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9780898715873

Anthology Of Statistics In Sports

by ; ;
  • ISBN13:

    9780898715873

  • ISBN10:

    0898715873

  • Format: Paperback
  • Copyright: 2005-05-01
  • Publisher: Society for Industrial & Applied

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Summary

The worlds of sports fans and statisticians collide in this interesting and accessible collection of previously published articles on the use of statistics to analyze sports, that the editors have thoughtfully culled from a variety of American Statistical Association (ASA) publications. Heavily weighted in the areas of competition (rating players and teams, evaluating strategies for victory), the articles vary in mathematical complexity, but most will be accessible to readers with a general knowledge of statistics. Newly written material from the editors and other notable contributors introduces each section of the book, and a chapter with suggestions on using the articles in the classroom is included.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.

Author Biography

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. He is a Fellow of the ASA and is a member of the Mathematical Association of America. 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. He is a Fellow of the ASA and is active in the ASA Section on Statistics in Sports. 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. He is a past President of the ASA Section on Statistics in Sports and the current President of the INFORMS Section on OR in Sports.

Table of Contents

Introduction
The use of sports in teaching statistics
Statistics in Football:
Introduction to the football articles
A geometry model for NFL field goal kickers
A state-space model for National Football League scores
Predictions for National Football League games via linear-model methodology
The best NFL Field goal kickers: are they lucky or good?
On the probability of winning a football game
Statistics in Baseball:
Introduction to the baseball Articles
Exploring baseball Hitting Data: What About Those breakdown Statistics?
Did shoeless Joe Jackson Throw the 1919 World Series?
Player Game Percentage
Estimation with selected binomial Information or Do You Really Believe that Dave Winfield Is batting .471?
Baseball: Pitching No-Hitters
Answering Questions about baseball Using statistics
The Progress of the score During a Baseball Game
Statistics in Basketball
Introduction to the Basketball Articles
Improved NCAA Basketball Tournament Modeling via Point Spread and Team strength Information
It's Okay to Believe in the 'hot hand'
More Probability models for the NCAA Regional basketball Tournaments
The Cold Facts About the 'hot hand' in basketball
Simpson's Paradox and the Hot Hand in basketball
Statistics in Ice Hockey
Introduction to the Ice Hockey Articles
Statistical Methods for Rating College Hockey Teams
Overtime or shootout: Deciding Ties in Hockey
It Takes a Hot Goalie to Raise the Stanley Cup
Statistical Methodologies and Mutliple Sports
Introduction to the Methodologies and Multiple Sports Articles
Bridging Different Eras in sports
Data Analysis Using Stein's Estimator and Its Generalizations
Assigning Probabilities to the Outcomes of Multi-Entry Competitions
Basketball, Baseball, and the Null Hypothesis
Lessons from sports statistics
Can TQM Improve Athletic Performance?
A Brownian Motion Model for the Progress of sports scores
Statistics in Miscellaneous Sports
Introduction to the Miscellaneous Sports Articles
Shooting Darts
Drive for show and Putt for Dough
Adjusting Golf handicaps for the difficulty of the Course
Rating Skating
Modeling scores in the Premier League: is Manchester United really the Best?
Down to Ten: Estimating the effect of a Red Card in Soccer
Heavy Defeats in Tennis: Psychological Momentum or Random effect?
Who is the fastest Man in theWorld?
Resizing Triathlons for Fairness
Table of Contents provided by Publisher. All Rights Reserved.

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