Quantifying and Comparing Training Load Metrics in Cycling: A Methodology Review

Accurate monitoring of training load is vital for optimizing performance and reducing the risk of maladaptation, injury, or illness in cyclists. Training load quantification also informs nutrition planning, as daily energy and macronutrient requirements are often adjusted based on exercise demands. Although a range of metrics is available, from power output-based measures to heart rate and perceived exertion indicators, students and early-career practitioners often encounter difficulties in selecting, calculating, and interpreting these indices. This methodology review outlines how to compute three commonly used training load metrics in cycling: training stress score, Edwards' training impulse, and session rating of perceived exertion. We provide step-by-step guidance on evaluating and predicting these metrics using practical, well-illustrated examples drawn from the data of a competitive cyclist. The methodology review includes the visualization and interpretation of relationships between metrics via scatterplots and regression analyses, both with and without intercepts, and in linear and curvilinear forms. We also introduce the use of partial correlation to examine associations between metrics while controlling for exercise duration thereby addressing a frequent confounding factor. A worked example illustrates how data can be used to derive session-specific carbohydrate and energy targets. By following the procedures outlined here, undergraduate and postgraduate students as well as early-career practitioners can enhance their understanding of training load monitoring and develop tailored strategies suited to individual cyclists and contextual constraints including training and nutrition periodization decisions. The included spreadsheet instructions and supplementary R script support replication and facilitate the integration of these methods into applied practice.