Predicting Field-Sport Distances Without Global Positioning Systems in Indoor Play: A Comparative Study of Machine-Learning Techniques.

Journal: International journal of sports physiology and performance
Published Date: Apr 29, 2025

Abstract

PURPOSE: Accurately predicting the distance covered by athletes during indoor sport activities without the use of GPS (global positioning systems) presents a significant challenge. This study evaluates the effectiveness of various machine-learning techniques in predicting total distance, sprinting distance, and running distance for athletes in men's and women's soccer and lacrosse at the University of Notre Dame.

Authors

  • Casey J Metoyer
    Sports Performance, University of Notre Dame, Notre Dame, IN, USA.
  • Jonathon R Lever
    Sports Performance, University of Notre Dame, Notre Dame, IN, USA.
  • Alan Huebner
    Sports Performance, University of Notre Dame, Notre Dame, IN, USA.
  • Holland A Bill
    Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
  • Gabriel Tauro
    Data Science Program, University of Notre Dame, Notre Dame, IN, USA.
  • Michael Labbe
    Military and Veteran Affairs, University of Notre Dame, Notre Dame, IN, USA.
  • David M Smiley
    Technology and Digital Studies Program, University of Notre Dame, Notre Dame, IN, USA.
  • Braden Kay
    Data Science Program, University of Notre Dame, Notre Dame, IN, USA.
  • William Sovine
    Data Science Program, University of Notre Dame, Notre Dame, IN, USA.
  • Jonathan D Hauenstein
    Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
  • John P Wagle
    Sports Performance, University of Notre Dame, Notre Dame, IN, USA.

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