Analyzing Baseball Data with R

Max Marchi is a baseball analyst with the Cleveland Indians. He was previously a statistician at the Emilia-Romagna Regional Health Agency. He has been a regular contributor to The Hardball Times and Baseball Prospectus websites and has consulted for MLB clubs. Jim Albert is a professor of statistics at Bowling Green State University. He has authored or coauthored several books and is the editor of the Journal of Quantitative Analysis of Sports. His interests include Bayesian modeling, statistics education, and the application of statistical thinking in sports.

The Baseball Datasets
Introduction
The Lahman Database: Season-by-Season Data
Retrosheet Game-by-Game Data
Retrosheet Play-by-Play Data
Pitch-by-Pitch Data





Introduction to R
Introduction
Installing R and RStudio
Vectors
Objects and Containers in R
Collection of R Commands
Reading and Writing Data in R
Data Frames
Packages
Splitting, Applying, and Combining Data





Traditional Graphics
Introduction
Factor Variable
Saving Graphs
Dot Plots
Numeric Variable: Stripchart and Histogram
Two Numeric Variables
A Numeric Variable and a Factor Variable
Comparing Ruth, Aaron, Bonds, and A-Rod
The 1998 Home Run Race





The Relation between Runs and Wins
Introduction
The Teams Table in Lahman's Database
Linear Regression
The Pythagorean Formula for Winning Percentage
The Exponent in the Pythagorean Formula
Good and Bad Predictions by the Pythagorean Formula
How Many Runs for a Win?





Value of Plays Using Run Expectancy
The Runs Expectancy Matrix
Runs Scored in the Remainder of the Inning
Creating the Matrix
Measuring Success of a Batting Play
Albert Pujols
Opportunity and Success for All Hitters
Position in the Batting Lineup
Run Values of Different Base Hits
Value of Base Stealing





Advanced Graphics
Introduction
The lattice Package
The ggplot2 Package





Balls and Strikes Effects
Introduction
Hitter's Counts and Pitcher's Counts
Behaviors by Count





Career Trajectories
Introduction
Mickey Mantle's Batting Trajectory
Comparing Trajectories
General Patterns of Peak Ages
Trajectories and Fielding Position





Simulation
Introduction
Simulating a Half Inning
Simulating a Baseball Season





Exploring Streaky Performances
Introduction
The Great Streak
Streaks in Individual At-Bats
Local Patterns of Weighted On-Base Average





Learning about Park Effects by Database Management Tools
Introduction
Installing MySQL and Creating a Database
Connecting R to MySQL
Filling a MySQL Game Log Database from R
Querying Data from R
Baseball Data as MySQL Dumps
Calculating Basic Park Factors





Exploring Fielding Metrics with Contributed R Packages
Introduction
A Motivating Example: Comparing Fielding Metrics
Comparing Two Shortstops





Appendix A: Retrosheet Files Reference
Appendix B: Accessing and Using MLBAM Gameday and PITCHf/x Data





Bibliography


Index





Further Reading and Exercises appear at the end of each chapter.