The Dr. Edward Kambour NFL Football Ratings

2016 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       80.9290  1.2928
     Kansas City       76.2855  1.1723
     Atlanta           75.1261  2.2336
     Pittsburgh        74.5805  4.1128
     Dallas            74.3535  0.5703
     Seattle           74.1088  5.3467
     Denver            73.8828  3.0067
     Cincinnati        73.6194  1.4180
     Arizona           73.1205  4.7588
     Green Bay         72.3676  5.7692
     Philadelphia      72.2450  2.2234
     Carolina          71.3271  1.2528
     Washington        70.9363  0.2496
     Minnesota         70.5580  3.2331
     New Orleans       70.5223  2.8367
     Buffalo           70.4936  4.3812
     Oakland           70.3263  0.7566
     NY Giants         70.1505  2.1018
     Indianapolis      69.4693  1.4184
     Miami             69.3524 -0.4918
     Tampa Bay         69.2546 -0.4580
     Baltimore         69.0874  5.9888
     San Diego         68.9298  2.0679
     Detroit           67.5025  3.8729
     Houston           67.4484  3.4454
     Tennessee         66.4328  0.9693
     NY Jets           64.4109  3.7411
     Chicago           64.1095  1.4191
     Jacksonville      63.6989  2.6490
     Los Angeles       62.5991  2.6306
     Cleveland         61.4719  0.7608
     San Francisco     61.2995  4.3360


Note: Ratings include games through January 15, 2017.
 







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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