## 4 Experimental Results and Discussion
### Application of the Proposed Method for Basketball Analysis  
Team sport strategy enables individual and group actions to be organized in order to produce collective, creative and unpredictable execution of the team's actions [28]. Development of analytical tools that can be used to predict the outcomes of different game strategies is a trending field of study in sports analysis. In the past, extracting the player and ball location was a cumbersome process that involved extensive video analysis. Availability of player and ball tracking data is enabling the researchers to develop such analytical tools.  
Due to the ease of recording points, assists, and related goal-scoring statistics, most play-by-play datasets often focus on offensive analytics [18]. Therefore, the presented model in this paper focused on the defensive tactics. Specifically, we focus on the defensive actions against pick-and-roll (PNR), which is the most commonly used offensive strategy in basketball [5]. The proposed machine learning model can identify two defensive tactics against PNR, known as switch and trap, by considering features derived from player and ball tracking data. The model achieved an overall accuracy of 69%. Although, the current model was trained to identify only switch and trap, the methodology can be replicated to learn other tactics, provided labelled training data can be sourced.  
The proposed model can be used for different applications of basketball analytics. For example, it can be used for match analysis to automatically retrieve a given defensive strategy from player and ball tracking data. After retrieval, the analysts can see how different opponents defend PNR, and analyse the relative effectiveness of different defensive strategies. Such information can be very useful to design strategies before games to thus providing competitive advantage through analytics. Data driven ghosting schemes are quite a useful and emerging tool for analysing the defensive behaviour of a team [39]. However, as of present such schemes are strictly based on tracking information and some basic contexts such as team roles, but lack crucial important contextual information such as defensive types and game period. However, contextual information regarding different defensive moves cannot directly be accessed from play-by-play data available. Hence, our proposal can be used as a method to embed additional contextual knowledge for player tracking data.  
There has been a significant interest in the sports analytics research community generative probability models for predicting the outcomes of games. These models are predominantly based on the available play-by-play data [29] and encode the knowledge of analytical studies on basketball performance [30]. However, the models such as [29], or [30], do not take in to account the complex nature of dynamical interactions between players. The play-by-play data that are collected for basketball matches do not encode many offensive or defensive strategies, but only the high-level events such as shots, passes, dribbles, chances and fouhs. The presented tool to recognise defensive moves against pick and roll from player-tracking data is an example of a model that represents dynamics of a subset of players, can enrich game outcome prediction models that represent the match situations as a continuous dynamical system [28] or a graphical model [30].  
In summary, the presented machine learning methodology is an important step toward the provision of additional contextual information that cannot easily be accessed through play-by-play data. The presented model to identify defensive strategies can augment such play-by-play data, especially if coupled with further analysis on the outcomes of the plays to provide additional metrics about defensive effectiveness.