Quantifying athlete wellness: Investigating the predictive potential of subjective wellness reports through a player monitoring system

Abstract

This study investigated the potential of self-reported wellness data from a player monitoring system and its predictive power of individual match performance among a female professional football player cohort. Using longitudinal data collected from the Pm Reporter Pro mobile application and corresponding individual performance scores (InStat Index), the study investigated if pre-match perceived wellness could predict individual match performance. The results show no significant evidence for a correlation between the two. This result may suggest that other factors might have a larger impact on performance, that the data quality captured by the current version of the player monitoring system is not sufficient, or that the impact of personally perceived wellness on performance is minimal. The limitations of bias in self-reported data and relatively small sample size might have affected the results. Despite these findings, the study provides valuable insights into the use of data-driven analytics with a concrete and widely used player monitoring system and suggests recommendations for future research.

Precision Sports Science: What Is Next for Data Analytics for Athlete Performance and Well-Being Optimization?

“…The ability of ASRM to predict performance or readiness to perform has been recently explored using different approaches. For instance, Alexandersen et al [97] conducted study on female football and found no significant relationship between wellness-related measures and match performance outcomes, using a linear regression model derived from a performance analysis company. However, when the focus is on predicting the ability to perform in matches or training sessions, wellness variables appear to play a significant role.…”