Game-Related Statistics Discriminate between Winning and Losing Teams in the Field Hockey

Background. There is a dearth of literature on match analysis in field hockey. Time-motion analysis, the relationship between play patterns and goal-scoring opportunities, and penalty corner strategies are currently available in the literature on field hockey. Nevertheless, none of the studies have identified the factors contributing to winning. These factors could be used to help coaches develop a specific training schedule, monitor playing patterns, improve player selection processes, specify each player's role, and evaluate their overall performance. Objectives. The present study aimed to identify game-related statistics in Field Hockey that best discriminate between winning and losing teams. The data was gathered from the 2018 Men's Hockey World Cup matches. Methods. The grouping variable selected for this study was Match Results (i.e., Win/Lose). Whereas the selected game-related statistics were Ball Possession, Shots Attempted, Pass Accuracy, Circle Entries, and Penalty Corner. A total of 36 matches were analyzed. Independent samples t-test was used to compare the mean difference and discriminant analysis was applied to identify the game related statistics that best discriminate between winning and losing teams. Results. The Results have shown a significant (p<0.05) mean difference for all the selected game-related statistics and the developed discriminant model was also found to be significant (p=0.000). The interpretation of the generated discriminant functions was examined based on the Structure Coefficients (SC) = |0.30|. Conclusion. According to the statistical significance of the model and SC, the variables which majorly contributed to discriminating between winning and losing teams were circle entries (SC=.663), ball possession (SC=.415) and shots attempted (SC=.307). Winning teams were examined to be ahead of losing teams in all the game-related statistics.