The Dr. Edward Kambour NFL Football Ratings

2016 Season Ratings


Below are the ratings for NFL football. The first column is the team, followed by the estimated power rating and home-field advantage. To forecast the outcome of a game simply subtract the visiting team's rating from the sum of the home team's rating and the home team's home field advantage. The difference is approximately the forecasted point-spread. Thus, if the result is positive, the home team is predicted to win, while if the result is negative, the visiting team is predicted to win. The teams are ranked by their ratings.

Predictions of this weekend's games can be found here.

  

                        Rating  HomeAd
     New England       79.0136  2.2069
     Seattle           76.7400  2.9760
     Kansas City       76.4159  1.0845
     Denver            75.3969  2.5465
     Pittsburgh        74.1489  4.4356
     Cincinnati        73.7603  0.6425
     Dallas            73.4772  0.9341
     Arizona           73.0513  5.3971
     Carolina          72.8153  1.6853
     Philadelphia      72.2472  0.4811
     Buffalo           72.2264  5.0518
     Minnesota         71.8028  2.9846
     Atlanta           71.0679  2.9377
     Oakland           70.2629  0.6185
     New Orleans       70.2487  3.7392
     San Diego         70.2432  1.9042
     Green Bay         70.2076  5.1708
     Washington        69.8742  1.0586
     Miami             69.7424 -0.4391
     NY Giants         69.6130  1.5001
     Baltimore         68.3876  4.7014
     Detroit           68.0900  4.5609
     Tampa Bay         68.0797 -0.8984
     Houston           67.5902  1.7962
     NY Jets           66.9503  3.8800
     Los Angeles       66.2936  3.6584
     Indianapolis      65.4853  3.8767
     Tennessee         65.2020 -0.0429
     Chicago           65.0388  0.0427
     San Francisco     63.7943  3.3954
     Jacksonville      61.9132  3.0591
     Cleveland         60.8195  0.9652


Note: Ratings include games through November 28, 2016.
 







Note: These ratings are the result of a Dynamic Hierarchical Bayesian Linear Forecaster. The author has a Ph.D. in Statistics from Texas A&M. He specializes in Bayesian Forecasting. The forecasting method has been presented at four technical conferences, the 1997 and 1998 Conferences of Texas Statisticians, as an invited presentation at the 2001 Joint Statistical Meetings , and at a 2003 Houston INFORMS meeting. The powerpoint slides from the INFORMS talk are available here.

Email:edwardkambour@sbcglobal.net

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