## 5 Conclusions  
The objective of this paper was to classify defensive strategies in basketball by utilising spatio-temporal pattern recognition using player and ball tracking data. A tracking dataset of player and ball trajectories in 32,377 possessions from nearly 630 basketball games in 2012/13 NBA season from STATS LLC was used in the study. The objective of this study was to classify two common defensive strategies (known as switch and trap) used against a popular offensive strategy known as pick-and-roll, by considering different features from the play-and-ball tracking data. To develop a substantial annotated dataset, an analytical model was developed with a capability to automatically identify and label trajectories that contain switch and trap defensive plays. This analytical model that recognised defensive plays was based on three features: the similarity of the movement vectors of the players; the distance between players, and the defensive zone of the players. Subsequently, a classification model was learnt to classify the defensive strategies in situations where the defensive relationship of players changed. The model extracted raw location data and features derived from the location data such as (velocity/distance) to train the classification model, which was able to classify \"switch\" and \"trap\" strategies in basketball. Different types of classifiers were evaluated such as, Support Vector Machines (SVM), Decision Trees and K-Nearest Neighbour (KNN). Considering both classifier performance and complexity, SVMs were deemed the best solution, which produced a classification accuracy of 68.9%. While the current work considered only \"switch\" and \"trap\", there are many more defensive strategies involved with Basketball. Future research may also wish to consider alternative strategies for defending strategies such as pick-and-roll, and perhaps consider defensive moves against other offensive strategies such as pick-and-pop. Furthermore, alternative methods to label large spatio-temporal datasets would also lead to better outcomes, as compared to analytical method proposed in this paper.  
The outputs of this study illustrate the suitability of player tracking data to learn competitive game-related strategies employed in team sports such as basketball. Data-driven methods such as the machine learning methodology presented in this paper, can provide useful insights into game play. Furthermore, such models may enable the improvement of analytical software by providing additional contextual information related to defensive plays, in addition to the play-by-play data that are commonly available. Thus, facilitating advanced analytical solutions that has the potential to inform player development, coaching strategies and game specific tactics.  
Conceptualization, C.T. and V.D.S.; methodology, C.T. and V.D.S.; software, C.T.; validation, C.T., S.S., and V.D.S.; data curation, V.D.S.; writing--original draft preparation, C.T., V.D.S., M.C., S.S.; writing--review and editing, M.C., S.S., V.D.S.; funding acquisition, V.D.S., S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Engineering and Physical Sciences Research Council, grant number EP/T000783/1.  
The authors declare no conflicts of interest.