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

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