The aim of our study is to investigate whether coupling power output (PO) and heart rate (HR) data of semi-professional road cyclists collected in the field is helpful for optimising the training process.

To extract, prioritize, and model the highly conserved variations in mean-maximal power (MMP) data in cyclists utilizing functional principal component (FPC) analysis.

Models for human running performances of various complexities and underlying principles have been proposed, often combining data from world record performances and bio-energetic facts of human physiology. The purpose of this work is to develop a novel, minimal and universal model for human running performance that employs a relative metabolic power scale.

As an alternative to the Wbal models, a methodology based on maximum mean power profiling (MMP) is presented here to predict intermittent exercise performance potential.

Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling.