Exact Score Predictions: Why ~10% Hit Rate Is Strong
August 3, 2025
Predicting the exact final score is a different beast than picking 1X2. Depending on the league and goal environment, the plausible score set contains 15–25 outcomes (0‑0, 1‑0, 2‑0, 2‑1, 1‑1, 0‑1, …). A flat random pick lands near 4–6%. Sustaining ~10% over a large sample is roughly 2× the random baseline and compares favorably to most public models.
How we do it
We do not guess scores directly. We model goal distributions for each side with:
- Expected goals (xG) adjusted for opponent quality.
- Shot volume and shot quality splits (open play vs set pieces).
- Styles that inflate variance (high press, transition heavy) vs dampen it (compact low blocks).
- Rest, travel and rotation risks which shift expected production.
From those, we simulate game states and read off the modal score (most probable) plus the top‑k alternatives.
When to use (and when not)
- Use exact scores for small, tactical stakes—they are volatile.
- Favor matches with clear style asymmetry (e.g., elite attack vs passive defense).
- Avoid low‑information spots: heavy rotation, cup ties with unknown lineups, or derbies with inflated variance.
Linking to other markets
Exact scores sing when combined with 1X2 and totals:
- If we favor Home + Under 2.5, the mode often clusters around 1‑0 / 2‑0.
- If we like Over 3.0, look to 2‑2 / 3‑1 / 3‑2 ladders.
Calibration beats bravado
We publish probabilities, not just a single pick. If we label 1‑0 at 14%, expect it to land about 14 times per 100 similar games in the long run. That’s how you avoid the illusion of control and keep bankroll discipline.
Takeaway: A 10% hit rate is meaningful as long as you size bets conservatively and use score information to guide related markets rather than chase longshots for their own sake.