Variational Autoencoders for Baseball Player Evaluation.

In the sporting world, baseball has been quicker to embrace the use of data analytics than any other sport, as detailed baseball statistics have become readily available in large and diverse quantities to the general public. Professional baseball teams use this data to develop game plans and evaluate players. In this work, we explore the latter by using a Variational Autoencoder (VAE), a special class of artificial neural networks. Specifically, we wish to relate a player's season-long batting statistics with the latent skills that a professional athlete needs to succeed in the MLB. In the growing field of sports analytics, we find this work incredibly important as it provides a novel, flexible, and powerful method to predict specific athletic skills based on years of recorded statistics.