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       80.9939  1.8889
     Denver            77.9790  3.6400
     Seattle           77.1020  6.6318
     Green Bay         74.2347  5.6345
     Kansas City       74.1290  2.1714
     Indianapolis      73.8468  3.3350
     Philadelphia      73.5983  1.1203
     Cincinnati        73.5162  0.4285
     Miami             73.2299 -0.0089
     Baltimore         73.0681  6.3927
     San Francisco     72.7411  2.3180
     Dallas            72.5331  1.4331
     San Diego         71.7582  2.6474
     Arizona           71.1113  6.9827
     Carolina          70.9956 -0.9460
     New Orleans       70.8854  4.3450
     Pittsburgh        70.2313  4.0449
     Detroit           70.1236  5.0307
     Buffalo           69.3947  6.6339
     Houston           69.1690  2.0958
     St Louis          69.0640  4.2625
     NY Giants         68.4280  0.9742
     Atlanta           67.8873  2.9156
     Minnesota         67.8732  2.9624
     Chicago           66.5593  1.7806
     Cleveland         66.2672  1.3421
     Tampa Bay         64.0954  1.1922
     Washington        64.0659 -0.8777
     NY Jets           62.2306  3.1081
     Oakland           62.0187  1.0003
     Tennessee         61.9198  0.7964
     Jacksonville      58.9496  1.2007




Note: Ratings include games through 12/15/14.

 



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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