商品簡介
Chapter 1: Introduction; Chapter 2: The Use of Sports in Teaching Statistics; PART I: STATISTICS IN FOOTBALL. Chapter 3: Introduction to the Football Articles; Chapter 4: A Geometry Model for NFL Field Goal Kickers; Chapter 5: A State-Space Model for National Football League Scores; Chapter 6: Predictions for National Football League Games via Linear-Model Methodology; Chapter 7: The Best NFL Field Goal Kickers: Are They Lucky or Good?; Chapter 8: On the Probability of Winning a Football Game; PART II: STATISTICS IN BASEBALL. Chapter 9: Introduction to the Baseball Articles; Chapter 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 Percentage; Chapter 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 Statistics; Chapter 16: The Progress of the Score During a Baseball Game; 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; PART IV: STATISTICS IN ICE HOCKEY. Chapter 23: Introduction to the Ice Hockey Articles; Chapter 24: Statistical Methods for Rating College Hockey Teams; Chapter 25: Overtime or Shootout: Deciding Ties in Hockey; Chapter 26: It Takes a Hot Goalie to Raise the Stanley Cup; PART V: STATISTICAL METHODOLOGIES AND MULTIPLE SPORTS. Chapter 27: Introduction to the Methodologies and Multiple Sports Articles; Chapter 28: Bridging Different Eras in Sports; Chapter 29: Data Analysis Using Stein’s Estimator and Its Generalizations; Chapter 30: Assigning Probabilities to the Outcomes of Multi-Entry Competitions; Chapter 31: Basketball, Baseball, and the Null Hypothesis; Chapter 32: Lessons from Sports Statistics; Chapter 33: Can TQM Improve Athletic Performance?; Chapter 34: A Brownian Motion Model for the Progress of Sports Scores; PART VI: STATISTICS IN MISCELLANEOUS SPORTS. Chapter 35: Introduction to the Miscellaneous Sports Articles; Chapter 36: Shooting Darts; Chapter 37: Drive for Show and Putt for Dough; Chapter 38: Adjusting Golf Handicaps for the Difficulty of the Course; Chapter 39: Rating Skating; Chapter 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 Soccer; Chapter 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.
作者簡介
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.