Deep learning approach for forecasting athletes' performance in sports tournaments

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sports and international tournaments have gained world attention in the past decade. Enhancing sports activities and promoting sports to participate in international events competitions and tournaments play a substantial role in the development and advancement of nations around the globe. In this paper we applied different deep learning models for predicting athletes' performance in tournaments to help them improve their results. We propose a deep learning selection algorithm to evaluate the effectiveness of the athletes' current training by predicting their race results upon completing each additional training which potentially improves their performance. We gathered public training data for athletes who participated in the 2017 Boston Marathon within a five-month window prior to the race. Deep learning models were applied and evaluated to predict marathon finishing times. These include Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). Results show that Deep Learning models give improved race time prediction accuracy over the baseline machine learning model such as standard Linear Regression (LR).

Original languageEnglish
Title of host publicationProceedings of 13th International Conference on Intelligent Systems
Subtitle of host publicationTheories and Applications, SITA 2020
PublisherAssociation for Computing Machinery
Pages203-208
Number of pages6
ISBN (Electronic)9781450377331
DOIs
Publication statusPublished - Sep 23 2020
Event13th International Conference on Intelligent Systems: Theories and Applications, SITA 2020 - Virtual, Online, Morocco
Duration: Sep 23 2020Sep 24 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Intelligent Systems: Theories and Applications, SITA 2020
Country/TerritoryMorocco
CityVirtual, Online
Period9/23/209/24/20

Keywords

  • Running
  • deep learning
  • marathon
  • prediction modeling
  • sports training

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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