Inspiration: Quantifying momentum shifts that traditional stats miss

What it does: Real-time momentum visualization, ML-powered run predictions, historical pattern matching, and interactive time period analysis

How we built it: Full-stack with React frontend, Python FastAPI backend, Node.js proxy, Supabase database, and scikit-learn ML models

Challenges: NBA API deprecation, visualization issues, momentum algorithm tuning, Supabase setup complexity, real-time synchronization

Accomplishments: Complete ML integration, proprietary momentum algorithm, interactive UI, robust data pipeline, comprehensive documentation

What we learned: Sports analytics complexity, API reliability, data visualization challenges, ML integration, full-stack coordination

What's next: WebSocket integration, player impact analysis, advanced ML models, mobile apps, betting integration, expansion to other sports

Tagline: "See the game's momentum shift before it happens"