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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

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|December 23, 2021
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Summary
This summary is machine-generated.

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.

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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.