Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics
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 p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/20/7996 |
| _version_ | 1827647950138900480 |
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| author | Arie-Willem de Leeuw Rick van Baar Arno Knobbe Stephan van der Zwaard |
| author_facet | Arie-Willem de Leeuw Rick van Baar Arno Knobbe Stephan van der Zwaard |
| author_sort | Arie-Willem de Leeuw |
| collection | DOAJ |
| description | 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 (<i>p</i> < 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. |
| first_indexed | 2024-03-09T19:29:36Z |
| format | Article |
| id | doaj.art-40b03fb6fbc94649898a4cec40811c33 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:29:36Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-40b03fb6fbc94649898a4cec40811c332023-11-24T02:30:02ZengMDPI AGSensors1424-82202022-10-012220799610.3390/s22207996Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training CharacteristicsArie-Willem de Leeuw0Rick van Baar1Arno Knobbe2Stephan van der Zwaard3Department of Computer Science, University of Antwerp—IMEC, 2000 Antwerp, BelgiumThe Dutch Volleyball Federation (Nevobo), 3528 BE Utrecht, The NetherlandsLeiden Institute of Advanced Computer Science (LIACS), Leiden University, 2333 CA Leiden, The NetherlandsLeiden Institute of Advanced Computer Science (LIACS), Leiden University, 2333 CA Leiden, The NetherlandsIn 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 (<i>p</i> < 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.https://www.mdpi.com/1424-8220/22/20/7996performance optimizationtraining load monitoringvolleyballmachine learning |
| spellingShingle | Arie-Willem de Leeuw Rick van Baar Arno Knobbe Stephan van der Zwaard Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics Sensors performance optimization training load monitoring volleyball machine learning |
| title | Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics |
| title_full | Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics |
| title_fullStr | Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics |
| title_full_unstemmed | Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics |
| title_short | Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics |
| title_sort | modeling match performance in elite volleyball players importance of jump load and strength training characteristics |
| topic | performance optimization training load monitoring volleyball machine learning |
| url | https://www.mdpi.com/1424-8220/22/20/7996 |
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