An Accelerometer-Based Training Load Analysis to Assess Volleyball Performance
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Keywords
Regression, monitoring, jump loads, wearable data
Abstract
Introduction: The purpose was to quantify a volleyball athlete’ s accelerometer-based workloads and utilize a neuromuscular fatigue jump test to assess on-court performance throughout a competitive season.
Methods: One, Division I volleyball athlete was monitored throughout each practice and competitive game using a validated wearable microsensor device (Catapult Sports). To assess neuromuscular fatigue, an approach jump (AJ) test was completed weekly. On-court statistics were recorded each game.
Results: Utilizing a forward linear regression model, low intensity decelerations, moderate and high intensity accelerations, and low and high intensity jumps accounted for 91.7% of the variation in weekly relative power assessed via AJ test (p < 0.001). Of those variables, only high intensity jumps were significantly (p = 0.035) different between practices that occurred prior to winning (49.6 ± 26.7) and losing (69.2 ± 39.8) game performances. Additionally, hitting percent was significantly better (.266 ± .190 win; .130 ± .129 loss; p = 0.05) in winning performances.
Conclusions: Alterations in approach jump performance throughout a competitive season is multifaceted; however, limiting high intensity jumps in practice may be advantageous to optimize volleyball performance.
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Copyright, 2023 by the authors. This work is licensed under the Creative Commons Attribution 4.0 International License.