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
     New England       79.7573  1.1887
     Denver            76.8435  4.6533
     Kansas City       75.5100  1.9347
     Seattle           75.1219  6.9886
     San Francisco     75.0543  2.5845
     Green Bay         75.0527  5.4646
     Miami             74.8782  0.0215
     Indianapolis      73.6237  2.9840
     Philadelphia      73.4207  1.6343
     Cincinnati        72.7835  1.5176
     Baltimore         72.5104  7.2527
     San Diego         72.0562  3.2708
     Dallas            72.0511  2.1963
     Arizona           71.6847  6.8270
     New Orleans       71.6616  5.8321
     Detroit           70.5569  4.5281
     Carolina          70.2153 -0.6555
     Pittsburgh        68.9928  4.6197
     Houston           68.7682  1.9048
     Chicago           67.7904  1.9979
     Atlanta           67.5841  3.1116
     Cleveland         67.5598  1.6799
     NY Giants         67.3959  1.0140
     Minnesota         66.7573  2.6831
     Buffalo           66.7544  6.9621
     St Louis          66.1830  3.8400
     Washington        65.3619 -0.4466
     Tennessee         64.4161  1.2233
     Tampa Bay         64.2035  0.6129
     NY Jets           63.3879  2.4715
     Oakland           63.2940 -0.9093
     Jacksonville      58.7686  1.1763


Note: Ratings include games through 11/17/14.
 







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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