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

Autor Eric A. Eager, Richard A. Erickson
en Limba Engleză Paperback – sep 2023
Baseball is not the only sport to use "moneyball." American football fans, teams, and gamblers are increasingly using data to gain an edge against the competition. Professional and college teams use data to help select players and identify team needs. Fans use data to guide fantasy team picks and strategies. Sports bettors and fantasy football players are using data to help inform decision making. This concise book provides a clear introduction to using statistical models to analyze football data.
Whether your goal is to produce a winning team, dominate your fantasy football league, qualify for an entry-level football analyst position, or simply learn R and Python using fun example cases, this book is your starting place. You'll learn how to:
  • Apply basic statistical concepts to football datasets
  • Describe football data with quantitative methods
  • Create efficient workflows that offer reproducible results
  • Use data science skills such as web scraping, manipulating data, and plotting data
  • Implement statistical models for football data
  • Link data summaries and model outputs to create reports or presentations using tools such as R Markdown and R Shiny
  • And more
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Specificații

ISBN-13: 9781492099628
ISBN-10: 1492099627
Pagini: 300
Dimensiuni: 177 x 233 x 19 mm
Greutate: 0.62 kg
Editura: O'Reilly

Descriere

Baseball is not the only sport to use "moneyball." American football fans, teams, and gamblers are increasingly using data to gain an edge against the competition. Professional and college teams use data to help select players and identify team needs. Fans use data to guide fantasy team picks and strategies. Sports bettors and fantasy football players are using data to help inform decision making. This concise book provides a clear introduction to using statistical models to analyze football data.


Notă biografică

Eric A Eager is the Head of Research, Development and Innovation at Pro Football Focus (PFF), where he uses his training as an applied mathematician to produce solutions to quantitative problems for 32 National Football League clients, over 105 NCAA Football clients and numerous media clients and contacts. He also co-hosts the PFF Forecast Podcast, which can be found on PodcastOne and iTunes and is the most popular football analytics podcast in the world since 2018. Additionally, Eager supplies odds used by Steve Kornacki on Football Night in America, the Today Show, and other programs since 2020. He studied applied mathematics and mathematical biology at the University of Nebraska, where he wrote his PhD thesis on how stochasticity and nonlinear processes affect population dynamics. Eager spent his first six years thereafter as a professor at the University of Wisconsin - La Crosse, before transitioning to PFF full-time in 2018. He has since taught statistics and mathematics to over 10,000 students through college-level courses, the Wharton Sports Analytics and Business Initiative's Moneyball Academy, as well as an online course, "Linear Algebra for Data Science in R" with DataCamp.
Eager has been interviewed by nfl.com's Ian Rappoport about Cowboys in-game decision making and The Washington Post for commentary about sports analytics. He joined the legendary Peter King's podcast about fourth-down decisions and is a frequent guest on Cris Collinsworth's podcast.