Understanding the prediction methodology and calculations
Our sports betting prediction system uses advanced statistical modeling to identify high-value betting opportunities. The system combines multiple mathematical models to calculate the true probability of game outcomes, then compares these probabilities against sportsbook odds to find profitable bets.
Our prediction models are specifically designed for and most accurate with team sports. We deliberately exclude individual sports like tennis, MMA, golf, and Formula 1 because:
Team sports provide: Regular competition, consistent lineups, predictable strength relationships, and clear win/loss outcomes - all essential for accurate statistical modeling.
We gather real-time data from multiple sources:
We maintain dynamic team strength ratings that update after each game:
Example: If a 1600-rated team beats a 1500-rated team (60% expected), their rating increases by ~13 points while the loser drops ~13 points.
We calculate win probabilities using pairwise comparison modeling:
Example: Team with 1600 rating vs 1400 rating = 1600/(1600+1400) = 53.3% win probability
We combine multiple models using Ridge regression to improve accuracy:
We refine probabilities using Isotonic regression for better accuracy:
Why Important: Raw model probabilities may be systematically biased. Calibration ensures our confidence levels accurately reflect real-world outcomes.
We calculate the expected value against sportsbook odds:
High-Value Example:
Our system automatically tracks every prediction to validate performance:
Our methodology is based on proven mathematical principles:
While sports betting markets are generally efficient, temporary inefficiencies exist due to:
Our edge comes from:
Our system focuses on team sports where statistical modeling is most effective:
Game: Chicago White Sox @ Atlanta Braves (Aug 20, 6:16 PM EST)
Prediction: Atlanta Braves Moneyline (-184)