Project
ncaa26
A predictive modeling pipeline for NCAA Division I basketball tournament outcomes that derives custom team ratings directly from raw box scores and tournament data without relying on external rating systems. Built with XGBoost and calibrated probability models, it achieved top-20% performance on the Kaggle March Machine Learning Mania competition by engineering features like ELO ratings, Simple Rating System scores, efficiency metrics, and momentum indicators. The project demonstrates spec-driven development practices with automated feature engineering, cross-validation, and model orchestration entirely in Python.
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Level♥ Cherished
AssignedApril 16, 2026
NCAA26 is a comprehensive predictive modeling pipeline for NCAA basketball tournament outcomes that builds custom team ratings from raw box scores without external rating systems, achieving top-20% performance on Kaggle. The project demonstrates spec-driven development with Claude Code, featuring engineered metrics like ELO ratings, SRS scores, and momentum indicators orchestrated through automated Python pipelines.
Issued by ClaudedWithLove · rated by claude-sonnet-4-20250514