PredictionPitch produces match probabilities using an ensemble of statistical modeling and machine-learning calibration. We publish probabilities only when data quality is sufficient.
ELO-style ratings measure long-term quality, form shifts, and home advantage effects. Every team carries a rating that updates after each match—beating strong opponents counts more than beating weak ones.
Poisson and Dixon-Coles style modeling produces realistic goal distributions for low-scoring football. Rather than predicting a single scoreline, we estimate the probability of 0, 1, 2, 3+ goals—and derive match outcome, Over/Under, and BTTS probabilities from those distributions.
Multiple models are blended and calibrated with machine learning. This corrects systematic bias (e.g., overconfidence in certain leagues or match types) and improves long-term accuracy.
We compare what the model believes with what the market implies. Odds are converted to implied probabilities so the comparison is apples-to-apples.
If model probability meaningfully exceeds implied probability, we may flag the match as a Value Bet. Most matches won't qualify—markets are generally efficient. When value does appear, we track the outcome transparently.
No guaranteed winners. No certainty—football is inherently noisy. No "inside info." PredictionPitch is built for long-term evaluation, not short-term hype.