TY - JOUR
T1 - Modeling Match Performance in Elite Volleyball Players
T2 - Importance of Jump Load and Strength Training Characteristics
AU - de Leeuw, Arie-Willem
AU - van Baar, Rick
AU - Knobbe, Arno
AU - van der Zwaard, Stephan
N1 - Funding Information: The research leading to these results received funding from Sportinnovator/ZonMw. Publisher Copyright: © 2022 by the authors.
PY - 2022/10/20
Y1 - 2022/10/20
N2 - 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.
AB - 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.
KW - Athletic Performance
KW - Exercise
KW - Humans
KW - Resistance Training
KW - Surveys and Questionnaires
KW - Volleyball
KW - machine learning
KW - performance optimization
KW - training load monitoring
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U2 - https://doi.org/10.3390/s22207996
DO - https://doi.org/10.3390/s22207996
M3 - Article
C2 - 36298347
SN - 1424-8220
VL - 22
SP - 1
EP - 13
JO - Sensors (Basel, Switzerland)
JF - Sensors (Basel, Switzerland)
IS - 20
M1 - 7996
ER -