How Predictor Network Works

Every prediction starts with Monte Carlo simulation. We run 10,000 bracket or tournament simulations per update, feeding in team ratings, player efficiency metrics, and tempo-free statistics. Each simulation plays out every matchup independently, and the final probabilities reflect how often each outcome occurred across all 10,000 runs.

Simulations alone miss information that the betting market captures. We pull real-time futures odds from 10+ sportsbooks via The Odds API, remove the bookmaker margin using multiplicative devigging, and average them into a consensus line. When our model disagrees with the market by more than a threshold, we flag that delta so users can see where statistical models and money disagree.

The third layer is sport-specific analytics. For college basketball, we pull adjusted efficiency ratings from BartTorvik and ESPN BPI. For golf, we use strokes gained data from Data Golf alongside course-fit scoring. For football, we anchor to historical Super Bowl score distributions and current offensive scheme data. Each sport gets a model tuned to the statistics that explain the most variance in that sport.

• BartTorvik/Adjusted efficiency ratings and tempo-free stats for NCAA basketball
• ESPN BPI/Basketball Power Index for team strength comparisons
• Data Golf/Strokes gained metrics, course fit, and historical golf performance
• The Odds API/Real-time futures odds from 10+ sportsbooks, devigged into consensus lines
• Expert Sentiment/Podcast and article analysis scored on a bullish/bearish scale with confidence weighting

We track every prediction against actual results. When the tournament ends, the accuracy numbers are final and public. Our goal is to be the most transparent sports prediction platform available: every probability is shown before the event, and every result is measured after.

Predictions are for entertainment purposes only. Past performance does not guarantee future results.