Visualising the complexity of the athlete monitoring cycle through principal component analysis

Purpose: The purpose of this invited commentary is to discuss the use of principal component analysis (PCA) as a dimension reduction and visualisation tool to assist in decision making and communication when analysing complex multivariate data sets associated with the training of athletes. Conclusions: Using PCA it is possible to transform a data matrix into a set of orthogonal composite variables called principal components (PC), with each PC being a linear weighted combination of the observed variables and with all PCs uncorrelated to each other. The benefit of transforming the data using PCA is that the first few PCs generally capture the majority of the information (i.e. variance) contained in the observed data, with the first PC accounting for the highest amount of variance and each subsequent PC capturing less of the total information. Consequently, through PCA it is possible to visualise complex data sets, containing multiple variables on simple 2D scatterplots without any great loss of information, thereby making it much easier to convey complex information to coaches. In the future, athlete monitoring companies should integrate PCA into their client packages to better support practitioners trying to overcome the challenges associated with multivariate data analysis and interpretation. In the interim, we present here an overview of PCA and associated R code to assist practitioners working within the field to integrate PCA into their athlete monitoring process.