University Of Tasmania
Bartlett 2016 - Relationship between internal and external training load in team sport athletes evidence for an individualised approach.pdf (689.78 kB)

Relationships between internal and external training load in team sport athletes: evidence for an individualised approach

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journal contribution
posted on 2023-05-20, 03:06 authored by Bartlett, JD, O'Connor, F, Nathan PitchfordNathan Pitchford, Torres-Ronda, L

Purpose: The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.

Methods: TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.

Results: Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.

Conclusions: This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.


Publication title

International journal of sports physiology and performance








School of Health Sciences


Human Kinetics

Place of publication

United States

Rights statement

Copyright 2016 Human Kinetics, Inc.

Repository Status

  • Open

Socio-economic Objectives

Expanding knowledge in the health sciences