Machine Learning in Elite Volleyball: Integrating Performance Analysis, Competition and Training Strategies (SpringerBriefs in Applied Sciences and Technology)

✍ Scribed by Rabiu Muazu Musa, Anwar P. P. Abdul Majeed, Muhammad Zuhaili Suhaimi, Mohd Azraai Mohd Razman, Mohamad Razali Abdullah, Noor Azuan Abu Osman

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 58
• Category: Library

✦ Synopsis

This brief highlights the use of various Machine Learning (ML) algorithms to evaluate training and competitional strategies in Volleyball, as well as to identify high-performance players in the sport. Several psychological elements/strategies coupled with human performance parameters are discussed in view to ascertain their impact on performance in elite Volleyball competitions. It presents key performance indicators as well as human performance parameters that can be used in future evaluation of team performance and players. The details outlined in this brief are vital to coaches, club managers, talent identification experts, performance analysts as well as other important stakeholders in the evaluation of performance and to foster improvement in this sport.

✦ Table of Contents

Acknowledgements
Contents
1 Nature of Volleyball Sport, Performance Analysis in Volleyball, and the Recent Advances of Machine Learning Application in Sports
Abstract
1.1 An Overview of Volleyball Sport
1.2 The Nature, Attributes, and Skill Requirements of the Volleyball Sport
1.3 Recent Advances in Univariate and Machine Learning Application in Sports
1.4 Mann‒Whiney U-Test Analysis
1.5 Features Extraction Analysis via Information Gain
1.6 Cluster Analysis
1.6.1 Hierarchical Agglomerative Cluster Analysis (HACA)
1.6.2 Louvain Clustering
1.7 Machine Learning Models
1.8 Participants of the Study
1.9 Performance Analysis
References
2 The Effect of Competition Strategies in Influencing Volleyball Performance
Abstract
2.1 Overview
2.2 Clustering
2.3 Classification
2.4 Results and Discussion
2.5 Summary
References
3 Identification of Psychological Training Strategies Essential for Volleyball Performance
Abstract
3.1 Overview
3.2 Feature Selection
3.3 Machine Learning-Based Regression Model
3.4 Results and Discussion
3.5 Summary
References
4 The Strategic Competitional Elements Contributing to Volleyball Performance
Abstract
4.1 Overview
4.2 Feature Selection
4.3 Machine Learning-Based Regression Analysis
4.4 Results and Discussion
4.5 Summary
References
5 Anthropometric Variables in the Identification of High-performance Volleyball Players
Abstract
5.1 Overview
5.2 Clustering
5.3 Classification
5.4 Results and Discussion
5.5 Summary
References
6 Performance Indicators Predicting Medallists and Non-medallists in Elite Men Volleyball Competition
Abstract
6.1 Overview
6.2 Performance Indicators Development
6.3 Classification
6.4 Results and Discussion
6.5 Summary
References
7 Summary, Conclusion, and Future Direction
Abstract
7.1 Summary
7.2 Conclusion
7.3 Future Direction