ArticleSensors (Basel, Switzerland)2022
Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use, Validity and Reliability of Inertial Movement Units in Volleyball: Systematic Review of the Scientific Literature.Sensors (Basel, Switzerland) · 2023Pooled it
- Smaller Formats of Volleyball Lead to Greater Improvements in Lower Limb Strength and Power, As Well As Reductions in Landing Forces: A Randomized Controlled Study in Girls.Journal of sports science & medicine · 2024Trial
- The Association Between Muscle Strength, Thigh Circumference, and Serving Efficiency in Elite Male Volleyball Players: A Prospective Observational Longitudinal Study.Journal of functional morphology and kinesiology · 2026Article
- Athlete or Season? Gut Microbiota Variance in Elite Volleyball Players: A Compositional Performance-Based Reanalysis.Sports (Basel, Switzerland) · 2026Article
- Recent advances in the application of artificial intelligence and wearable devices in volleyball.Frontiers in sports and active living · 2026Review
- Construction and validation of the indicator system for factors influencing pre-match tactical decision-making of volleyball coaches.Frontiers in psychology · 2026Article
- Classifying Soccer Players Based on Physical Capacities and Match-Specific Running Performance Using Machine Learning.Journal of sports science & medicine · 2025Article
- Construction of the prediction model and analysis of key winning factors in world women's volleyball using gradient boosting decision tree.Scientific reports · 2025Article
- Machine Learning in Rugby Union: Predicting and Identifying Key Performance Indicators for Professional Rugby Union Players in Match Play Based Workload.European journal of sport science · 2025Article
- Physical function evaluation in volleyball training based on intelligent GRNN.Scientific reports · 2025Article
- Personalized physical activity recommendations for people with axial spondyloarthritis using wearable activity tracker data: an exploratory study.Rheumatology international · 2025Article
- Multidimensional analysis of serving speed in volleyball players by position, sets, and league types: interactions and statistical differences.BMC sports science, medicine & rehabilitation · 2024Article
- The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type.Sensors (Basel, Switzerland) · 2024Article
- Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review.Frontiers in sports and active living · 2024Article
- Development of an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players.BMJ open sport & exercise medicine · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.