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International Volleyball is an indoor volleyball video game developed for P=
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Abstract:In this study, we investigated the relationships between training =
load, perceived wellness and match performance in professional volleyball b=
y applying the machine learning techniques XGBoost, random forest regressio=
n and subgroup discovery. Physical load data were obtained by manually logg=
ing all physical activities and using wearable sensors. Daily wellness of p=
layers was monitored using questionnaires. Match performance was derived fr=
om annotated actions by a video scout during matches. We identified conditi=
ons 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 at=
tack performance is linked to large variations in weights of full-body stre=
ngth training exercises, excessively heavy upper-body strength training, lo=
w 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 e=
xcessive 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 perfor=
mance if training schedules are adapted to the position of a player.Keyword=
s: performance optimization; training load monitoring; volleyball; machine =
learning
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