  This paper presents a novel framework for predicting shot location and type
in tennis. Inspired by recent neuroscience discoveries we incorporate neural
memory modules to model the episodic and semantic memory components of a tennis
player. We propose a Semi Supervised Generative Adversarial Network
architecture that couples these memory models with the automatic feature
learning power of deep neural networks and demonstrate methodologies for
learning player level behavioural patterns with the proposed framework. We
evaluate the effectiveness of the proposed model on tennis tracking data from
the 2012 Australian Tennis open and exhibit applications of the proposed method
in discovering how players adapt their style depending on the match context.
