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.

1. Introduction; 2. The use of sports in teaching statistics; Part I. Statistics in Football. 3. Introduction to the football articles; 4. A geometry model for NFL field goal kickers; 5. A state-space model for National Football League scores; 6. Predictions for National Football League games via linear-model methodology; 7. The best NFL Field goal kickers. are they lucky or good?; 8. On the probability of winning a football game; Part II. Statistics in Baseball.. 9. Introduction to the baseball Articles; 10. Exploring baseball Hitting Data. What About Those breakdown Statistics?; 11. Did shoeless Joe Jackson Throw the 1919 World Series?; 12. Player Game Percentage; 13. Estimation with selected binomial Information or Do You Really Believe that Dave Winfield Is batting .471?; 14. Baseball: Pitching No-Hitters; 15. Answering Questions about baseball Using statistics; 16. The Progress of the score During a Baseball Game; Part III. Statistics in Basketball: 17. Introduction to the Basketball Articles; 18. Improved NCAA Basketball Tournament Modeling via Point Spread and Team strength Information; 19. It's Okay to Believe in the 'hot hand'; 20. More Probability models for the NCAA Regional basketball Tournaments; 21. The Cold Facts About the 'hot hand' in basketball; 22. Simpson's Paradox and the Hot Hand in basketball; Part IV. Statistics in Ice Hockey: 23. Introduction to the Ice Hockey Articles; 24. Statistical Methods for Rating College Hockey Teams; 25. Overtime or shootout. Deciding Ties in Hockey; 26. It Takes a Hot Goalie to Raise the Stanley Cup; Part V. Statistical Methodologies and Multiple Sports: 27. Introduction to the Methodologies and Multiple Sports Articles; 28. Bridging Different Eras in sports; 29. Data Analysis Using Stein's Estimator and Its Generalizations; 30. Assigning Probabilities to the Outcomes of Multi-Entry Competitions; 31. Basketball, Baseball, and the Null Hypothesis; 32. Lessons from sports statistics; 33. Can TQM Improve Athletic Performance?; 34. A Brownian Motion Model for the Progress of sports scores; Part VI. Statistics in Miscellaneous Sports: 35. Introduction to the Miscellaneous Sports Articles; 36. Shooting Darts; 37. Drive for show and Putt for Dough; 38. Adjusting Golf handicaps for the difficulty of the Course; 39. Rating Skating; 40. Modeling scores in the Premier League. is Manchester United really the Best?; 41. Down to Ten. Estimating the effect of a Red Card in Soccer; 42. Heavy Defeats in Tennis. Psychological Momentum or Random effect?; 43. Who is the fastest Man in the World?; 44. Resizing Triathlons for Fairness.