Purpose: To discuss the use of principal-component analysis (PCA) as a dimension-reduction and visualization tool to assist in decision making and communication when analyzing 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 (PCs), 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 (ie, 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 visualize 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, the authors present here an overview of PCA and associated R code to assist practitioners working in the field to integrate PCA into their athlete-monitoring process.
Weaving, Beggs, Dalton-Barron, and Jones are with the Inst for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, United Kingdom. Weaving and Jones are also with Leeds Rhinos Rugby League Club, Leeds, United Kingdom. Dalton-Barron is also with Catapult Sports, Leeds, United Kingdom. Dalton-Barron and Jones are also with the Rugby Football League, Leeds, United Kingdom. Jones is with Yorkshire Carnegie Rugby Union Club, Leeds, United Kingdom. Abt is with the Dept of Sport, Health and Exercise Science, University of Hull, Hull, United Kingdom.