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Updated: Apr 27, 2026

Importance of Jumping Ability in Handball Throwing Speed and Accuracy
Published on: April 4, 2025
The variance needed to accurately describe jump height from vertical ground reaction force data
Chris Richter1, Kevin McGuinness, Noel E O'Connor
1Applied Sports Performance Research in the School of Health and Human Performance, CLARITY: Centre for Sensor Web Technologies, and INSIGHT: Centre for Data Analytics at Dublin City University, Dublin, Ireland.
Choosing the right threshold in functional principal component analysis (fPCA) is crucial for accurately predicting jump height from vertical ground reaction force (vGRF) data. An optimal threshold range of 99%-99.9% was identified for preserving essential information.
Area of Science:
- Biomechanics
- Functional Data Analysis
- Sports Science
Background:
- Functional principal component analysis (fPCA) is used to reduce dimensionality in functional data, like vertical ground reaction force (vGRF) curves.
- The selection of a threshold to determine the number of retained principal components in fPCA can significantly impact subsequent analyses.
- Previous studies have used various thresholds without consistent evaluation, leading to potential variability in results.
Purpose of the Study:
- To determine the optimal fPCA threshold for preserving information critical to accurately predicting jump height from vGRF curves.
- To evaluate the impact of different fPCA thresholds on the accuracy of jump height prediction models.
- To provide evidence-based recommendations for threshold selection in biomechanical analyses of jumping.
Main Methods:
- Vertical ground reaction force (vGRF) data from jump movements were analyzed using functional principal component analysis (fPCA).
- A neural network model was employed to predict jump height using features derived from vGRF curves at various fPCA thresholds.
- Prediction errors for jump height were systematically compared across different thresholds to identify the optimal range.
Main Results:
- The optimal fPCA threshold range for predicting jump height was found to be between 99% and 99.9% of preserved information.
- This optimal range corresponds to retaining 6 to 11 principal components.
- Thresholds within this range resulted in significantly lower jump height prediction errors compared to other tested thresholds.
Conclusions:
- An fPCA threshold between 99% and 99.9% is recommended for analyses aiming to predict jump height from vGRF data.
- Proper threshold selection in fPCA is essential for maximizing the predictive power of biomechanical variables.
- This study provides a data-driven approach to optimize functional data analysis in sports biomechanics.
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