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Full Description
With its flexible capabilities and open-source platform, R has become a major tool for analyzing detailed, high-quality baseball data. Analyzing Baseball Data with R provides an introduction to R for sabermetricians, baseball enthusiasts, and students interested in exploring the rich sources of baseball data. It equips readers with the necessary skills and software tools to perform all of the analysis steps, from gathering the datasets and entering them in a convenient 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 traditional graphics functions in the base package and introduce more sophisticated graphical displays available through the lattice and ggplot2 packages. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and fielding measures. Each chapter contains exercises that encourage readers to perform their own analyses using R. All of the datasets and R code used in the text are available online.This book helps readers answer questions about baseball teams, players, and strategy using large, publically available datasets. It offers detailed instructions on downloading the datasets and putting them into formats that simplify data exploration and analysis. Through the book's various examples, readers will learn about modern sabermetrics and be able to conduct their own baseball analyses.
Contents
The Baseball Datasets Introduction The Lahman Database: Season-by-Season DataRetrosheet Game-by-Game DataRetrosheet Play-by-Play Data Pitch-by-Pitch DataIntroduction to R Introduction Installing R and RStudio VectorsObjects and Containers in R Collection of R Commands Reading and Writing Data in R Data Frames Packages Splitting, Applying, and Combining DataTraditional 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-RodThe 1998 Home Run RaceThe 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 StealingAdvanced Graphics IntroductionThe lattice PackageThe ggplot2 PackageBalls and Strikes Effects Introduction Hitter's Counts and Pitcher's CountsBehaviors by CountCareer Trajectories Introduction Mickey Mantle's Batting Trajectory Comparing TrajectoriesGeneral Patterns of Peak Ages Trajectories and Fielding PositionSimulation IntroductionSimulating a Half InningSimulating a Baseball SeasonExploring Streaky Performances Introduction The Great Streak Streaks in Individual At-Bats Local Patterns of Weighted On-Base AverageLearning 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 RQuerying Data from R Baseball Data as MySQL Dumps Calculating Basic Park FactorsExploring Fielding Metrics with Contributed R Packages Introduction A Motivating Example: Comparing Fielding MetricsComparing Two ShortstopsAppendix A: Retrosheet Files ReferenceAppendix B: Accessing and Using MLBAM Gameday and PITCHf/x DataBibliography IndexFurther Reading and Exercises appear at the end of each chapter.
