Football Analytics with Python and R : Learning Data Science Through the Lens of Sports
Idioma: inglés
Editorial: O'Reilly Media, 2023
- Tapa blanda
- Usado
Librería: World of Books Inc, Montgomery, IL, Estados Unidos de AmericaWorld of Books Inc
Vendedor de AbeBooks desde 23 de marzo de 2026
Condición: Usado - Aceptable
EUR 25,47
Cantidad disponible: 2 disponibles
Añadir al carritoDescripción del artículo del vendedor
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.…
N° de ref. del artículo CIN1492099627G
- Título
- Football Analytics with Python and R : Learning Data Science Through the Lens of Sports
- Autor
- Eric Eager
- Editorial
- O'Reilly Media
- Año de publicación
- 2023
- Estado
- Good
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1492099627
- ISBN 13
- 9781492099628
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
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
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.
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World of Books Inc
Montgomery, IL, Estados Unidos de America
Vendedor de AbeBooks desde 23 de marzo de 2026
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