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Using Random Forest Regression to Determine Influential Force-Time Metrics for Countermovement Jump Height: A
Justin J Merrigan1, Jason D Stone1,2, John P Wagle3
1Human Performance Innovation Center, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, West Virginia.
Journal of Strength and Conditioning Research
|December 23, 2021
Summary
Random forest regression identified key force-time metrics influencing countermovement jump (CMJ) height. Deeper, faster, and more forceful countermovements, alongside relative power, are crucial for maximizing jump performance in athletes.
Area of Science:
- Sports Science
- Biomechanics
- Performance Analysis
Background:
- Countermovement jump (CMJ) height is a critical performance indicator in many sports.
- Identifying influential force-time metrics is essential for optimizing training and performance.
- Previous analyses often involve numerous, potentially collinear, force-time variables.
Purpose of the Study:
- To determine the most influential force-time metrics on countermovement jump (CMJ) height.
- To compare the efficacy of different regression models in identifying these key metrics.
- To provide insights for practitioners to enhance jump performance through targeted training.
Main Methods:
- Eighty-two NCAA Division I American football players performed maximal-effort CMJs on force plates.
- Force-time data were analyzed using best subsets regression and random forest regression (RFR).
- Absolute and relative force-time metrics were used as predictor variables for jump height.
Main Results:
- Random forest regression models identified 8 key metrics, explaining approximately 93% of jump height variance.
- Higher CMJs were associated with a deeper, faster, and more forceful countermovement with lower eccentric-to-concentric force ratios.
- Relative mean and peak concentric power were the most influential metrics when scaled to body mass.
Conclusions:
- Force-time metrics, particularly those related to power output and countermovement execution, significantly influence CMJ height.
- Random forest regression and best subsets regression are effective tools for identifying key performance metrics.
- Training programs should focus on developing power capabilities and improving countermovement technique to maximize jump performance.
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