Conference proceeding
Variational Autoencoders for Baseball Player Evaluation
FUZZY SYSTEMS AND DATA MINING V (FSDM 2019), Vol.320, pp.305-311
Frontiers in Artificial Intelligence and Applications
01/01/2019
DOI: 10.3233/FAIA190194
Abstract
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.
Details
- Title: Subtitle
- Variational Autoencoders for Baseball Player Evaluation
- Creators
- Geoffrey Converse - Univ Iowa, Iowa City, IA 52242 USABrooke Arnold - Univ Iowa, Iowa City, IA 52242 USAMariana Curi - Universidade de São PauloSuely Oliveira - Univ Iowa, Iowa City, IA 52242 USA
- Contributors
- Antonio J Tallón-Ballesteros (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- FUZZY SYSTEMS AND DATA MINING V (FSDM 2019), Vol.320, pp.305-311
- Series
- Frontiers in Artificial Intelligence and Applications
- DOI
- 10.3233/FAIA190194
- ISSN
- 0922-6389
- eISSN
- 1879-8314
- Publisher
- IOS Press
- Number of pages
- 7
- Language
- English
- Date published
- 01/01/2019
- Academic Unit
- Computer Science; Mathematics
- Record Identifier
- 9984410848702771
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