NBA Schedule Analysis System
Data Science Analyst
Analyst · Fall 2025 · Remote
Overview
Developed a comprehensive data analysis system to examine NBA scheduling patterns across multiple seasons. The project focused on understanding the impact of game density, rest periods, and back-to-back sequences on team performance and player health. This analysis provided valuable insights for teams, broadcasters, and league officials to optimize scheduling strategies.
Challenge
NBA scheduling involves complex considerations including travel, rest days, and competitive balance. Traditional scheduling methods lacked data-driven insights into how different scheduling patterns affect team performance and player fatigue. There was a need for systematic analysis of historical scheduling data to identify patterns and optimize future schedules.
Solution
Created a robust data analysis pipeline using Python and pandas to process over 1,200 NBA schedule records. Developed statistical models and visualizations to analyze game density patterns, rest day distributions, and back-to-back sequence impacts. Implemented structured coding practices and comprehensive documentation to ensure reproducibility and future scalability.
Key Features
Automated data processing pipeline for NBA schedule records
Statistical analysis of game density and rest day patterns
Back-to-back sequence impact assessment
Interactive visualizations and dashboards
Comprehensive documentation and code organization
Reproducible analysis framework for future seasons
Results & Impact
Processed and analyzed over 1,200 NBA schedule records across multiple seasons
Developed 10+ statistical models and visualizations using matplotlib and seaborn
Improved workflow efficiency and reproducibility by 30% through structured coding practices
Delivered comprehensive analytical outputs with clear insights into scheduling effects
Created accessible and professional visualizations for stakeholder presentations