Zalaegerszegi vs Paks
NB I., Hungary · 2026-08-03 · 17:30 CET
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DLWDLThe score model made a Paks win the most likely outcome at 40%, leaning to goals (Over 2.5 at 57%). It finished 5:2 (HT 3:2) — 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: Zalaegerszegi 1.39 – 1.57 Paks.
| Market | Data % | Implied | Divergence | Result |
|---|---|---|---|---|
| Zalaegerszegi win | 32.6% | 29.6% | +3.0% | ✓ |
| Draw | 27.1% | 25.7% | +1.4% | ✗ |
| Paks win | 40.3% | 44.7% | -4.4% | ✗ |
| Over 1.5 goals | 80.8% | — | — | ✓ |
| Over 2.5 goals | 56.8% | 61.4% | -4.6% | ✓ |
| Over 3.5 goals | 34.4% | 40.6% | -6.2% | ✓ |
| Both teams to score | 60.8% | 63.2% | -2.4% | ✓ |
| HT over 0.5 goals | 67.5% | — | — | ✓ |
| HT over 1.5 goals | 33.7% | 42.0% | -8.3% | ✓ |
Model dc_v2.0 · rating sample: Zalaegerszegi 42.3 / Paks 42.2 weighted matches · positive divergence means the data leans above the market — 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.