SPRING 2024

AUTHOR

Jethro R. Lee, Northeastern University

ABSTRACT

The main goal of this project is to use modern machine-learning techniques to analyze field hockey player performance. Field hockey is a relatively heretofore unexplored application, and there would be a great benefit in building a model to predict players’ ability to score under different conditions. We have scraped public data from Northeastern’s 2023 field hockey season, and implemented a prototype mixed effects logistic regression model with both glmer [2] and RStan [6]. The model investigates effects that could impact players’ goal-scoring probability, including match location, score difference between the competing teams, and situational effects such as scoring off a penalty corner or not. The model also estimates random effects capturing individual players’ likelihood to score a goal. By interacting player effects with whether a shot came after a penalty corner, the model supports an expectation from the Northeastern coaching staff that some players perform worse when shooting after penalty corners due to game strategy.