Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics

Arie Willem de Leeuw*, Rick van Baar, Arno Knobbe, Stephan van der Zwaard

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In this study, we investigated the relationships between training load, perceived wellness and match performance in professional volleyball by applying the machine learning techniques XGBoost, random forest regression and subgroup discovery. Physical load data were obtained by manually logging all physical activities and using wearable sensors. Daily wellness of players was monitored using questionnaires. Match performance was derived from annotated actions by a video scout during matches. We identified conditions of predictor variables that related to attack and pass performance (p < 0.05). Better attack performance is related to heavy weights of lower-body strength training exercises in the preceding four weeks. However, worse attack performance is linked to large variations in weights of full-body strength training exercises, excessively heavy upper-body strength training, low jump heights and small variations in the number of high jumps in the four weeks prior to competition. Lower passing performance was associated with small variations in the number of high jumps in the preceding week and an excessive amount of high jumps performed, on average, in the two weeks prior to competition. Differences in findings with respect to passing and attack performance suggest that elite volleyball players can improve their performance if training schedules are adapted to the position of a player.

Original languageEnglish
Article number7996
Pages (from-to)1-13
Number of pages13
JournalSensors
Volume22
Issue number20
DOIs
Publication statusPublished - 2 Oct 2022

Bibliographical note

Funding Information:
The research leading to these results received funding from Sportinnovator/ZonMw.

Publisher Copyright:
© 2022 by the authors.

Funding

The research leading to these results received funding from Sportinnovator/ZonMw.

FundersFunder number
Sportinnovator/ZonMw

    Keywords

    • machine learning
    • performance optimization
    • training load monitoring
    • volleyball

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