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The Basketball Game Box Score

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Uploaded: 30/03/02
Updated: 09/01/09

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A Box Score summarizes a Basketball game, and displays all of the major statistical categories for every player who entered the game.
Box Score stats are those stats that are officially recorded for each game and reported in the box score.




The Basketball Box Score Stats

by Mario Sebastiani, Spain - March 30, 2002 ~ Updated: January 9, 2009

A Basketball game Box Score summarizes a Basketball game, and displays all of the major statistical categories for every player who entered the game.
Here we add to those stats that are officially recorded and reported for each game, our interpretation and vision of a complete box score.

The perfect Box Score, and there is no so much like this, should fulfill the following principles: ......



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Related Source

"Anthology of Statistics in Sports"
The book by Jim Albert, Jay Bennett, and James J. Cochran Anthology of Statistics in SportsAnthology of Statistics in Sports, contains  36 articles  which are organized, along with eight introductory articles, in one volume with six parts, each on a different sport or theme: football, baseball, basketball, ice hockey, statistical methodologies and multiple sports. Each part includes a short introductory article followed by anywhere from three to nine articles. Edited with newly written material from the editors and other notable contributors which introduces each section of the book, and a chapter with suggestions on using the articles in the classroom is included.

In the title of Chapter 19, Patrick D. Larkey, Richard A. Smith, and Joseph B. Kadane proclaim that "It's Okay to Believe in the 'Hot Hand'." meanwhile Amos Tversky and Thomas Gilovich declare an opposing view in Chapter 21, "The Cold Facts about the 'Hot Hand' in Basketball."  Robert Hooke takes a philosophical approach in Chapter 31, "Basketball, Baseball, and the Null Hypothesis," to speculate on why statisticians such as Tversky and Gilovich don't observe the "hot hand" phenomenon even though intuition and experience shout that it must exist.
The introductory survey article by editors Albert and Cochran in Chapter 2 is particularly helpful and give references containing additional examples and discussing how to teach a special probability and statistics class focused on sports.
This book grew from the efforts of members of the ASA - American Statistical Association - Section on Statistics in Sports, which is dedicated to promoting high professional standards in the application of statistics to sports and fostering statistical education in sports.

PART III: STATISTICS IN BASKETBALL. Chapter 17: Introduction to the Basketball Articles; Chapter 18: Improved NCAA Basketball Tournament Modeling via Point Spread and Team Strength Information; Chapter 19: It’s Okay to Believe in the “Hot Hand”; Chapter 20: More Probability Models for the NCAA Regional Basketball Tournaments; Chapter 21: The Cold Facts About the “Hot Hand” in Basketball; Chapter 22: Simpson’s Paradox and the Hot Hand in Basketball....

This book will appeal to statisticians with an interest in sports who could use the chapters as engaging classroom examples. Sports fans with some understanding of statistics also will find the book of interest, especially the chapters written at a modest mathematical level. This is an enjoyable and useful book.

About the Editors:
Jim Albert is a Professor of Mathematics and Statistics at Bowling Green State University. His research interests include Bayesian inference and model selection for generalized linear models, statistical education, and the application of statistical methods to sports.

Jay Bennett is a Principal Scientist with Telcordia Technologies and has experience in traffic engineering, network reliability, and software reliability. His research interests include quality of service metrics and applications of statistical methods to sports and entertainment.

James J. Cochran is an Assistant Professor in the Department of Marketing and Analysis at Louisiana Tech University. His research interests include statistical methods (particularly general linear models), statistical learning, and stochastic combinatorial optimization.

All the Editors are Fellow of the ASA - American Statistical Association.

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