Authors: Bret R. Myers, Ph.D.1, Michael Burns2, Brian Q. Coughlin3, Edward Bolte4
1Department of Management and Operations, Villanova University, Villanova, PA, USA
2Villanova School of Business, Villanova University, Villanova, PA, USA
3Department of Athletics, Villanova University, Villanova PA, PA, USA
4Department of Athletics, Villanova University, Villanova PA, PA, USA
Bret R. Myers, Ph.D.
800 E Lancaster Avenue
Villanova, PA 19085
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
Michael Burns is an MBA Candidate and Graduate Research Fellow at Villanova School of Business. Michael is also the Director of Operations for the Men’s Soccer team at Villanova University.
Brian Q. Coughlin is the Director of Men’s Lacrosse Operations at Villanova and also has both a BBA and MBA from Villanova School of Business. Brian is also a Data Analyst at goPuff.
Edward Bolte is a student at Villanova University and student manager on the Lacrosse team. Edward is majoring in Civil Engineering
On the Development and Application of an Expected Goals Model for Lacrosse
The purpose of this study is to develop and apply an Expected Goals metric in lacrosse for team evaluation. Expected Goals is a metric that is used to represent the likelihood of a shot being a goal. The metric has gained traction in both soccer and hockey and has proven to add information and value in both team and player evaluations in both sports respectively. Like in soccer and hockey, the Expected Goals model for lacrosse in this paper is developed using 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 < 0.001). The xG and xGOT metrics have higher correlations to team winning percentage (0.65 and 0.75) than their counterpart statistics of shots and shots on target. In terms of application, teams in the sample that had more xG than their opponents won 73% of the time as opposed to winning only 65% of the time when they outshoot their opponents. Similarly, teams in the sample that had more xGOT than their opponents won 71% of the time as opposed to only 62% of the time when they have more shots on target than their opponents. The evidence in this study suggests that using Expected Goals as a measure of attacking performance adds both value and information that can be useful for team evaluation.(more…)