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Related Experiment Videos

Landing models for volleyball players: a longitudinal evaluation

J S Dufek1, S Zhang

  • 1Department of Exercise and Movement Science, University of Oregon, Eugene 97403-1240, USA.

The Journal of Sports Medicine and Physical Fitness
|March 1, 1996
PubMed
Summary

Elite volleyball players show seasonal changes in knee range of motion during landings. Landing impact forces (F1, F2) are predictable by jump mechanics, but individual athlete models are recommended for tailored training.

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Area of Science:

  • Sports Medicine
  • Biomechanics
  • Human Movement Science

Background:

  • Elite athletes undergo rigorous training schedules.
  • Understanding lower extremity biomechanics is crucial for injury prevention and performance optimization in volleyball.
  • Longitudinal studies are needed to track performance changes throughout a competitive season.

Purpose of the Study:

  • To longitudinally evaluate lower extremity landing performance in elite volleyball players.
  • To identify key kinetic and kinematic variables influencing landing impact forces.
  • To explore potential training-related kinematic changes across a competitive season.

Main Methods:

  • Seven NCAA Division 1 female volleyball players participated in three data collection sessions (pre-, post-, off-season).

Related Experiment Videos

  • Block-jumps were analyzed using a dual force platform (1000 Hz) and sagittal view videography (200 Hz).
  • Key variables included first (F1) and second (F2) maximum vertical forces, knee joint range of motion (K(ROM)), and jump height.

    • Main Results:

    • A significant change in knee joint range of motion (K(ROM)) was observed across the season (p < 0.05).
    • Multiple regression models explained 88.1% of variance for F1 and 98.3% for F2.
    • Ankle joint angular velocity predicted F1, while jump phase braking impulse predicted F2.

    Conclusions:

    • Seasonal training and practice lead to kinematic differences in volleyball players' landing mechanics.
    • Individualized prediction models are necessary due to differential results across athletes.
    • Findings have implications for designing sport-specific training and assessment protocols for dynamic landing activities.