Using the scores from the above explorative factor analysis as predictors in a multiple regression analysis offers two main advantages over the more common practice of implementing all directly measured parameters in the multiple regression analysis: On the one hand, when estimating the coefficients of the regression model, it avoids potential problems that can result from multicollinearity among the predictor variables . In the present study, multicollinearity was clearly evident among the force-application parameters. This outcome is understandable since many of these parameters (e.g. for different force components) cannot be considered independent of each other, as they are the product of the same biomechanical action. On the other hand, absolute values of the estimated coefficients in the regression model can be directly compared to evaluate their importance. This is because the original parameters were standardized to have a mean of 0 and a standard deviation of 1 before being used in the factor-analysis calculations. As a result, the importance of each latent factor for the prediction of start performance can be directly derived from the coefficients of the regression model.