Expanding Expected Goals Methodology in Field Hockey
Authors: Bret R. Myers1, Andrew M. Daly2
1Department of Management and Operations, Villanova University, Villanova, PA, USA
2Department of Athletics, Villanova University, Villanova, PA, USA
Corresponding Author:
Bret R. Myers, Ph.D.
1039 Smithfield LN
Downingtown, PA 19335
bret.myers@villanova.edu
(804) 357-5876
Bret R. Myers, Ph.D. is a Professor of Practice in the Department of Management and Operations in the Villanova School of Business. His research interests focus on sports analytics, specifically, in the areas of team evaluation and managerial decision-making. He is also an Analytics Consultant for the Columbus Soccer Club of Major League Soccer.
Andrew M. Daly is MIS and Business Analytics Major at Villanova University. He is also an analyst and student manager for the Villanova Field Hockey team. In this role, he has both video and data analysis responsibilities and reports directly to the coaching staff.
Expanding Expected Goals Methodology in Field Hockey
ABSTRACT
The purpose of this study is to demonstrate the value of the overarching expected goals methodology in the sport of field hockey by examining performance data in NCAA Division I Field Hockey. Expected Goals (xG), a metric used to represent the likelihood of a shot being a goal, has grown in popularity across multiple sports. The expected goals methodology involves model building through logistic regression. Specifically, two metrics are created through this technique: 1) The standard expected goals model (xG) based on characteristics of the scoring opportunity before the shot is taken and 2) Post-shot expected goals (xGOT) which is updated to reflect whether or not the shot is on target.
Results: In terms of development, the logistic regression models used for the development of the xG and xGOT models both yield high levels of significance for fit (p-values of 4.13e-26 and 2.78 e-16 respectively). In terms of application, the xG and xGOT metrics both have high correlations to goals scored when aggregating on a game-by-game basis (0.76 and 0.77 respectively). Furthermore, the metrics can enhance insights gained from matches, evidenced by additional visualizations provided in this study.
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