An Accelerometer-Based Training Load Analysis to Assess Volleyball Performance

Main Article Content

Gabriel J. Sanders
Brian Boos
Frank Shipley
Cory M. Scheadler
Corey A. Peacock

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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