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

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

doi:10.3390/s22207996

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…In volleyball, some authors used XGBoost, random forest regression, and subgroup discovery to study male national volleyball teams' well-being, RPE, and readiness using questionnaires. The study examines defensive and attacking game phases and shows how machine learning can analyses volleyball performance patterns ( 44 ).…”

Section: Discussionmentioning

confidence: 99%

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Munoz-Macho,

Domínguez-Morales,

Sevillano-Ramos

2024

Front. Sports Act. Living

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IntroductionIn competitive sports, teams are increasingly relying on advanced systems for improved performance and results. This study reviews the literature on the role of artificial intelligence (AI) in managing these complexities and encouraging a system thinking shift. It found various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations. The main goal of the study was to assess research supporting performance and healthcare.MethodsSystematic searches were conducted on databases such as Pubmed, Web of Sciences, and Scopus to find articles using AI to understand or improve sports team performance. Thirty-two studies were selected for review.ResultsThe analysis shows that, of the thirty-two articles reviewed, fifteen focused on performance and seventeen on healthcare. Football (Soccer) was the most researched sport, making up 67% of studies. The revised studies comprised 2,823 professional athletes, with a gender split of 65.36% male and 34.64% female. Identified AI and non-AI methods mainly included Tree-based techniques (36%), Ada/XGBoost (19%), Neural Networks (9%), K-Nearest Neighbours (9%), Classical Regression Techniques (9%), and Support Vector Machines (6%).ConclusionsThis study highlights the increasing use of AI in managing sports-related healthcare and performance complexities. These findings aim to assist researchers, practitioners, and policymakers in developing practical applications and exploring future complex systems dynamics.

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Section: Discussionmentioning

confidence: 99%

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Munoz-Macho,

Domínguez-Morales,

Sevillano-Ramos

2024

Front. Sports Act. Living

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“…de Leeuw et al (2022) found that perceived load and fatigue due to training load negatively affect competition performance in volleyball players. Brazo-Sayavera et al (2017) reported that fatigue increases with increasing number of jumps and decreases block performance in volleyball players.…”

Section: Discussionmentioning

confidence: 99%

Exploring the relationship between total vertical jump load and block performance in elite volleyball players: Position-specific analysis

İşgüzar,

Keskin,

Tokat

et al. 2023

HumanSciences

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Aim: The study aimed to examine the relationship between total load jumps and negative block performance of elite male volleyball players. Method: During the research, 18 elite volleyball athletes were monitored, composed of 7 middle players, 8 spikers, and 3 opposite players. The total vertical jump and block performance of volleyball players were assessed during 23 demo matches completed in routine training sessions. The tracking of vertical jump count was facilitated by employing a vert belt, while the assessment of block performance relied on the application of Data Volley 4 Pro software. The statistical analysis of the data was conducted using the SPSS 22.0 package program for Windows. The Spearman correlation test was applied to assess the relationship between jump load and block performance. Results: A negative, weak, and significant relationship was found between vertical jump load and ineffective block (r=-,206; p<0,05) and a weak but insignificant relationship for block error (r=,100; p>0,05) for middle players. A positive, insignificant relationship was found between vertical jump load and block error (r=,007; p>0,05), and a negative, insignificant, for the ineffective block (r=-,079; p>0,05) for spikers. A positive, insignificant relationship between total jump load and ineffective block (r=,054; p>0,05), and block error (r=,027; p>0,05). Conclusion: Across all players, the findings suggest that the total jump load is not significantly related to either block errors or ineffective blocks. The positive correlations observed between jump load and both block errors and ineffective blocks are not statistically significant for the entire group.

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“…We used the data mining technique Subgroup Discovery, a descriptive, exploratory technique that can handle relatively small datasets, as considered here. With this technique, we can extract easy-interpretable findings that have proven useful in sport-specific settings 13–17…”

Section: Methodsmentioning

confidence: 99%

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

Wagemans

Leeuw

Catteeuw³

et al. 2023

BMJ Open Sport Exerc Med

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ObjectivesThis retrospective cohort study explored an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players.MethodsNeuromuscular data (eccentric hamstring strength, isometric adduction and abduction strength and countermovement jump) of 77 professional male football players were assessed at the start of the season (baseline) and, respectively, at 4, 3, 2 and 1 weeks before the injury. We included 278 cases (92 injuries; 186 healthy) and applied a subgroup discovery algorithm.ResultsMore injuries occurred when between-limb abduction imbalance 3 weeks before injury neared or exceeded baseline values (threshold≥0.97), or adduction muscle strength of the right leg 1 week before injury remained the same or decreased compared with baseline values (threshold≤1.01). Moreover, in 50% of the cases, an injury occurred if abduction strength imbalance before the injury is over 97% of the baseline values and peak landing force in the left leg 4 weeks before the injury is lower than 124% compared with baseline.ConclusionsThis exploratory analysis provides a proof of concept demonstrating that a subgroup discovery algorithm using neuromuscular tests has potential use for injury prevention in football.

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

Cited by 11 publications

References 34 publications

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Exploring the relationship between total vertical jump load and block performance in elite volleyball players: Position-specific analysis

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

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