In functional principal component analysis (fPCA) a threshold is chosen to define the number of retained principal components, which corresponds to the amount of preserved information. A variety of thresholds have been used in previous studies and the chosen threshold is often not evaluated. The aim of this study is to identify the optimal threshold that preserves the information needed to describe a dependent variable accurately. To find an optimal threshold, a neural network was used to predict jump height from vertical ground reaction force curve measures generated by a fPCA at different thresholds. The findings indicate that a threshold from 99% to 99.9% (6-11principal components) is optimal for describing jump height, as these thresholds generated significantly lower jump height prediction errors than other thresholds.