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Artificial intelligence, optimization, and data sciences in sports

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence, optimization, and data sciences in sportsedited by Maude J. Blondin, Iztok Fister Jr., Panos M. Pardalos.
其他作者:	Blondin, Maude J.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xi, 353 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	SportsData processing.
電子資源:	https://doi.org/10.1007/978-3-031-76047-1
ISBN:	9783031760471$q(electronic bk.)

Artificial intelligence, optimization, and data sciences in sports
Artificial intelligence, optimization, and data sciences in sports[electronic resource] /edited by Maude J. Blondin, Iztok Fister Jr., Panos M. Pardalos. - Cham :Springer Nature Switzerland :2025. - xi, 353 p. :ill. (chiefly color), digital ;24 cm. - Springer optimization and its applications,v. 2181931-6836 ;. - Springer optimization and its applications ;v. 3..

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.

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.

ISBN: 9783031760471$q(electronic bk.)

Standard No.: 10.1007/978-3-031-76047-1doiSubjects--Topical Terms:

709084
Sports
--Data processing.

LC Class. No.: GV706.8

Dewey Class. No.: 796.028563

Artificial intelligence, optimization, and data sciences in sports
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505 0 $a 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.
520 $a 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.
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