The use of principal component analysis for reduction of training load data in professional soccer

The aim of this study was to explore the use of principal component analysis (PCA) in understanding multivariate relationships in soccer training load data. Training load data were collected from 20 professional male soccer players during a 28-week in-season period. Twelve training load variables (total distance, PlayerLoad^TM, low-speed running distance, moderate-speed running distance, high-speed running distance, sprint distance, moderate-speed running efforts, high-speed running efforts, sprint efforts, accelerations, decelerations, and changes of direction) were collected during training sessions, with correlation analysis revealing high intercorrelation between most variables (r = 0.04-0.98). Principal component analysis was performed on datasets containing all players and on individual players. On the whole dataset, two principal components were retained explaining a total of 81% of data variance. The first component comprised variables associated with distances in speed zones and the second component changes of direction. Whilst some individual variation existed among players, distances in speed zones were loaded on the first component and inertial movement analysis variables, such as accelerations, decelerations, and changes of direction, were loaded on the second component. These findings evidence the strong relationships between several common training load variables and highlight the risk of data redundancy. By selecting variables from each component, practitioners can reduce the number of variables reported whilst retaining as much of the variation in data as possible.