Artificial Intelligence, Optimization, and Data Sciences in Sports

Maude Josée Blondin, Jr. Fister Iztok, Panos M. Pardalos (Herausgeber)

Buch | Hardcover

X, 340 Seiten

2025
Springer International Publishing (Verlag)
978-3-031-76046-4 (ISBN)

Noch nicht erschienen - erscheint am 20.02.2025
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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.

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 the B.Sc. and M.Sc. degrees in computer science from the

University of Maribor in 2011 and 2013, respectively, where he is currently

pursuing a Ph.D. degree. He has participated in the development of more than 100

scientific papers. His interests include evolutionary computation, sports data

mining, and mobile and pervasive computing. Furthermore, he is a member of the

Editorial Board of several international journals. He has acted as a program

committee in more than 25 international conferences.

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.

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.

Erscheint lt. Verlag	20.2.2025
Reihe/Serie	Springer Optimization and Its Applications
Zusatzinfo	X, 340 p. 60 illus., 50 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
Schlagworte	computational intelligence sports • digital twins sports • machine learning sport • Sports analytics • theory sports training
ISBN-10	3-031-76046-8 / 3031760468
ISBN-13	978-3-031-76046-4 / 9783031760464
Zustand	Neuware