Analyzing Baseball Data with R

ISBN-10: 1466570229

ISBN-13: 9781466570221

Edition: 2013

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This practical guide helps statisticians, students, and baseball fans learn about sabermetrics and the use of R in data exploration. It covers the relation between runs and wins and the runs expectancy matrix as well as how to visualize pitch trajectories and umpires' tendencies and how to uncover patterns of players and teams. The authors also examine the effects of ballpark dimensions and weather conditions, compare the intra-player correlations of several baseball stats, and explore recent progress in the objective evaluation of defense.
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Book details

List price: $31.99
Copyright year: 2013
Publisher: Taylor & Francis Group
Publication date: 10/29/2013
Binding: Paperback
Pages: 334
Size: 6.00" wide x 9.00" long x 1.00" tall
Weight: 1.276
Language: English

The Baseball Datasets
The Lahman Database: Season-by-Season Data
Retrosheet Game-by-Game Data
Retrosheet Play-by-Play Data
Pitch-by-Pitch Data
Introduction to R
Installing R and RStudio
Objects and Containers in R
Collection of R Commands
Reading and Writing Data in R
Data Frames
Splitting, Applying, and Combining Data
Traditional Graphics
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
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
The lattice Package
The ggplot2 Package
Balls and Strikes Effects
Hitter's Counts and Pitcher's Counts
Behaviors by Count
Career Trajectories
Mickey Mantle's Batting Trajectory
Comparing Trajectories
General Patterns of Peak Ages
Trajectories and Fielding Position
Simulating a Half Inning
Simulating a Baseball Season
Exploring Streaky Performances
The Great Streak
Streaks in Individual At-Bats
Local Patterns of Weighted On-Base Average
Learning about Park Effects by Database Management Tools
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
A Motivating Example: Comparing Fielding Metrics
Comparing Two Shortstops
Retrosheet Files Reference
Accessing and Using MLBAM Gameday and PITCHf/x Data
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