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NFL EPA Analysis
Project type
Academic Data Analysis Project
Date
April 2025
Location
Ashland University
This project focused on analyzing which player attributes and in-game situations impact Expected Points Added (EPA) in NFL games. Using a real-world dataset from Kaggle, I built and compared multiple statistical models in RStudio to determine which variables best explain play outcomes. The goal was to identify whether player metrics like speed and acceleration or situational factors like down and yards to go had a greater influence on team performance.
Tools & Skills Used:
-RStudio
-Multiple Linear Regression
-Robust Regression
-Ridge Regression
-Model Selection (AIC & BIC)
-Breusch-Pagan Test (Heteroskedasticity)
-VIF (Multicollinearity Testing)
-Data Cleaning & Merging
-Sports Analytics