Proposal of a Global Training Load Measure Predicting Match Performance in an Elite Team Sport

Front Physiol. 2017 Nov 21:8:930. doi: 10.3389/fphys.2017.00930. eCollection 2017.

Abstract

Aim: The use of external and internal load is an important aspect of monitoring systems in team sport. The aim of this study was to validate a novel measure of training load by quantifying the training-performance relationship of elite Australian footballers. Methods: The primary training measure of each of 36 players was weekly load derived from a weighted combination of Global Positioning System (GPS) data and perceived wellness over a 24-week season. Smoothed loads representing an exponentially weighted rolling average were derived with decay time constants of 1.5, 2, 3, and 4 weeks. Differential loads representing rate of change in load were generated in similar fashion. Other derived measures of training included monotony, strain and acute:chronic ratio. Performance was a proprietary score derived from match performance indicators. Effects of a 1 SD within-player change below and above the mean of each training measure were quantified with a quadratic mixed model for each position (defenders, forwards, midfielders, and rucks). Effects were interpreted using standardization and magnitude-based inferences. Results: Performance was generally highest near the mean or ~1 SD below the mean of each training measure, and 1 SD increases in the following measures produced small impairments: weekly load (defenders, forwards, and midfielders); 1.5-week smoothed load (midfielders); 4-week differential load (defenders, forwards, and midfielders); and acute:chronic ratio (defenders and forwards). Effects of other measures in other positions were either trivial or unclear. Conclusion: The innovative combination of load was sensitive to performance in this elite Australian football cohort. Periods of high acute load and sustained increases in load impaired match performance. Positional differences should be taken into account for individual training prescription.

Keywords: GPS; mixed modeling; monitoring; performance indicators; training load.