Artificial Intelligence, Optimization, and Data Sciences in Sports

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.