.0% | — | — | ✓ |
| HT over 1.5 goals | 25.8% | 34.1% | -8.4% | ✓ |
Model dc_v2.0 · rating sample: Anyang 33.8 / Seoul 45.2 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.