Football Analytics with Python & R
Language: English
Published by O'reilly Media Nov 2023, 2023
- Softcover
- New
Seller: Wegmann1855, Zwiesel, GermanyWegmann1855
AbeBooks seller since June 2, 2022
Condition: New
£ 58.72
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Add to basketItem description from seller
Neuware -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.…
Seller Inventory # 9781492099628
- Title
- Football Analytics with Python & R
- Author
- Eric A. Eager
- Publisher
- O'reilly Media Nov 2023
- Publication year
- 2023
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1492099627
- ISBN 13
- 9781492099628
- Item weight
- 617 grams
- Dimensions
- 233x180x22 mm
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
"Synopsis" may belong to another edition of this title.
About the Author
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.
Richard A Erickson helps people use mathematics and statistics to understand our world as well as make decisions with this data. He is a lifelong Green Bay Packer fan, and, like thousands of other cheeseheads, a team owner. He has taught over 25,000 students statistics through graduate-level courses, workshops, and his DataCamp courses on Generalized Linear Models in R and Hierarchical Models in R. He also uses Python on a regular basis to model scientific problems.
Erickson received his PhD in Environmental Toxicology with an applied math minor from Texas Tech where he wrote his dissertation on modeling population-level effects of pesticides. He has modeled and analyzed diverse datasets including topics such as soil productivity for the USDA, impacts of climate change on disease dynamics, and improving rural healthcare. Erickson currently works as a research scientist and has over 70 peer-reviewed publications. Besides teaching Eric about R and Python, he also taught Eric to like cheese curds.
"About the title" may belong to another edition of this title.
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