Analyze the impact of complex scheduling algorithms on injury rates and athletic performance in a collegiate sports environment

Abstract

Efficiently managing sports schedules in collegiate environments is a challenging and crucial task. With multiple teams, events, facilities, and diverse stakeholder needs, traditional scheduling methods often fail to meet the dynamic and complex requirements of modern sports programs. Complex scheduling algorithms offer a promising solution by optimizing the allocation of resources, time slots, and facilities while minimizing conflicts and maximizing participation. The research analyzes the impact of complex scheduling algorithms on injury rates and athletic performance in a collegiate sports environment, with a particular focus on ACL injuries in basketball players. The research gathers the history, performance, and demographic data from athletes. The data was preprocessed using cleansing and normalizing the data, and handling missing values. The research employs an ICO-MLPN to predict injury risk and improve athletic performance in collegiate sports environments. The research explores the application of the DLB scheduling algorithm to create tailored schedules that account for individual requirements, training intensity, and recovery periods to reduce the risk of ACL. The findings suggest that the complex scheduling algorithms improve athletic performance and also significantly reduce the incidence of ACL injuries, offering an ICO-MLPN framework for collegiate basketball programs to improve performance with a recall of 95.33%, accuracy of 98.70%, precision of 98.2%, and F1-score of 96.20%. After implementing DLB, the injury risk incident rate decreased from 50% to 30%, treatment costs decreased from 50% to 20%, physical health satisfaction improved from 65% to 85%, and mental health satisfaction increased from 60% to 80%. Recovery time decreased from 2.5 days to 2 days, and minimum severity injuries increased from 50% to 65%. The approach underscores the potential of combining scheduling optimization and innovative algorithms to create personalized training schedules that prioritize both performance and injury risk reduction.