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            78.1426  3.3569
     New England       77.2106  1.3545
     Seattle           76.8562  6.8636
     San Francisco     76.7805  2.8801
     Cincinnati        75.6559  2.5220
     San Diego         75.4800  2.7362
     Carolina          74.0123 -0.7372
     Philadelphia      73.6184  0.8735
     Dallas            73.4818  2.7623
     Baltimore         73.4770  6.7435
     Kansas City       73.3647  1.5501
     Indianapolis      73.0713  2.7095
     Green Bay         72.9561  3.5517
     Miami             71.0767 -1.1509
     New Orleans       70.9884  6.4065
     Chicago           70.8643  1.4696
     Detroit           70.8639  4.4557
     Arizona           70.3234  7.2550
     NY Giants         68.6239  0.9679
     Pittsburgh        68.0084  3.4223
     Cleveland         67.9037  2.1969
     Atlanta           67.8125  3.1025
     Houston           67.0631  2.5882
     Buffalo           66.2194  7.3353
     Minnesota         65.8593  2.9739
     Washington        65.4218  0.5626
     Tennessee         65.0512  1.5169
     St Louis          64.8795  2.9428
     Tampa Bay         63.1525  1.5145
     NY Jets           62.5416  3.0520
     Oakland           62.4870 -0.1202
     Jacksonville      56.7519  0.2305


Note: Ratings include games through 10/13/14.
 







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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