Correct Score Predictions Today

How These Score Predictions Work

For each match we project expected goals for both sides — home attack strength × away defence strength × the league's home-goals baseline (and the mirror for the away team). Those become the λ (lambda) parameters of two Poisson distributions. We then evaluate P(home = i) × P(away = j) for every (i, j) from 0-0 through 7-7 and rank the results by probability.

The percentage next to each score is the actual Poisson mass for that cell — not an approximation. A 12% score means the model puts 12 units of probability out of 100 on exactly that scoreline. For the deeper methodology, see our correct score betting strategy.

Be Honest About the Maths

Correct score is the hardest market to beat long-term. The top scoreline might show 12% probability, but the bookmaker's implied probability from their odds might be 9%. That 3% gap is your edge — if it's real. Over 50 bets at those margins, you'd expect a profit. Over 5 bets, variance dominates and anything can happen.

The practical takeaway: use these as one input alongside our BTTS analysis and Over/Under tips. If the correct score, BTTS, and Over/Under models all point the same direction (say, 2-1 predicted, BTTS Yes, Over 2.5), that's convergence worth acting on. When they disagree, the match is genuinely unpredictable — and sitting one out is a valid move.