• Utilized 2021-2023 NFL play-by-play data and Python for data cleaning, transformation, and statistical analysis. Applied machine learning techniques for predictive modeling and feature selection. • Developed models to evaluate defensive run gap performance, integrating team and player metrics to identify key tendencies and gaps in run defense. • Leveraged decision trees and random forests to uncover patterns in defensive alignments and run play outcomes, providing actionable insights for strategic improvements.
This analysis uses the nfl_data_py library. nfl_data_py: https://github.com/cooperdff/nfl_data_py