Machine Learning in Sports

This open access book provides cutting-edge work on machine learning in sports analytics, emphasizing the integration of computer vision, data analytics, and machine learning to redefine strategic sports analysis. This book not only covers the essential methodologies of capturing and analyzing real...

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Hovedforfatter: Fujii, Keisuke
Format: Online
Sprog:engelsk
Udgivet: Springer Nature 2025
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Online adgang:ONIX_20250414_9789819614455_24
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author Fujii, Keisuke
author_browse Fujii, Keisuke
author_facet Fujii, Keisuke
author_sort Fujii, Keisuke
collection Directory of Open Access Books
description This open access book provides cutting-edge work on machine learning in sports analytics, emphasizing the integration of computer vision, data analytics, and machine learning to redefine strategic sports analysis. This book not only covers the essential methodologies of capturing and analyzing real sports data but also pioneers the integration of real-world analytics with digital modeling, advancing the field toward sophisticated digital modeling in sports. Through a seamless blend of theoretical frameworks and practical applications, the book illustrates how these integrated technologies can be utilized to predict, evaluate, and suggest next plays in sports. By leveraging the power of machine learning, the book presents cutting-edge approaches to sports analytics, where data from actual games is enhanced with predictive simulations for strategic planning and decision-making. The use of digital modeling in sports opens up new dimensions of interaction between the physical play and its digital analysis, offering a comprehensive understanding that was previously unattainable. This book is an essential read for postgraduates, researchers, and technologists, who are interested in sports analysts. The book consists of five parts: Part I, which comprises a single chapter exploring the fundamentals and scope of learning-based sports analytics; Parts II, III, IV, and V review the various aspects of this field, including data acquisition with computer vision, predictive analysis and play evaluation with machine learning, potential play evaluation with learning-based agent modeling, and future perspectives and ecosystems on the field. This structure provides a comprehensive overview that will engage and inform researchers and practitioners interested in the intersection of analytical research and cutting-edge technology in sports.
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spelling doab-20.500.12854ir-1584192025-07-30T09:00:14Z Machine Learning in Sports Fujii, Keisuke Artificial intelligence Machine learning Deep learning Real-world data Prediction Reinforcement learning Cyber-physical systems Modeling Sports Football Soccer Basketball thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory thema EDItEUR::S Sports and Active outdoor recreation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory thema EDItEUR::S Sports and Active outdoor recreation This open access book provides cutting-edge work on machine learning in sports analytics, emphasizing the integration of computer vision, data analytics, and machine learning to redefine strategic sports analysis. This book not only covers the essential methodologies of capturing and analyzing real sports data but also pioneers the integration of real-world analytics with digital modeling, advancing the field toward sophisticated digital modeling in sports. Through a seamless blend of theoretical frameworks and practical applications, the book illustrates how these integrated technologies can be utilized to predict, evaluate, and suggest next plays in sports. By leveraging the power of machine learning, the book presents cutting-edge approaches to sports analytics, where data from actual games is enhanced with predictive simulations for strategic planning and decision-making. The use of digital modeling in sports opens up new dimensions of interaction between the physical play and its digital analysis, offering a comprehensive understanding that was previously unattainable. This book is an essential read for postgraduates, researchers, and technologists, who are interested in sports analysts. The book consists of five parts: Part I, which comprises a single chapter exploring the fundamentals and scope of learning-based sports analytics; Parts II, III, IV, and V review the various aspects of this field, including data acquisition with computer vision, predictive analysis and play evaluation with machine learning, potential play evaluation with learning-based agent modeling, and future perspectives and ecosystems on the field. This structure provides a comprehensive overview that will engage and inform researchers and practitioners interested in the intersection of analytical research and cutting-edge technology in sports. 2025-04-15T04:10:57Z 2025-04-15T04:10:57Z 2025-04-14T12:57:18Z 2025 book ONIX_20250414_9789819614455_24 https://library.oapen.org/handle/20.500.12657/100769 9789819614448 https://directory.doabooks.org/handle/20.500.12854/158419 eng SpringerBriefs in Computer Science open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/100769/1/9789819614455.pdf Springer Nature Springer Nature Singapore 10.1007/978-981-96-1445-5 10.1007/978-981-96-1445-5 9fa3421d-f917-4153-b9ab-fc337c396b5a 86ee1bcf-6367-42c1-80e1-5f7551d3dc2f 1f0de1ea-9a4f-46d5-a9ca-5bbc3290d8fe 9789819614448 Springer Nature Singapore 127 Singapore [...] Japan Science and Technology Agency 国立研究開発法人科学技術振興機構 10.13039/501100002241 open access
spellingShingle Artificial intelligence
Machine learning
Deep learning
Real-world data
Prediction
Reinforcement learning
Cyber-physical systems
Modeling
Sports
Football
Soccer
Basketball
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory
thema EDItEUR::S Sports and Active outdoor recreation
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory
thema EDItEUR::S Sports and Active outdoor recreation
Fujii, Keisuke
Machine Learning in Sports
title Machine Learning in Sports
title_full Machine Learning in Sports
title_fullStr Machine Learning in Sports
title_full_unstemmed Machine Learning in Sports
title_short Machine Learning in Sports
title_sort machine learning in sports
topic Artificial intelligence
Machine learning
Deep learning
Real-world data
Prediction
Reinforcement learning
Cyber-physical systems
Modeling
Sports
Football
Soccer
Basketball
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory
thema EDItEUR::S Sports and Active outdoor recreation
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory
thema EDItEUR::S Sports and Active outdoor recreation
topic_facet Artificial intelligence
Machine learning
Deep learning
Real-world data
Prediction
Reinforcement learning
Cyber-physical systems
Modeling
Sports
Football
Soccer
Basketball
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory
thema EDItEUR::S Sports and Active outdoor recreation
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory
thema EDItEUR::S Sports and Active outdoor recreation
url ONIX_20250414_9789819614455_24
work_keys_str_mv AT fujiikeisuke machinelearninginsports