Models & Methodology
Every BSI model documents its inputs, assumptions, validation approach, and failure modes. No black boxes. If you can't see how it works, you shouldn't trust it.
HAV-F Player Evaluation
Composite player evaluation metric — Hitting, At-Bat Quality, Velocity, Fielding. Percentile-based scoring against the full college baseball cohort.
Win Probability
Real-time win probability estimates based on game state, score differential, inning/quarter, and historical leverage data.
Monte Carlo Simulation
Season outcome projections using thousands of simulated seasons. Conference standings, tournament probability, CWS odds.
Data Quality & Sources
How BSI validates data across 3+ providers before serving it. API response times, freshness guarantees, cross-reference methodology.
Why Document This?
Most sports analytics platforms market model outputs — win probability numbers, projection percentages — without explaining what feeds them. That makes the numbers unfalsifiable. You can't evaluate a prediction you can't inspect.
BSI documents inputs, assumptions, and failure modes because that's what makes analytics trustworthy. A model that admits where it breaks is more useful than one that pretends it doesn't.