Related Experiment Video
Updated: May 23, 2025
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
Published on: May 26, 2020
Predicting Field-Sport Distances Without Global Positioning Systems in Indoor Play: A Comparative Study of
Casey J Metoyer1, Jonathon R Lever1, Alan Huebner1,2
1Sports Performance, University of Notre Dame, Notre Dame, IN, USA.
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.
Methods:
The techniques assessed include XGBoost Regressor, ElasticNet Regression, Ridge Regression, and Lasso Regression. Key performance metrics such as root-mean-square error, SDs, means, and 95% CIs are analyzed to provide insights into the relative performance of each method.
Results:
XGBoost provided the lowest root-mean-square error for total distance (97.962 [12.973]), sprint distance (91.616 [4.234]), and running distance (137.103 [2.789]).
Conclusion:
The results demonstrate varying levels of accuracy and precision across different sports, genders, and contexts (game vs practice), highlighting the importance of selecting the appropriate model for specific applications in optimizing team performance, injury prevention, and player conditioning.
Related Concept Videos
01:28Field Application of Global Positioning System
01:23Motion of a Projectile
Horizontal motion, governed by the initial kick, maintains a constant velocity throughout the flight of the soccer ball.
01:05Estimation of the Physical Quantities