This book delves into the dynamic intersection of data science, data mining, machine learning, and optimization within sports. It compiles and presents the latest achievements in this vibrant and emerging research area, offering a comprehensive overview of how these technologies revolutionize sports analytics and performance.

Topical coverage includes artificial intelligence in sports, automated machine learning for training sessions, computational social science, and deep learning applications. Readers will also explore cutting-edge concepts such as digital twins in sports and sports prediction through data analysis. This volume highlights theoretical advancements and practical case studies that demonstrate real-world applications.

Ideal for researchers, practitioners, and students in fields related to sports science, data analytics, and machine learning, this book serves as a crucial resource for anyone looking to understand the transformative impact of technology on sports. Whether you are an academic scholar or a professional working in the industry, this collection offers valuable insights that bridge the gap between research and practical solutions.

Insights into future research directions Synergy of data science, data mining, machine learning, and optimization in sports Practical application of emerging technologies and utility in sports

Autorentext

Maude J. Blondin, Eng, has been an assistant professor at the Electrical and Computer Engineering (ECE) Department at UdeS since Sept. 2020. She is the head of Multiobjective Optimization REsearch lab (MORE Lab) at UdeS, a laboratory that she founded and counts currently 9 students and postdocs. She has strong optimization and control training applied to practical systems with advanced skills in distributed and multiobjective optimization and control algorithms applied to MAS (Ph.D. in Electrical Engineering and Postdoctoral research in Mechanical and Aerospace engineering, University of Florida, Gainesville, Florida, USA). She published a book on the recent progress of optimization in control in 2021 and coedited two contributed books. She is on the editorial board of the SN Operations Research Forum journal. She has published more than 20 peer-reviewed articles in renowned conferences and journals and reviewed more than 55 publications for recognized conferences and high-impact factors journals. She has been invited as a guest speaker on eight national and international occasions. Since the start of her career, i.e., Sept. 2020, she has gathered more than 620k$ as PI/co-PI and more than 500k$ as a co-investigator.

Iztok Fister Jr. received his Master's in bioinformatics from the University of Maribor in 2019, his M.Eng. degree in Computer Science in 2013, and his Ph.D in Computer Science in 2017 from the University of Maribor. He is an associate professor at the Faculty of Electrical Engineering and Computer Science of the University of Maribor, focusing on computational intelligence, data mining, and sports science. 

Panos M. Pardalos serves as professor emeritus of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. He is also an affiliated faculty member of the computer and information science Department, the Hellenic Studies Center, and the biomedical engineering program. He is also the director of the Center for Applied Optimization. Pardalos is a world-leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

Inhalt

Chapter 1. Artificial Intelligence, Optimization, and Data Sciences in Sports: Editorial.- Chapter 2. Machine Learning for Soccer Match Result Prediction.- Chapter 3. Machine learning for prediction of the index of effec-tiveness in cycling.- Chapter 4. Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements.- Chapter 5. Artificial Intelligence & Machine Learning-Based Data Analytics for Sports. General Overview & NBA Case Study.- Chapter 6. An ecological dynamics approach to the use of Artificial Intelligence and Machine Learning to analyse performance in football.- Chapter 7. A Supervised Learning Approach for Evaluating Football Performances.- Chapter 8. Bridging Route based Cycling Training with Digital Twins.- Chapter 9. Perspectives of Artificial Intelligence in Training and Exercise.- Chapter 10. A fuzzy model for optimise the football rule assuring spectacle, fair play, objectivity and ethics.- Chapter 11. Physical Efficiency in Soccer: Relevance, Correlations and Impacts using AI Methods.- Chapter 12. A PageRank-Based Method for College Football Recruiting Rankings.- Chapter 13. APPLICATIONS OF IMPROVEMENTS TO THE PYTHAGOREAN WON-LOST EXPECTATION IN OPTIMIZING ROSTERS.