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


Regression, monitoring, jump loads, wearable data


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.

Abstract 557 | PDF Downloads 456 PodScholars Podcast Downloads 0


1. Polgaze T, Dawson B. The physiological requirements of the positions in state league volleyball. Sports Coach. 1992(15):32-37.
2. Sheppard JM, Gabbett TJ, Stanganelli LC. An analysis of playing positions in elite men's volleyball: considerations for competition demands and physiologic characteristics. J Strength Cond Res. 2009;23(6):1858-1866.
3. Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44 Suppl 2:S139-147.
4. Wundersitz DW, Josman C, Gupta R, Netto KJ, Gastin PB, Robertson S. Classification of team sport activities using a single wearable tracking device. J Biomech. 2015;48(15):3975-3981.
5. Charlton PC, Kenneally-Dabrowski C, Sheppard J, Spratford W. A simple method for quantifying jump loads in volleyball athletes. J Sci Med Sport. 2016.
6. Gabbett TJ. The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. J Strength Cond Res. 2010;24(10):2593-2603.
7. Claudino JG, Cronin J, Mezencio B, et al. The countermovement jump to monitor neuromuscular status: A meta-analysis. J Sci Med Sport. 2016.
8. Kennedy RA, Drake D. The effect of acute fatigue on countermovement jump performance in rugby union players during preseason. J Sports Med Phys Fitness. 2017.
9. Sayers SP , Harackiewicz DV , Harman EA, Frykman PN, Rosenstein MT. Cross-validation of three jump power equations. Med Sci Sports Exerc. 1999;31(4):572-577.
10. Vincent W, Weir J. Statistics in Kinesiology, 4th Edition. 2012.
11. Girard O, Lattier G, Maffiuletti NA, Micallef JP, Millet GP. Neuromuscular fatigue during a prolonged intermittent exercise: Application to tennis. J Electromyogr Kinesiol. 2008;18(6):1038-1046.
12. Girard O, Millet GP. Neuromuscular fatigue in racquet sports. Phys Med Rehabil Clin N Am. 2009;20(1):161-173, ix.

Most read articles by the same author(s)