Basketball Teams and Leagues Analysis
A data science project that applies machine learning to NBA statistics, analyzing how individual player performance metrics correlate with team success. Features regression models, efficiency score calculations, and comprehensive data visualizations.
Tech Stack
The Problem
Understanding the quantitative relationship between individual player performance and overall team success in basketball requires sophisticated statistical analysis beyond traditional box scores.
Approach
Collected and preprocessed NBA team and player statistics using Pandas. Applied scikit-learn regression models to predict team performance based on player metrics. Created Seaborn and Matplotlib visualizations to communicate findings.
Key Challenges
- Feature selection from hundreds of available statistics
- Handling multicollinearity between related performance metrics
- Accounting for team chemistry factors not captured in individual stats
Results
Identified key individual metrics most predictive of team success and built models with strong predictive accuracy for regular season performance.