title: Relationships Between Internal and External Training Load in Team-Sport Athletes: Evidence for an Individualized Approach.
abstract: OBJECTIVE
  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.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Bartlett
authors.forename: Fergus
authors.initials: F
authors.lastname: O'Connor
authors.forename: Nathan
authors.initials: N
authors.lastname: Pitchford
authors.forename: Lorena
authors.initials: L
authors.lastname: Torres-Ronda
authors.forename: Samuel J
authors.initials: SJ
authors.lastname: Robertson
mesh_terms: D001315:Australia; D040362:Geographic Information Systems; D006801:Humans; D008297:Male; D010465:Perception; D064797:Physical Conditioning, Human / Q000379:methods*; D005082:Physical Exertion / Q000502:physiology*; D012916:Soccer / Q000502:physiology* / Q000523:psychology*; D055815:Young Adult
keywords: GPS; RPE; elite athletes; monitoring; training prescription