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.
© Copyright 2023 The Sport Journal. U.S. Sports Academy. Alle Rechte vorbehalten.