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            79.4314  3.8219
     New England       76.4212  0.9381
     Seattle           76.2181  7.1051
     San Francisco     75.7042  3.1227
     San Diego         75.2636  2.5741
     Indianapolis      75.1304  3.3280
     Kansas City       74.1443  1.3077
     Philadelphia      73.9770  0.8537
     Baltimore         73.8609  6.8307
     Green Bay         73.7965  4.0348
     Dallas            73.5704  2.7911
     Cincinnati        73.1127  3.0576
     Carolina          72.5947 -0.4316
     Miami             72.1321 -1.4033
     New Orleans       71.0352  6.3640
     Arizona           70.7351  7.0432
     Detroit           70.6730  4.3552
     Chicago           69.9888  1.0944
     NY Giants         68.4764  0.9909
     Pittsburgh        68.0202  3.3416
     Houston           67.1048  2.6247
     Atlanta           66.8044  3.2929
     Minnesota         66.3375  2.7372
     Buffalo           65.7623  7.2186
     Washington        65.6486  0.5695
     Cleveland         65.6328  2.7460
     St Louis          65.5230  3.1434
     Tennessee         65.2167  1.4394
     NY Jets           63.5889  2.7683
     Tampa Bay         63.0469  1.4864
     Oakland           62.4341 -0.2342
     Jacksonville      58.6138  1.0892


Note: Ratings include games through 10/20/14.
 







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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