Analyzing Baseball Data with R, Second Edition

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. Content: Cover Half Title Title Page Copyright Page Table of Contents Preface Chapter 1: The Baseball Datasets 1.1 Introduction 1.2 The Lahman Database: Season-by-Season Data 1.2.1 Bonds, Aaron, Ruth, and Rodriguez home run trajectories 1.2.2 Obtaining the database 1.2.3 The Master table 1.2.4 The Batting table 1.2.5 The Pitching table 1.2.6 The Fielding table 1.2.7 The Teams table 1.2.8 Baseball questions 1.3 Retrosheet Game-by-Game Data 1.3.1 The 1998 McGwire and Sosa home run race 1.3.2 Retrosheet 1.3.3 Game logs 1.3.4 Obtaining the game logs from Retrosheet 1.3.5 Game log example1.3.6 Baseball questions 1.4 Retrosheet Play-by-Play Data 1.4.1 Event files 1.4.2 Event example 1.4.3 Baseball questions 1.5 Pitch-by-Pitch Data 1.5.1 MLBAM Gameday and PITCHf/x 1.5.2 PITCHf/x Example 1.5.3 Baseball questions 1.6 Player Movement and Off-the-Bat Data 1.6.1 Statcast 1.6.2 Baseball Savant data 1.6.3 Baseball questions 1.7 Summary 1.8 Further Reading 1.9 Exercises Chapter 2: Introduction to R 2.1 Introduction 2.2 Installing R and RStudio 2.3 The Tidyverse 2.3.1 dplyr 2.3.2 The pipe 2.3.3 ggplot2 2.3.4 Other packages 2.4 Data Frames 2.4.1 Career of Warren Spahn2.4.2 Introduction 2.4.3 Manipulations with data frames 2.4.4 Merging and selecting from data frames 2.5 Vectors 2.5.1 Defining and computing with vectors 2.5.2 Vector functions 2.5.3 Vector index and logical variables 2.6 Objects and Containers in R 2.6.1 Character data and data frames 2.6.2 Factors 2.6.3 Lists 2.7 Collection of R Commands 2.7.1 R scripts 2.7.2 R functions 2.8 Reading and Writing Data in R 2.8.1 Importing data from a file 2.8.2 Saving datasets 2.9 Packages 2.10 Splitting, Applying, and Combining Data 2.10.1 Iterating using map() 2.10.2 Another example2.11 Getting Help 2.12 Further Reading 2.13 Exercises Chapter 3: Graphics 3.1 Introduction 3.2 Character Variable 3.2.1 A bar graph 3.2.2 Add axes labels and a title 3.2.3 Other graphs of a character variable 3.3 Saving Graphs 3.4 Numeric Variable: One-Dimensional Scatterplot and Histogram 3.5 Two Numeric Variables 3.5.1 Scatterplot 3.5.2 Building a graph, step-by-step 3.6 A Numeric Variable and a Factor Variable 3.6.1 Parallel stripcharts 3.6.2 Parallel boxplots 3.7 Comparing Ruth, Aaron, Bonds, and A-Rod 3.7.1 Getting the data 3.7.2 Creating the player data frames3.7.3 Constructing the graph 3.8 The 1998 Home Run Race 3.8.1 Getting the data 3.8.2 Extracting the variables 3.8.3 Constructing the graph 3.9 Further Reading 3.10 Exercises Chapter 4: The Relation Between Runs and Wins 4.1 Introduction 4.2 The Teams Table in the Lahman Database 4.3 Linear Regression 4.4 The Pythagorean Formula for Winning Percentage 4.4.1 The Exponent in the Pythagorean model 4.4.2 Good and bad predictions by the Pythagorean model 4.5 How Many Runs for a Win? 4.6 Further Reading 4.7 Exercises The book will be of interest to basefall fans who want to learn some sabermetrics, and also people who know sabermetrics but would like to use R in their data exploration. Many students do not work on baseball data because the datasets are very large. By learning R through our book, they will be encouraged to do more baseball research on their own.