Sports Analytics : Data-Driven Sports and Decision Intelligence (2024)

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Sports Analytics : Data-Driven Sports and Decision Intelligence (2024)

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  • 製本 Hardcover:ハードカバー版/ページ数 238 p.
  • 商品コード 9783031635724

Full Description

In "Sports Analytics: Data-Driven Sports and Decision Intelligence," embark on a journey through the exhilarating world of sports enhanced by the power of data-driven insights. From the nail-biting moments on the field to the strategic decisions behind the scenes, this comprehensive guide unveils the secrets that propel teams to victory and champions to greatness.It explores the cutting-edge techniques and methodologies that revolutionize the way we understand and analyze sports performance. From player evaluations to game strategies, injury prevention to fan engagement, this book equips you with the tools to gain a competitive edge in any sport. Whether you're a coach, player, analyst, or simply a passionate fan, this book will change the way you see the game.

This book details how to use analytics and machine learning to highlight key performance indicators (KPIs) of sports for analysis. The authors show how to apply various statistical techniques, machine learning and data mining algorithms for on-field and off-field analysis. They go on to show how analytical algorithms are used in the sports ecosystem to derive solutions for the team and leadership, helping team managers and coaches to monitor games and player information through dashboards. The book then shows how to deploy machine learning algorithms for validating and improving teams and players performance. The book is relevant to professionals and academics working in machine learning and data analysis related to sports.

Contents

Introduction.- Visualizing in Sports Analytics.- IoT and Block Chain in Sports.- Sports Revenue Analytics.- Sports Marketing Analytics.- Forecasting in Sports.- Machine learning & AI in sports.- Classification and Regression techniques for sports.- Sports Analytics in different sports.- 'Sports analytics data'-Who uses it?.- Case Studies.- Conclusion.

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