Analyzing Baseball Data with R, Second Edition

Max Marchi (Cleveland Indians, Ohio, USA), Jim Albert, Benjamin S. Baumer (Smith College, Northhampton, MA) , Jim Albert

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English

Chapman & Hall/CRC
22 November 2018

Probability & statistics; Mathematical & statistical software; Baseball

Series: Chapman & Hall/CRC The R Series

Analyzing Baseball Data with R Second Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis.

The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, catcher framing, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and launch angles and exit velocities. All the datasets and R code used in the text are available online.

New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages, including dplyr, ggplot2, tidyr, purrr, and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability, and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples, you will learn about modern sabermetrics and how to conduct your own baseball analyses.

Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs.

Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports.

Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets, he is a co-author of The Sabermetric Revolution and Modern Data Science with R.

By: Max Marchi (Cleveland Indians Ohio USA), Jim Albert, Benjamin S. Baumer (Smith College, Northhampton, MA), Jim Albert, Benjamin S. Baumer (Smith College, Northhampton, MA)
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Edition: 2nd edition
Dimensions: Height: 234mm, Width: 156mm,
Weight: 839g
ISBN: 9780367024864
ISBN 10: 0367024861
Series: Chapman & Hall/CRC The R Series
Pages: 342
Publication Date: 22 November 2018
Audience: General/trade , Professional and scholarly , ELT Advanced , Undergraduate
Format: Hardback
Publisher's Status: Active

Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets, he is a co-author of The Sabermetric Revolution and Modern Data Science with R.

Reviews for Analyzing Baseball Data with R, Second Edition

Overall, the book meets its main aim of teaching the reader to analyze real data using R. It is well suited to baseball fans, who have a solid statistical background, and want to learn R or modernize their style of R programming. Baseball fans with a more basic statistical education will also learn from this book . . . ~Tim Downie, Journal of Statistical Software