Clive Beggs

Soccer Analytics

An Introduction Using R

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Chapman and Hall/CRC
• Seitenzahl: 398
• Erscheinungstermin: 11. März 2024
• Englisch
• Abmessung: 240mm x 161mm x 26mm
• Gewicht: 760g
• ISBN-13: 9781032357836
• ISBN-10: 1032357835
• Artikelnr.: 69031308

Autorenporträt

Clive Beggs is Emeritus Professor of Applied Physiology in the Carnegie School of Sport at Leeds Beckett University in the UK. He is both a physiologist and a bio-engineer, who has worked for many years with leading research teams around the world on a wide variety of medical and sport related projects - publishing many scientific papers in both fields. With a background in mathematical modelling of clinical and biological systems, he also has expertise in data analysis and machine learning, which he regularly uses in his sport performance work. Clive is both an amateur runner and soccer fan, and it is his life-long interest in sport and mathematics that has prompted him to write this book.

Inhaltsangabe

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.

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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.

Rezensionen

"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