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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F2</i> 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.
Highlights
S PORTS data analytics has been gaining significant attention through collegiate and professional leagues and Esports
Due to the inherently chaotic nature of sports, coaches and scientists are constantly searching for insights related to an athlete’s game performance and injury potential
This study attempted to quantify both; the game performance aspects associated with basketball while simultaneously understanding potential injury risks
Summary
S PORTS data analytics has been gaining significant attention through collegiate and professional leagues and Esports. Any insights regarding athletic performance can make a difference in games in terms of athletic performance or preventing injuries, determining the overall success of a team. This transdisciplinary research has brought athletes, athletic trainers, exercise scientists, engineers, and data scientists together to investigate a Division-1 basketball team’s season where COVID-19 caused unprecedented disruptions. Using daily sleep patterns of the athletes combined with subjective training and survey data, this project positioned itself in a way that game performance and injuries could be predicted with machine learning (ML) methods. A. ATHLETIC PERFORMANCE Sleep and recovery strongly influence physical qualities such as strength, anaerobic power, flexibility, and physical performance [1]. Sleep extension beyond 8 hours per day for
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
