Decision tree induction is a novel approach to exploring attacker-defender interactions in many sports. In this study hockey was chosen as an example to illustrate the potential use of decision tree inductions for the purpose of identifying and communicating characteristics that drive the outcome. Elite female players performed one-versus-one contests (n = 75) over two sessions. Each contest outcome was classified as either a win or loss. Position data were acquired using radio-tracking devices, and movement-based derivatives were calculated for two time epochs (5 to 2.5 seconds, and 2.5 to zero seconds before the outcome occurred). A decision tree model was trained using these attributes from the first session data, which predicted that when the attacker was moving at ≥ 0.5 m · s(-1) faster than the defender during the early epoch, the probability of an attacker's win was 1.00. Conversely, when the speed difference at that time was below this threshold the probability of a loss was 0.78. Secondary attributes included defender speed in the lateral direction during the early epoch, and angle of attack (i.e., angle between the respective velocity vectors of the attacker and defender) during the late epoch. The model was then used to predict outcomes of one-versus-one contests from the second session (accuracy = 0.643; area under the receiver operating characteristic (ROC) curve = 0.712). Moreover, decision trees provide an intuitive framework for relating spatial-temporal concepts to coaches, and the suitability of decision trees for analysing the features of one-versus-one exchanges are discussed.
- Athletic Performance
- Competitive Behavior
- Data Interpretation, Statistical
- Decision Trees