Overcoming the problem of multicollinearity in sports performance data: A novel application of partial least squares correlation analysis.

The LOVO PLSCA technique appears to be a useful tool for evaluating the relative importance of predictor variables in data sets that exhibit considerable multicollinearity. When used as a filtering tool, LOVO PLSCA produced a MLR model that demonstrated a significant relationship between 'end fitness' and the predictor variable 'accumulated distance at very-high speed' when 'starting fitness' was included as a covariate. As such, LOVO PLSCA may be a useful tool for sport scientists and coaches seeking to analyse data sets obtained using GPS and MEMS technologies.