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    Soccer Analytics: An Introduction Using R

    Soccer Analytics by Beggs, Clive;

    An Introduction Using R

    Series: Chapman & Hall/CRC Data Science Series;

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      • Publisher's listprice GBP 52.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        26 818 Ft (25 541 Ft + 5% VAT)
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    26 818 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Edition number 1
    • Publisher Chapman and Hall
    • Date of Publication 11 March 2024

    • ISBN 9781032357584
    • Binding Paperback
    • No. of pages396 pages
    • Size 234x156 mm
    • Weight 730 g
    • Language English
    • Illustrations 63 Illustrations, black & white; 63 Line drawings, black & white; 21 Tables, black & white
    • 704

    Categories

    Short description:

    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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    Long description:

    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:



    • Download and edit soccer data

    • Produce graphics and statistics

    • Predict match outcomes and final league positions

    • Formulate betting strategies

    • Rank teams

    • Construct passing networks

    • Assess match play

    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.



    "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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    Table of Contents:

    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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