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       80.1929  2.1283
     Seattle           78.4751  6.0897
     Denver            76.9987  3.4904
     Green Bay         75.0022  5.4059
     Dallas            74.3262  1.7951
     Kansas City       73.7243  2.5914
     Philadelphia      73.2637  1.4419
     Carolina          72.7559 -1.1557
     Baltimore         72.7413  6.4588
     Cincinnati        72.6742  1.1916
     Indianapolis      72.5000  4.1979
     Miami             72.4598 -0.5797
     San Francisco     72.3348  2.2386
     San Diego         71.3343  2.7821
     Pittsburgh        70.6222  4.1690
     Detroit           70.5256  4.9249
     New Orleans       70.1691  3.8972
     Buffalo           70.0647  6.3324
     Arizona           69.6365  6.4944
     Houston           69.4695  2.1489
     NY Giants         69.1636  0.5580
     St Louis          68.4343  4.0783
     Minnesota         68.0873  3.0570
     Atlanta           68.0073  1.7380
     Chicago           66.5634  1.7198
     Cleveland         66.3421  1.1641
     Washington        64.1055 -0.8504
     Tampa Bay         63.8024  1.0641
     NY Jets           63.7191  2.9979
     Oakland           61.6141  1.3194
     Tennessee         61.3249  0.8878
     Jacksonville      59.5649  1.2641


Note: Ratings include games through 1/18/15.
 







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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