## 2 Background on Basketball Strategies and Related Research
### Use of Machine Learning in Basketball Analytics  
[PERSON] and [PERSON] [31] developed a machine learning model to process player tracking data to identify offensive plays in basketball. The trajectory dataset utilised in [31] was provided by SportVU [32]. Each data point within this dataset is stored as a sequence which has the coordinates of the ball and all players on the basketball court. A sequence is recorded every 0.16 s. The authors devised variants of neural network algorithms to model a classifier to recognise basketball tactics based on unlabelled historical data. This study implemented the recurrent neural network (RNN) which can deal with sequential data of variable length. The inputs of the RNN comprise the player coordinates and the outputs are the labels for the different offensive tactics. The authors also describe the development of an autoencoder neural network based on an extra dataset provided by NBA team, the Toronto Raptors, to enhance the model to become a player-specific model. The researchers demonstrated that the model could be used to predict the movement of players, i.e., to forecast future player position [31]. This recurrent neural network achieved a classification performance of 80.6% accuracy. Nevertheless, due to the limited number of training data, the predictive capability of the recurrent network was relatively weak (less than 60%). [PERSON] et al. [19] also developed a supervised machine learning algorithm utilising NBA player tracking data from SportVU. The authors focussed on recognising and analysing ball screen defence (pick-and-roll). Their system takes the unlabelled trajectory data as input, and then derives the time of all ball screens that occur during each game. Finally, classifying how the ball screen was guarded by defence players. In [19], the researchers first defined four players who formed the ball screen tactics: a ball handler, a ball defender, a screener and a screener defender. Secondly, they defined four different categories for the defensive tactics based on the trajectories of these four players. In [19] authors trained a logistic regression machine learning model that uses the distance between four defined characters as features. Finally, through 5-fold cross-validation they achieved an overall average classification accuracy of 69%. In addition, this paper analysed the trends of different players and different teams in the face of the ball screen. Learning long-term behaviour for multi-agent spatiotemporal trajectories is a key challenge in many learning problems [33]. [PERSON] et al., proposed Multi-Agent Generative Behavioural Cloning, referred to as MAGnet, based on a deep learning algorithm. The authors describe MAGnet as a flexible class of generative models that can generate rich multi-agent spatiotemporal trajectories over a long-time horizon [33]. Moreover, the main advantage of MAGnet is that it has a shared structure between agents, and it has a hierarchical latent structure to jointly represent long-term (macro) and short-term (micro) temporal dependencies. Furthermore, the researchers compared MAGnet with a Variational Recurrent Neural Network algorithm (VRNN) [34]. The VRNN algorithm proposed by [PERSON] et al. [34] is built by the combination of a recurrent neural network with variational autoencoder. According to [33], the MAGnet algorithm has significantly higher performance than the VRNN model [34]. The concept of macro-goals allows the observer to analyse the long-term goals of a player and how they change their objectives during game play. [PERSON] et al. suggested that exploring a more powerful probabilistic structure to handle more agents under a complex condition is an avenue worth pursuing. Nearly all basketball tactical analysis studies only analyse the status of a section of players and not the whole team. For example, the pick and roll strategy typically comprises four players--two players on each team--the remaining six players are not analysed.