A state-space model for national football league scores

Journal of the American Statistical Association

Volumn 93, Issue 441, 1998, Pages 25-35

  Glickman, Mark E ^a   Stern, Hal S ^b  

^a Boston University ^*  (United States)

^b Iowa Sate University ^*  (United States)

Author keywords

Bayesian diagnostics; Dynamic models; Kalman filter; Markov chain Monte Carlo; Predictive inference

Indexed keywords

EID: 0032329161     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.1998.10474084     Document Type: Article

References (30)

1. Amoako-Adu, B., Marmer, H., and Yagil, J. (1985), 'The Efficiency of Certain Speculative Markets and Gambler Behavior," Journal of Economics and Business, 37, 365-378.
2. Carlin, B. P., Poison, N. G., and Stoffer, D. S. (1992), "A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling," Journal of the American Statistical Association, 87, 493-500.
3. Carter, C. K., and Kohn, R. (1994), "On Gibbs Sampling for State-Space Models," Biometrika, 81, 541-553.
4. Chaloner, K., and Brant, R. (1988), "A Bayesian Approach to Outlier Detection and Residual Analysis," Biometrika, 75, 651-659.
5. de Jong, P., and Shephard, N. (1995), "The Simulation Smoother for Time Series Models," Biometrika, 82, 339-350.
6. Fruhwirth-Schnatter, S. (1994), "Data Augmentation and Dynamic Linear Models," Journal of Time Series Analysis, 15, 183-202.
7. Gelfand, A. E., and Smith, A. F. M. (1990), "Sampling-Based Approaches to Calculating Marginal Densities," Journal of the American Statistical Association, 85, 972-985.
8. Gelman, A., Carlin, J. B., Stem, H. S., and Rubin, D. B. (1995), Bayesian Data Analysis, London: Chapman and Hall.
9. Gelman, A., Meng, X., and Stem, H. S. (1996), "Posterior Prcdictive Assessment of Model Fitness via Realized Discrepancies" (with discussion), Statistica Sinica, 6, 733-807.
10. Gelman, A., and Rubin, D. B. (1992), "Inference From Iterative Simulation Using Multiple Sequences," Statistical Science, 7, 457-511.
11. Geman, S., and Geman, D. (1984), "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
12. Glickman, M. E. (1993), "Paired Comparison Models With Time-Varying Parameters," unpublished Ph.D. dissertation, Harvard University, Dept, of Statistics.
13. Harrison, P. J., and Stevens, C. F. (1976), "Bayesian Forecasting’' Journal of the Royal Statistical Society, Ser. B, 38, 240-247.
14. Harville, D. (1977), "The Use of Linear Model Methodology to Rate High School or College Football Teams," Journal of the American Statistical Association, 72, 278-289.
15. -(1980), "Predictions for National Football League Games via Linear-Model Methodology," Journal of the American Statistical Association, 75, 516-524.
16. Kalman, R. E. (1960), "A New Approach to Linear Filtering and Prediction Problems," Journal of Basic Engineering, 82, 34-45.
17. Kalman, R. E., and Bucy, R. S. (1961), "New Results in Linear Filtering and Prediction Theory," Journal of Basic Engineering, 83, 95-108.
18. Rosner, B. (1976), "An Analysis of Professional Football Scores," in Management Science in Sports, eds. R. E. Machol, S. P. Ladany, and D. G. Morrison, New York: North-Holland, pp. 67-78.
19. Rubin, D. B. (1984), "Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician," The Annals of Statistics, 12, 1151-1172.
20. Sallas, W. M., and Harville, D. A. (1988), "Noninformative Priors and Restricted Maximum Likelihood Estimation in the Kalman Filter," in Bayesian Analysis of Time Series and Dynamic Models, ed. J. C. Spall, New York: Marcel Dekker, pp. 477-508.
21. Shephard, N. (1994), "Partial Non-Gaussian State Space," Biometrika, 81, 115-131.
22. Smith, A. F. M. (1983), "Bayesian Approaches to Outliers and Robustness," in Specifying Statistical Models From Parametric to Nonparamet-ric. Using Bayesian or Non-Bayesian Approaches, eds. J. P. Florens, M. Mouchart, J. P. Raoult, L. Simer, and A. F. M. Smith, New York: Springer-Verlag, pp. 13-35.
23. Stem, H. (1991), "On the Probability of Winning a Football Game," The American Statistician, 45, 179-183.
24. Stem, H. (1992), "Who's Number One? Rating Football Teams," in Proceedings of the Section on Statistics in Sports, American Statistical Association, pp. 1-6.
25. Stefani, R. T. (1977), "Football and Basketball Predictions Using Least Squares," IEEE Transactions on Systems, Man, and Cybernetics, 7, 117-120.
26. Stefani, R. T. (1980), "Improved Least Squares Football, Basketball, and Soccer Predictions," IEEE Transactions on Systems, Man, and Cybernetics, 10, 116-123.
27. Thompson, M. (1975), "On Any Given Sunday: Fair Competitor Orderings With Maximum Likelihood Methods," Journal of the American Statistical Association, 70, 536-541.
28. West, M., and Harrison, P. J. (1990), Bayesian Forecasting and Dynamic Models, New York: Springer-Verlag.
29. Zellner, A. (1975), "Bayesian Analysis of Regression Error Terms," Journal of the American Statistical Association, 70, 138-144.
30. Zuber, R. A., Gandar, J. M., and Bowers, B. D. (1985), "Beating the Spread: Testing the Efficiency of the Gambling Market for National Football League Games," Journal of Political Economy, 93, 800-806.