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Analyzing baseball data with R

Author: Max Marchi; Jim Albert
Publisher: Boca Raton : CRC Press, [2014] ©2014
Series: Chapman & Hall/CRC the R series (CRC Press)
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"Preface Baseball has always had a fascination with statistics. Schwarz (2005) documents the quantitative measurements of teams and players since the beginning of professional baseball history in the 19th century. Since the foundation of the Society of Baseball Research in 1971, an explosion of new measures have been developed for understanding offensive and defensive contributions of players. One can learn much  Read more...
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Genre/Form: Statistics
Document Type: Book
All Authors / Contributors: Max Marchi; Jim Albert
ISBN: 9781466570221 1466570229
OCLC Number: 796749730
Description: xvii, 333 pages : illustrations ; 24 cm.
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.
Series Title: Chapman & Hall/CRC the R series (CRC Press)
Responsibility: Max Marchi, Jim Albert.
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Abstract:

"Preface Baseball has always had a fascination with statistics. Schwarz (2005) documents the quantitative measurements of teams and players since the beginning of professional baseball history in the 19th century. Since the foundation of the Society of Baseball Research in 1971, an explosion of new measures have been developed for understanding offensive and defensive contributions of players. One can learn much about the current developments in sabermetrics by viewing articles at websites such as www.baseballprospectus.com, www.hardballtimes.com, and www.fangraphs.com. The quantity and detail of baseball data has exhibited remarkable growth since the birth of the Internet. First data was collected for players and teams for individual seasons { this type of data is what would be dis- played on the back side of a Topps baseball data. The volunteer-run Project Scoresheet organized the collection of play-by-play game data, and this type of data is currently freely available at the Retrosheet organization at www.retrosheet.org/. Since 2006, PITCHf/x data has been measuring the speeds and trajectories of every pitched ball, and newer types of data are collecting the speeds and locations of batted balls and the locations and movements of elders. The ready availability of these large baseball datasets has led to challenges for the baseball enthusiast interested in answering baseball questions with these data. It can be problematic to download and organize the data. Standard statistical software packages may be well-suited for working with small datasets of a specific format, but they are less helpful in merging datasets of different types or performing particular types of analyses, say contour graphs of pitch locations, that are helpful for PITCHf/x data"--

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"There are some great resources out there for learning R and for learning how to analyze baseball data with it. In fact, a few pretty smart people wrote a fantastic book on the subject, Read more...

 
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