Call for Chapters: AI and Machine Learning Applications in Sports Analytics

Editors

Tanupriya Choudhury, Symbiosis International (Deemed University), India
Pradeep Arya, Symbiosis International (Deemed University), India
Ketan Kotecha, Symbiosis International (Deemed University), India
Ashutosh Sharma, Henan University of Science and Technology, India
Jung-Sup Um, Kyungpook National University, Korea, Republic Of

Call for Chapters

Proposals Submission Deadline: January 7, 2024
Full Chapters Due: June 30, 2024
Submission Date: June 30, 2024

Introduction

Sports analytics is a rapidly growing field that combines the power of artificial intelligence (AI) and machine learning (ML) algorithms to gain valuable insights from vast amounts of sports data. The book " Game Changers: Unleashing the Power of AI and ML in Sports Analytics" delves into this exciting domain, providing readers with a comprehensive understanding of how AI and ML techniques can be employed to revolutionize decision-making in sports. The book starts by introducing the fundamental concepts of AI and ML, ensuring that even readers without prior knowledge can grasp the underlying principles. It then progresses to explore various applications of these technologies in different areas of sports analytics, including player performance analysis, game strategy optimization, injury prediction, talent scouting, fan engagement, and more. One area where AI and ML have had a significant impact is player performance analysis. Traditionally, coaches relied on subjective observations or basic statistics to evaluate players' skills. However, with advanced algorithms now available through AIML techniques such as deep learning or reinforcement learning models, it becomes possible to analyze large datasets containing detailed information about each player's actions during games. These models can uncover patterns and relationships within the data that human analysts might miss – enabling teams to make data-driven decisions regarding team composition or individual training programs. For instance, consider basketball: an AI model could analyze thousands of hours of video footage from games and instantly identify specific actions like dribbling technique or shooting accuracy for every player on the court. By analyzing these metrics over time along with other contextual factors like fatigue levels or defensive pressure faced by each player during games - coaches can gain deeper insights into their team's strengths and weaknesses while identifying areas for improvement. Moreover, game strategy optimization is another critical aspect covered in this book utilizing AIML approaches. Coaches often face complex decisions during matches – whether it's choosing which plays to run against particular opponents or deciding when substitutions should occur based on real-time game situations. With AI-powered analytics tools at their disposal - coaches can leverage historical data, opponent scouting reports, and even real-time tracking data to evaluate different strategies' success rates. This enables them to make more informed decisions that maximize their team's chances of winning. Additionally, the book explores how AI and ML techniques can help predict injuries by analyzing various factors such as player workload, physical condition, historical injury records, and even environmental conditions. By identifying patterns or risk factors associated with specific types of injuries - teams can take proactive measures like adjusting training regimens or scheduling rest periods for players at higher risk. Furthermore, talent scouting is an area where AIML techniques have transformed the traditional approach. Instead of relying solely on scouts' subjective assessments or limited sample sizes from games attended in person - teams can now leverage machine learning algorithms to analyze vast amounts of player performance data from multiple sources (e.g., video footage from youth leagues). These algorithms can identify key indicators that correlate with future success at higher levels – helping teams discover hidden gems who might otherwise be overlooked. Lastly, fan engagement has become increasingly important in modern sports organizations. With social media platforms generating massive amounts of user-generated content related to sports events – AI-powered sentiment analysis tools allow teams to gauge public opinion in real time regarding game outcomes or individual performances. This information helps organizations tailor marketing campaigns or engage directly with fans through personalized interactions based on their preferences and sentiments expressed online. In conclusion, " Game Changers: Unleashing the Power of AI and ML in Sports Analytics" provides a comprehensive guide for anyone interested in exploring the possibilities offered by artificial intelligence and machine learning within the realm of sports analytics. Through detailed explanations and numerous examples across various sporting disciplines – this book equips readers with the practical knowledge they need to harness these cutting-edge technologies effectively for improved decision-making processes within their respective fields. To provide more in-depth information on how Sports Analytics are achieved by artificial intelligence and machine learning let's explore some key features and examples:

Objective

"Game Changers: Unleashing the Power of AI and ML in Sports Analytics" is an insightful exploration into how artificial intelligence (AI) and machine learning (ML) are transforming sports analytics. The book serves as a primer on AI and ML, making it accessible to novices while delving into their applications across various facets of sports, such as player performance analysis, game strategy optimization, injury prediction, talent scouting, and fan engagement.

Target Audience

Academics and Researchers, AIML Professionals, Technology Enthusiasts, Government and Regulatory Authorities, Industry Practitioners and Graduate and Postgraduate Students. It can be suggested as textbook of following subjects awarded in Deemed Universities Artificial Intelligence in Sports Sports Analytics Book Game Strategy Optimization Technology and Policy Studies Computer Science and Engineering Graduate and Postgraduate Programs Distance Learning and Online Programs

Recommended Topics

Contents: 1. Introduction to Sports Analytics: A brief introduction of the book's content and the implications of AI and machine learning in sports. 2. The AI and ML Revolution: A detailed examination of how new technologies are altering the sports sector. 3. Data Sources and Collection: Investigating the many data sources utilized in sports analytics, which include television footage to sensors worn by athletes. 4. Player Performance Analysis: Dive into AI and ML-powered player performance evaluation, including complex metrics and insights. 5. Game Strategy Optimization: Coaches are utilizing AI and ML to make crucial decisions in real-time that affect the outcomes of games. 6. Injury Prediction and Prevention: AI can be used to predict injuries and take proactive measures to prevent them. It also explore the benefits and potential drawbacks of this approach. 7. Talent Scouting and Recruitment: Machine learning offers a solution to this problem by providing organizations with the tools to identify and evaluate potential candidates more accurately and efficiently. 8. Fan Engagement and Personalization: AI and ML have the potential to revolutionize the sports and entertainment industry by providing businesses with the tools they need to engage with fans in new and meaningful ways 9. Real-Time Analytics and Decision-Making: An investigation into the impact of real-time data on in-game decision-making and strategies 10. Case Studies in Sports Analytics: explore how AI and ML have made significant progress in the field of sports through real-world examples 11. Ethical Considerations in Sports Analytics: Exploring the ethical challenges presented by the use of AI and ML in sports 12. The Future of Sports Analytics: Predictions and trends for the use of artificial intelligence (AI) and machine learning (ML) in the sports industry. 13. Practical Applications of AI and ML: Practical examples of how to apply these technologies in the field of sports 14. Analytics in Different Sports: A chapter showcasing the application of AI and ML in various sports. 15. Building Your Sports Analytics Team: Insight into the necessary skills and expertise for success in sports analytics. Updated Important Dates April 30, 2024: Proposal Submission Deadline May 21, 2024: Notification of Acceptance June 10, 2024: Full Chapter Submission July 10, 2024: Review Results Returned August 15, 2024: Final Acceptance Notification September 5, 2024: Final Chapter Submission

Submission Procedure

Researchers and practitioners are invited to submit on or before January 7, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by January 21, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by June 30, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, AI and Machine Learning Applications in Sports Analytics. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2024.



Important Dates

January 7, 2024: Proposal Submission Deadline
January 21, 2024: Notification of Acceptance
April 26, 2024: Full Chapter Submission
April 28, 2024: Review Results Returned
April 28, 2024: Final Acceptance Notification
June 30, 2024: Final Chapter Submission



Inquiries

Tanupriya Choudhury Graphic Era Deemed to be University tanupriyachoudhury.cse@geu.ac.in Pradeep Arya Symbiosis International (Deemed University) pradeep.arya@sitpune.edu.in Ketan Kotecha Symbiosis International (Deemed University) Ashutosh Sharma Henan University of Science and Technology Jung-Sup Um Kyungpook National University

Classifications


Business and Management; Computer Science and Information Technology; Medicine and Healthcare; Social Sciences and Humanities; Physical Sciences and Engineering
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