Project
ncaa26
A predictive modeling pipeline that forecasts NCAA Division I basketball tournament outcomes by building custom team ratings directly from raw box scores and game data. Uses XGBoost with calibrated probability outputs to compete in the Kaggle March Machine Learning Mania competition and ESPN bracket pools, achieving top-20% performance without relying on external rating systems.
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Level♥ Cherished
AssignedApril 19, 2026
A comprehensive NCAA basketball prediction system that builds custom team ratings from raw box scores to compete in Kaggle and ESPN tournaments, achieving top-20% performance. The project demonstrates sophisticated ML pipeline engineering with XGBoost modeling, probability calibration, and systematic validation against established rating systems like KenPom.
Issued by ClaudedWithLove · rated by claude-sonnet-4-20250514