Longitudinal Monitoring of Athletes: Statistical Issues and Best Practices

Abstract

Athlete monitoring utilizing strength and conditioning as well as other sport performance data is increasing in practice and in research. While the usage of this data for purposes of creating more informed training programs and producing potential performance prediction models may be promising, there are some statistical considerations that should be addressed by those who hope to use this data. The purpose of this review is to discuss many of the statistical issues faced by practitioners as well as provide best practices recommendations. Single-subject designs (SSD) appear to be more appropriate for monitoring and statistically evaluating athletic performance than traditional group statistical methods. This paper discusses several SSD options available that produce measures of both statistical and practical significance. Additionally, this paper discusses issues related to heteroscedasticity, reliability, validity and provides recommendations for each. Finally, if data are incorporated into the decision-making process, it should be returned and utilized quickly. Data visualizations are often incorporated into this process and this review discusses issues and recommendations related to their clarity, simplicity, and distortion. Awareness of these issues and utilization of some best practice methods will likely result in an enhanced and more efficient decision-making process with more informed athlete development programs.