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
     Seattle           79.1937  7.0139
     San Francisco     78.5378  3.4820
     New England       78.4805  0.5818
     Carolina          77.4328 -0.9915
     Denver            77.0696  3.2637
     New Orleans       74.9855  6.2631
     Cincinnati        74.7741  1.9451
     San Diego         72.1898  1.8987
     Green Bay         71.4870  3.1086
     Philadelphia      71.1447  0.4924
     Miami             71.0031 -1.3097
     Kansas City       70.6186  1.7853
     Dallas            70.2323  3.3640
     Pittsburgh        70.2190  3.6369
     Indianapolis      70.0983  2.6178
     Chicago           70.0032  2.8976
     Arizona           69.9075  6.9872
     Atlanta           69.3743  2.2483
     NY Giants         69.3066  0.5019
     Baltimore         69.2607  6.4401
     Detroit           69.1549  3.9555
     St Louis          68.2160  4.1887
     Tampa Bay         66.9683  1.9182
     Tennessee         66.9299  2.0273
     Minnesota         66.7500  3.8408
     Washington        65.6798  0.6008
     Cleveland         64.8340  1.4595
     Houston           64.8199  2.4932
     NY Jets           64.7222  2.9594
     Buffalo           64.3636  7.7819
     Oakland           63.4608 -0.1694
     Jacksonville      58.7814  0.1631



Note: Ratings don't include 2014 season games.
 







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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