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Aimed at all those interested in analysing soccer data, be they fans, gamblers, coaches, sports scientists, or data scientists and statisticians wishing to pursue a career in professional soccer. It aims to equip the reader with the knowledge and skills required to confidently analyse soccer data using R, all in a few easy lessons.
Sports analytics is on the rise, with top soccer clubs, bookmakers, and broadcasters all employing statisticians and data scientists to gain an edge over their competitors.
Many popular books have been written exploring the mathematics of soccer. However, few supply details on how soccer data can be analysed in real-life. The book addresses this issue via a practical route one approach designed to show readers how to successfully tackle a range of soccer related problems using the easy-to-learn computer language R. Through a series of easy-to-follow examples, the book explains how R can be used to:
Soccer Analytics: An Introduction Using R is a comprehensive introduction to soccer analytics aimed at all those interested in analysing soccer data, be they fans, gamblers, coaches, sports scientists, or data scientists and statisticians wishing to pursue a career in professional soccer. It aims to equip the reader with the knowledge and skills required to confidently analyse soccer data using R, all in a few easy lessons.
Inhaltsverzeichnis
1. Soccer analytics: the way ahead. 2. Getting started with R. 3. Using R to harvest and process soccer data. 4. Match data and league tables. 5. Predicting end-of-season league position. 6. Predicting soccer match outcomes. 7. Betting strategies. 8. Who are the key players? Using passing networks to analyse match play. 9. Which is the best team? Ranking systems in soccer. 10. Using linear regression to analyse match performance data. 11. Successful data analytics.
"As someone who shares with the author a lifelong interest in sports in general and soccer in particular I fully agree with the authors' premise of writing this book. The organization of chapters around picking up specific skills is useful. The provision of R scripts and data (through a Github page associated with the book) is
welcome and will encourage readers to delve into their own analytics. I believe that this book overall successfully covers its targeted niche."
~Alexander Aue, Journal of the American Statistical Association
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