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This paper presents a novel framework for predicting shot location and typein tennis. Inspired by recent neuroscience discoveries we incorporate neuralmemory modules to model the episodic and semantic memory components of a tennisplayer. We propose a Semi Supervised Generative Adversarial Networkarchitecture that couples these memory models with the automatic featurelearning power of deep neural networks and demonstrate methodologies forlearning player level behavioural patterns with the proposed framework. Weevaluate the effectiveness of the proposed model on tennis tracking data fromthe 2012 Australian Tennis open and exhibit applications of the proposed methodin discovering how players adapt their style depending on the match context.

Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis