Classification of Collegiate Athletes Based on Their Injury History

Abstract

Sports-related injuries can have a significant impact on an athlete’s performance and career. While some injuries are inevitable, many can be prevented. Cluster analysis is a useful statistical technique that can assign individuals into groups (i.e., latent subgroups) based on common characteristics. PURPOSE: To utilize cluster analysis to 1) identify the latent subgroups based on athletes’ injury history; and 2) examine the characteristics of latent subgroups among athletes. METHODS: A total of 1,538 college athletes competing in the South Eastern Conference in NCAA division I were segmented by three criteria; 1) Injury parts indicate the body part sustaining the injury, 2) Injury types describe the detail of their injury status such as strain, contusion or tendonitis. 3) Injury duration refers to how long the athlete was unable to participate in training. K-means clustering analysis with the Euclidean similarity of injury log vectors was conducted to label players. The number of groups(k) was determined by applying the average silhouette method. The characteristics of clusters were analyzed descriptively, and the sports were allocated to each group followed by the athlete clusters. RESULTS: Five clusters were identified by the maximum average silhouette coefficient (0.153) among coefficients for randomly drawn k’s between 2 to 20. The first group, mainly baseball, men’s basketball, and men’s tennis, had injury to their ankle, arm, and hamstring for contusion and strain for a few weeks. The second group was mostly from football, with injury to their ankle, knee, and shoulder with the most extended injury durations. The third group, mostly football or track and field, were the athletes likely to have knee inflammation, and the duration was nearly half of a year. The injured body parts of the fourth group were back, finger, and hamstring, and the types of injuries were fracture and tendonitis. This cluster was mainly women’s basketball and track and field athletes. The members of the last group had head injury (e.g., concussion), and were soccer, softball or volleyball athletes. CONCLUSION: This study may help practitioners in recognizing the likelihood of an athletes’ injury according to their sport. Additionally, coaches could also consider this information in daily practices.