The Dr. Edward Kambour NFL Football Ratings

2014 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
     Denver            79.6192  3.9289
     New England       77.3894  1.2642
     Seattle           76.1200  7.0430
     San Francisco     75.5442  3.1107
     Kansas City       75.1286  1.7690
     San Diego         75.0141  2.6167
     Indianapolis      74.0321  3.6163
     Baltimore         73.9091  6.8472
     Philadelphia      73.8404  0.8745
     Cincinnati        73.2123  3.0093
     Dallas            72.8338  2.5630
     Green Bay         72.7287  4.2771
     Carolina          72.4854 -0.4729
     Miami             72.3744 -1.4162
     New Orleans       71.7735  6.8063
     Arizona           70.7558  7.0452
     Detroit           70.4549  4.4205
     Pittsburgh        68.9792  3.7910
     Chicago           68.8371  1.3800
     NY Giants         68.4812  1.0451
     Houston           68.0449  2.4217
     Buffalo           67.0759  6.9182
     Atlanta           67.0191  3.1973
     Minnesota         66.6995  2.6601
     Washington        66.5676  0.2616
     Cleveland         66.0517  2.8786
     Tennessee         64.6409  1.0608
     St Louis          64.1151  3.5077
     Tampa Bay         62.9362  1.2953
     NY Jets           62.6810  2.3653
     Oakland           62.1902 -0.1456
     Jacksonville      58.4646  1.0822


Note: Ratings include games through 10/27/14.
 







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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