## 1 Introduction
Past studies have shown that the trajectory data of in-game players can be considered as one of the most effective factors available in machine learning techniques: for example, mapping NBA strategies based on player-tracking data [12], analysing players' shooting style in the NBA using trajectory and body pose [13] and predicting three-point shooting results using recurrent neural networks [14]. Another important avenue of basketball analytics research is focused on predicting the outcome of a basketball play. Often such predictions are performed by developing generative probability models [15] that capture the contextual knowledge from performance analysis studies such as [4; 16; 17]. Furthermore, play-by-play data are an important element in developing useful generative models. However, performance assessment studies of basketball and the associated play-by-play data generated using player tracking data are heavily focused on offensive play metrics [18], and until recently modest effort has been spent on characterizing defensive play. This is mainly due to the ease of recording points, assists and related goal-scoring statistics. The availability of contextual information about defensive plays is thus an important need for development of useful analytical models. The objective of this paper is to demonstrate the use of machine learning to recognise two defensive strategies commonly employed against the popular Pick-and-Roll (PNR) strategy in basketball. The paper proposes a methodology to recognise these defensive plays by analysing the spatio-temporal patterns in player and ball tracking data. The majority of tactical analysis studies in basketball investigate the status of a specific group of players rather than the whole team. The purpose of this study was to develop a computational model using machine learning algorithms to analyse in-game player movement during basketball match-play. Specifically, this paper utilises machine learning algorithms on player and ball tracking data in basketball to analyse the relative movement of in-game players and the ball to explain the strategies employed by the defensive team. The outputs of this work could be used to inform coaching decisions and to devise opponent-specific match strategies and team tactics. In the longer term, it is envisaged that similar techniques may be developed and deployed in a wide range of sporting and non-sporting contexts. The contributions of this paper are as follows:  
* Development of an analytical model to match the one-on-one relationship between players in both teams based on the players' location data. The process of matching is based on the underlying factors behind the trajectory data of players, such as distance, speed vector and the specified zone of each player. * Development of a dynamic strategy classification model that automatically identifies change of defense mechanisms and classifies defensive tactics against PNR into two classes:'switch' and 'trap' [19]. In comparison to previous work, the current study focuses on all players in the team (on-ball players and off-ball players). The rest of this paper is organized as follows: Section 2 presents the work associated with data-driven algorithms for learning strategies in basketball. The proposed methodology for learning defensive strategies in basketball utilising player tracking data is presented in Section 3. Section 4 discusses the experimental setup and results and Section 5 comprises indications of future work.