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Football Analytics with Python & R: Learning Data Science Through the Lens of Sports

Football Analytics with Python & R: Learning Data Science Through the Lens of Sports

Football Analytics with Python & R: Learning Data Science Through the Lens of Sports Paperback / softback - 2023

by Eric Eager

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Details

  • Title Football Analytics with Python & R: Learning Data Science Through the Lens of Sports
  • Author Eric Eager
  • Binding Paperback
  • Condition New
  • Pages 349
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2023-09-19
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # A9781492099628
  • ISBN 9781492099628 / 1492099627
  • Weight 1.23 lbs (0.56 kg)
  • Dimensions 9.1 x 6.8 x 0.9 in (23.11 x 17.27 x 2.29 cm)
  • Category Computers - Data Base Management
  • Library of Congress subjects Python (Computer program language), R (Computer program language)
  • Library of Congress Catalogue Number 2023279858
  • Dewey Decimal Code 796.332
  • Quantity available 10

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From the publisher

Baseball is not the only sport to use "moneyball." American football teams, fantasy football players, fans, and gamblers are increasingly using data to gain an edge on the competition. Professional and college teams use data to help identify team needs and select players to fill those needs. Fantasy football players and fans use data to try to defeat their friends, while sports bettors use data in an attempt to defeat the sportsbooks.

In this concise book, Eric Eager and Richard Erickson provide a clear introduction to using statistical models to analyze football data using both Python and R. Whether your goal is to qualify for an entry-level football analyst position, dominate your fantasy football league, or simply learn R and Python with fun example cases, this book is your starting place.

Through case studies in both Python and R, you'll learn to:

  • Obtain NFL data from Python and R packages and web scraping
  • Visualize and explore data
  • Apply regression models to play-by-play data
  • Extend regression models to classification problems in football
  • Apply data science to sports betting with individual player props
  • Understand player athletic attributes using multivariate statistics

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