Using Convolutional Neural Networks to Forecast Sporting Event Results

Mu Yen Chen, Ting Hsuan Chen, Shu Hong Lin

Research output: Chapter in Book/Report/Conference proceedingChapter


Sporting events like the FIFA World Cup and the World Baseball Classic have increased in popularity, and the enthusiasm with which these competitions are reported and commented on is evidence of their wide-reaching influence. These games are popular discussion topics. Many who follow sports are only casual fans, but even these “temporary” fans have the same expectations that all fans do: The teams they support should be able to win. This study selects the National Basketball Association (NBA) to represent competitive ball sports. Predictions herein are based on the examination and statistical analysis of previous records of NBA games. This research integrates the field of sports outcome predictions with the methodology of deep learning via convolutional neural networks. Training and predictions are modelled on statistics gleaned from a total of 4,235 games over the past three years. We analyze the training results of a variety of model structures. While previous studies have applied convolutional neural networks to image or object recognition, our study proposes a specific encoding method that is integrated with deep learning in order to predict the results of future games. The prediction accuracy rate of the model herein is approximately 91%, while the deviation is approximately 0.2. These strong results confirm the validity of our designated encoding method.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages17
Publication statusPublished - 2020

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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