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       78.3979  0.5187
     Carolina          78.1467 -0.6968
     Seattle           78.0469  7.3942
     San Francisco     77.4251  2.8407
     Cincinnati        76.6665  1.8975
     Denver            76.2677  3.1214
     San Diego         73.6594  2.2613
     New Orleans       73.3488  6.5255
     Philadelphia      71.5747  0.4683
     Miami             71.3214 -0.3377
     Dallas            71.2953  2.7550
     Chicago           71.2105  2.3301
     Green Bay         70.6450  3.1944
     Arizona           70.4291  6.8316
     Baltimore         70.2156  6.5984
     Indianapolis      69.9751  2.4262
     Detroit           69.2860  4.6729
     Atlanta           69.0986  2.4155
     Kansas City       68.8846  1.1103
     Minnesota         68.6574  2.7439
     Pittsburgh        68.2301  3.8502
     Tennessee         67.7078  1.2084
     Buffalo           67.4455  8.1533
     Houston           67.2070  2.5750
     Tampa Bay         66.6625  1.8619
     NY Giants         66.6395  0.4598
     Washington        66.3886  1.4558
     Cleveland         66.1591  1.6578
     St Louis          65.4551  3.1177
     NY Jets           65.1575  2.8149
     Oakland           62.2087 -0.5435
     Jacksonville      56.1867  0.6358



Note: Ratings don't includes games through 9/15/14.
 







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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