Anthology of Statistics in Sports

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.

• Chapter 1: IntroductionChapter 2: The Use of Sports in Teaching StatisticsPART I: STATISTICS IN FOOTBALLChapter 3: Introduction to the Football ArticlesChapter 4: A Geometry Model for NFL Field Goal KickersChapter 5: A State-Space Model for National Football League ScoresChapter 6: Predictions for National Football League Games via Linear-Model MethodologyChapter 7: The Best NFL Field Goal Kickers: Are They Lucky or Good?Chapter 8: On the Probability of Winning a Football GamePART II: STATISTICS IN BASEBALLChapter 9: Introduction to the Baseball ArticlesChapter 10: Exploring Baseball Hitting Data: What About Those Breakdown Statistics?Chapter 11: Did Shoeless Joe Jackson Throw the 1919 World Series?Chapter 12: Player Game PercentageChapter 13: Estimation with Selected Binomial Information or Do You Really Believe that Dave Winfield Is Batting .471?Chapter 14: Baseball: Pitching No-Hitters. Chapter 15: Answering Questions About Baseball Using StatisticsChapter 16: The Progress of the Score During a Baseball GamePART III: STATISTICS IN BASKETBALL. Chapter 17: Introduction to the Basketball ArticlesChapter 18: Improved NCAA Basketball Tournament Modeling via Point Spread and Team Strength InformationChapter 19: It’s Okay to Believe in the “Hot Hand”Chapter 20: More Probability Models for the NCAA Regional Basketball TournamentsChapter 21: The Cold Facts About the “Hot Hand” in BasketballChapter 22: Simpson’s Paradox and the Hot Hand in BasketballPART IV: STATISTICS IN ICE HOCKEY. Chapter 23: Introduction to the Ice Hockey ArticlesChapter 24: Statistical Methods for Rating College Hockey TeamsChapter 25: Overtime or Shootout: Deciding Ties in HockeyChapter 26: It Takes a Hot Goalie to Raise the Stanley CupPART V: STATISTICAL METHODOLOGIES AND MULTIPLE SPORTSChapter 27: Introduction to the Methodologies and Multiple Sports ArticlesChapter 28: Bridging Different Eras in SportsChapter 29: Data Analysis Using Stein’s Estimator and Its GeneralizationsChapter 30: Assigning Probabilities to the Outcomes of Multi-Entry CompetitionsChapter 31: Basketball, Baseball, and the Null HypothesisChapter 32: Lessons from Sports StatisticsChapter 33: Can TQM Improve Athletic Performance?Chapter 34: A Brownian Motion Model for the Progress of Sports ScoresPART VI: STATISTICS IN MISCELLANEOUS SPORTSChapter 35: Introduction to the Miscellaneous Sports ArticlesChapter 36: Shooting DartsChapter 37: Drive for Show and Putt for DoughChapter 38: Adjusting Golf Handicaps for the Difficulty of the CourseChapter 39: Rating SkatingChapter 40: Modeling Scores in the Premier League: Is Manchester United Really the Best?Chapter 41: Down to Ten: Estimating the Effect of a Red Card in SoccerChapter 42: Heavy Defeats in Tennis: Psychological Momentum or Random Effect?Chapter 43: Who Is the Fastest Man in theWorld?Chapter 44: Resizing Triathlons for Fairness.