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

Considering the fact that present day ball speeds exceed 130mph, the time required by the receiver to make a decision regarding the opponents' intention, and initiate a response could exceed the flight time for the ball [1], [2], [3], [4]. Several studies have shown that this reactive ability is the product of pattern recognition skills that are obtained through a "biological probabilistic engine", that derives theories regardingopponents intentions with the partial information available[1], [5], [6]. For instance, it has been shown that expert tennis players are better at detecting events in advance [1], [7] and posses better knowledge/ expertise of situational probabilities [3]. Further investigation of human neurological structures have revealed that those capabilities occur due to a bottom-up computational process [1] within the human brain, from sensory memory to the experiences stored in episodic memory [8], [9] and knowledge derived in semantic memory [9], [10]. Despite the growing interest among researchers in the machine learning domain in better understanding factors influencing decision making in fastball sports, there have been very few studies transferring the observations of the underlying neural mechanisms to neural modelling in machine learning.Current state-of-the-art methodologies try to capture the underlying semantics through a handful of handcrafted features, without paying attention to essential mechanisms in the human brain, where the expertise and observations are stored and knowledge is derived.