E-raamat: Introduction to NFL Analytics with R

Presents an introduction to the analysis of NFL data using R. It emphasizes the use of the tidyverse in R, together with NFL-specific packages, such as nflverse, nflfastR, and nflreadr. It covers the entire sports analytics framework, including data collection, cleaning and wrangling, visualization, analysis, and advanced methods.

It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans, and commentators, throughout broadcasts, regularly use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement, highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavour, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by-step instructions in a structured, jargon-free manner

Key Coverage:

• Installing R, RStudio, and necessary packages

• Working and becoming fluent in the tidyverse

• Finding meaning in NFL data with examples from all the functions in the nflverse family of packages

• Using NFL data to create eye-catching data visualizations

• Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient Boosting

The book is written for novices of R programming all the way to more experienced coders, as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL, while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists, bloggers, and arm-chair quarterbacks alike, will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.