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This is a Quantitative Study study.

October 5, 2025Open Access

Statistical Evaluation of Outdoor Field Hockey Penalty Corners

SHSamuel HughesSt. Jude Children's Research Hospital GMG. A. MatthewsImperial Valley College JYJun YanQingdao University of Science and Technology

Key Points

The study shows that specific techniques increase the likelihood of scoring from penalty corners, and vice versa.
Using XGBoost, an area under the curve score of 0.667 indicates model accuracy on out-of-sample data.
Various machine learning algorithms were evaluated, focusing on predictive ability and identifying key scoring factors.
The findings suggest strategic decisions can significantly affect scoring potential during penalty corners.

Abstract

Penalty corners stand out as pivotal goal-scoring opportunities in field hockey, crucial to a team’s triumph. This study harnesses data from women’s collegiate field hockey games to formulate a statistical model predicting the likelihood of scoring a penalty corner, contingent on the strategies deployed. Various machine learning algorithms are compared to ascertain the most predictive model and to dissect the paramount factors influencing penalty corners. The XGBoost model emerges superior, boasting an area under the curve (AUC) score of 0.667 on out-of-sample observations. With other predictors held constant, the model reveals that drag flicks, sweep shots, and deflections are positively associated with goal occurrences, while, intriguingly, direct shots—despite their prevalence—are negatively associated with scoring probability. KEYWORDS: College Sports; K-Nearest Neighbor; Lasso; Quantitative Analysis; Random Forest; Sporting Strategy; Sports Analytics; XGBoost

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