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Cleveland Browns Field Goal Kicking Analytics: What Makes the Perfect Kick?
- Program: Computer Science
- Course: EN.601.513 Group Undergraduate Project
- Year: 2025
Project Description:
This project analyzes the Cleveland Browns’ practice field goal data to examine how variables like ball speed, launch angle, and apex influence kick success. Using linear regression, Random Forest models, and XGBoost, we predicted make probabilities across various conditions and distances. These probabilities were scaled using in-game Expected Points Added (EPA) values from nflfastR to estimate the potential impact of each practice kick. A bin-by-bin EPA analysis revealed areas where kickers underperform in games compared to practice, helping identify opportunities for targeted coaching. We also applied league-average scaling curves to evaluate how Browns kickers’ practice-to-game transition compares with the rest of the NFL. Results indicate that mechanical adjustments—such as increasing ball speed on longer kicks—can substantially enhance projected game EPA, supporting data-driven performance optimization.
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Cleveland Browns Field Goal Analytics
