Decoding the Tackle: Using a Machine Learning Approach to Understand Direct Head Contact Events in Elite Women's Rugby

Abstract

Concerns about the cumulative effects of head acceleration events in rugby are growing, but how tackle events lead to direct head contact in women's rugby remains underexplored. This cross-sectional study aimed to develop and evaluate a machine learning model to identify characteristics associated with direct head contact and incorrect tackler head placement in elite women's rugby. Match situational and precontact technical characteristics (n = 31) from 1500 randomly selected tackle events were coded visually and retrospectively analyzed from the 2022–23 Women's Six Nations Championship. A machine learning model was developed and evaluated using a grid search with 5-fold cross-validations and F₁ scores (i.e., a measure of predictive performance). The top modifiable characteristics associated with the target outcomes across 100 test sets were identified by mutual importance and decision tree modeling. The top modifiable characteristics linked to direct head contact to the tackler were incorrect head placement, coming to balance, and foot placement. Tackle direction, point of contact on the tackler, and multiplayer tackles were key characteristics for incorrect tackler head placement. Tackler drop height, front/oblique tackle direction, and multiplayer tackles were strongly associated with direct head contact to the ball-carrier. Incorrect tackler head placement, the direction of tackle, tackler drop height, and multiplayer tackles are key characteristics in direct head contact events in elite women's rugby. Addressing these characteristics should be prioritized in contact training practices, education resources, and law enforcement to enhance player safety and direct head contact events in the women's game.