// ABOUT
A personal lab for applying data science techniques to NBA data. Each project explores a real analytical question — from player valuation to game strategy — using real-world data, statistical methods, and machine learning. The goal is reps: building intuition for data science through something I care about.
// PROJECTS
Player Efficiency Analysis
Exploring PER and advanced metrics to identify undervalued players across the league.
Shot Quality Model
Building an ML model to predict shot quality using player tracking and spatial data.
Lineup Optimization
Using linear programming to find optimal five-man lineups for specific game scenarios.
Trade Value Predictor
Predicting player trade values using historical salary, age curves, and performance data.
Play-by-Play Clustering
Clustering play patterns with unsupervised learning to identify team offensive tendencies.
Draft Class Projection
Projecting NBA draft potential from college stats using historical comp analysis.