Document Type

Article

Publication Date

2023

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

IEEE Access

Source ISSN

2169-3536

Original Item ID

DOI: 10.1109/ACCESS.2023.3334911

Abstract

Monitoring workload is essential to evaluate possibility of fatigue or injuries and overall performance of the players. Large player’s workload in any game including basketball can contribute to the stress and fatigue and overloaded players may get exhausted and exhibit burnout symptoms, which eventually result in their lower efficiency in the game. This paper aims at predicting the efficiency of the basketball players in all positions (guard, forward and center) based on their workload information. Machine learning (ML) methods for regression and classification are applied to the dataset for predictive modelling of the variables. The analysis includes: (i) one-model for all player positions and (ii) position-based models for respective player position. Leveraging tabular variational autoencoders (TVAE), synthetic data is generated to improve the accuracy. The evaluated models from both regression and classification verify that better accuracies can be obtained in the position-based models rather than the one-model for all positions approach. The performance analysis of the algorithms indicates that the player’s efficiency can be estimated from the workload information which can provide valuable insight and individualized recommendation for optimal performance during the competition.

Comments

Published version. IEEE Access, Vol. 11 (2023): 130186-130196. DOI. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

Download
Yahyasoltani_16260acc.docx (127 kB)
ADA Accessible Version

Share

COinS