Statistical Machine Learning in Health and Sports: A Practical Guide is expected to emerge as a comprehensive resource, combining theoretical foundations with practical applications. The book aims to offer valuable insights for both practitioners and researchers, providing a pragmatic guide to harnessing statistics and machine learning in the realms of health and sports. Anticipated expectations include clear methodologies, real-world examples, and actionable strategies to enhance understanding and application in these dynamic domains.

This book invites contributions from both researchers and practitioners interested in the field of Statistical Machine Learning in Health and Sports. Chapters should be self-contained and fall into one of the following categories:

• Original Works: Presenting original research within the book's scope.
• Experiments: Addressing analytics scenarios relevant to the book.
• Review/Survey: Providing a comprehensive review of recent developments in emerging areas of interest.
• Expanded versions of previously published work in leading data analytics outlets are also encouraged, but they must contain a minimum of 50% new content, clearly identified as such.

• Quantitative Analysis in Medicine and Sports
• Predictive Modeling for Sports Performance
• Medical Data Analytics
• Wearable Technology and Health Analytics
• Occupational Health and Safety Data Analytics
• Personalized Medicine using Machine Learning
• Health Informatics and Predictive Analytics
• Data-driven Training and Coaching Strategies in Sports
• Mental Health Analytics in Sports
• Predictive Analytics for Sports Injury Recovery
• Health Monitoring through IoT Devices in Sports