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Analyzing baseball data with R /

Marchi, Max.

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Analyzing baseball data with R /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Analyzing baseball data with R // Max Marchi, Jim Albert.
作者:	Marchi, Max.
其他作者:	Albert, Jim.
出版者:	Boca Raton :CRC Press, : 2014.,
面頁冊數:	xvii, 333 p. :ill. ;24 cm.
標題:	Baseball - Fiction. -
ISBN:	9781466570221

Analyzing baseball data with R /
Marchi, Max.

Analyzing baseball data with R /Max Marchi, Jim Albert. - Boca Raton :CRC Press,2014. - xvii, 333 p. :ill. ;24 cm. - R series..

Includes bibliographical references (pages 325-328) and index.

Preface Baseball has always had a fascination with statistics. Schwarz (2005) docu- ments the quantitative measurements of teams and players since the begin- ning 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 o ensive and defensive contributions of players. One can learn much about the current developments in sabermet- rics 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 col- lecting the speeds and locations of batted balls and the locations and move- ments 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. Stan- dard statistical software packages may be well-suited for working with small datasets of a speci c format, but they are less helpful in merging datasets of di erent types or performing particular types of analyses, say contour graphs of pitch locations, that are helpful for PITCHf/x data.

ISBN: 9781466570221GBP26.99

LCCN: 2013039505Subjects--Topical Terms:

1282673
Baseball
--Fiction.

LC Class. No.: GV877 / .M353 2014

Dewey Class. No.: 796.3570727

Analyzing baseball data with R /
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520 # $a Preface Baseball has always had a fascination with statistics. Schwarz (2005) docu- ments the quantitative measurements of teams and players since the begin- ning 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 o ensive and defensive contributions of players. One can learn much about the current developments in sabermet- rics 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 col- lecting the speeds and locations of batted balls and the locations and move- ments 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. Stan- dard statistical software packages may be well-suited for working with small datasets of a speci c format, but they are less helpful in merging datasets of di erent types or performing particular types of analyses, say contour graphs of pitch locations, that are helpful for PITCHf/x data.
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