Document Type

Peer-Reviewed Article

Publication Date

2022

Abstract

We investigated the impact of sleep and training load of Division - 1 women’s basketball players on their game performance and injury prediction using machine learning algorithms. The data was collected during a pandemic-condensed season with unpredictable interruptions to the games and athletic training schedules. We collected data from sleep monitoring devices, training data from coaches, injury reports from medical staff, and weekly survey data from athletes for 22 weeks.With proper data imputation, interpretable feature set, data balancing, and classifiers, we showed that we could predict game performance and injuries with more than 90% accuracy. More importantly, our F1 and F2 scores of 0.94 and 0.83 for game performance and injuries, respectively, show that we can use the prediction for informative analysis in the future for coaches to make insightful decisions. Our data analysis also showed that collegiate athletes sleep less than the recommended hours (6-7 instead of 8 hours). This coupled with a long hiatus in games and training increases the risk of injury. Varied training and higher heart rate variability (due to better quality sleep) indicated a better performance, while athletes with poor sleep patterns, were more prone to injuries.

Comments

This work is licensed under a Creative Commons Attribution 4.0 License.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited.

DOI

10.1109/ACCESS.2022.3145368

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.