Characterization of Successful Patterns of Play in Field Hockey from Wearable Sensor Data

Analyzing patterns of play in team sports remains challenging despite advances in GPS tracking technology. While individual player statistics are well-documented, understanding what makes specific team plays successful requires extracting meaningful features from spatial–temporal data. This study addresses the question: can machine learning algorithms effectively characterize successful versus unsuccessful attacking plays in field hockey based on GPS-derived team movement patterns? We analyzed GPS data from a national men’s field hockey team recorded during the 2018 Men’s Hockey World Cup organized by the International Hockey Federation (FIH). We developed novel team-based features from longitude and latitude coordinates, including player closeness (~8–15 m Inter-Player spacing), corner positioning, movement energy via discrete wavelet transforms, and velocity–acceleration metrics. Thirteen features describing 63 attacking plays (42 successful, 21 unsuccessful) were classified using Random Forest (RF). Success was defined as achieving a goal, shot on goal, or attacking circle entry. Using 5-fold cross-validation repeated 10 times, the Random Forest model achieved a mean classification accuracy of 78% (95% CI: 75–81%) and an AUC of 0.88 (95%CI: 0.85–0.91). Sensitivity analyses showed that removing Opposition had minimal impact on predictive performance (accuracy 77.1%, AUC 0.862), while leave-one-match-out cross-validation yielded variable performance across held-out matches (accuracy 60–82%, AUC 0.69–0.96). The most discriminative features were player closeness during plays, high maximum acceleration (>2.5 m·s−2), number of athletes involved (9+ players), and mean distance traveled (~10 m). Play duration was also critical, with successful plays lasting 3–6 s. Machine learning can effectively characterize successful field hockey plays from GPS data when appropriate team-oriented features are extracted. Although the novel wavelet-based player energy metric showed a negative predictive contribution in its current form, it may still show promise for future load management applications. This computational methodological framework is potentially generalizable to other team sports requiring spatial–temporal pattern recognition.