Correct Score Tips & Predictions for Today

We model each match with expected goals (xG) derived from attack/defence strength, schedule context and league baselines. A Poisson goal model converts those xG values into exact scoreline probabilities and we surface the top edges only.

In low‑tempo or defensively solid leagues, the base probability mass sits on 0–1 goals per team. Where xG is compressed and prices are inflated, 1‑0 and 1‑1 frequently offer the best value versus market.

High shot volume, aggressive pressing or poor defensive metrics raise xG variance. In those fixtures our model shifts probability to 2‑1/2‑2, provided the market hasn’t fully priced the risk.

Use disciplined bankroll management. We suggest small fractions of Kelly (or flat units) and, when variance is high, split stakes across two to three top scorelines rather than going all‑in on one.

Yes. Many users pair the primary scoreline with correlated markets (BTTS, Over/Under) to reduce downside while retaining positive expected value.