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       81.1179  1.7474
     Kansas City       76.3375  1.1107
     Atlanta           75.5937  2.4176
     Seattle           74.2487  5.1803
     Dallas            74.1333  0.8366
     Pittsburgh        74.0383  4.5488
     Denver            73.8598  3.0746
     Cincinnati        73.5821  1.5364
     Arizona           73.0661  4.9405
     Philadelphia      72.0626  2.3737
     Green Bay         71.7294  5.9606
     Carolina          71.3655  1.3185
     Washington        71.1481 -0.2380
     New Orleans       70.7375  2.5428
     Minnesota         70.5441  3.1134
     Buffalo           70.5427  4.3246
     NY Giants         70.2114  1.8901
     Oakland           70.1807  1.0843
     Indianapolis      69.4939  1.4136
     Tampa Bay         69.4493 -0.7551
     Baltimore         69.3004  5.5074
     Miami             69.2963 -0.3741
     San Diego         68.8997  2.1192
     Houston           67.5770  3.3611
     Detroit           67.5035  3.7350
     Tennessee         66.5764  0.5872
     NY Jets           64.3387  3.8774
     Chicago           64.1608  1.1876
     Jacksonville      63.5138  2.8963
     Los Angeles       62.7041  2.5701
     Cleveland         61.3639  0.7496
     San Francisco     61.3227  4.5044


Note: Ratings include games through January 22, 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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