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       79.5841  2.6419
     Kansas City       78.0285  1.4090
     Seattle           77.9817  3.4042
     Denver            77.2973  2.7359
     Carolina          76.5817  1.7797
     Cincinnati        76.1968  0.6945
     Arizona           75.3608  5.1881
     Pittsburgh        73.8321  4.3145
     Minnesota         73.7219  3.1985
     Philadelphia      73.7063  0.6132
     Green Bay         73.4517  5.0953
     Buffalo           70.4482  4.9156
     San Diego         69.5059  1.8482
     Houston           69.3251  2.2359
     Miami             69.2911 -0.5837
     NY Jets           69.2562  3.6522
     Dallas            68.6696  0.7634
     Baltimore         68.6464  4.3791
     Detroit           68.4625  4.5434
     NY Giants         67.8633  1.3191
     Atlanta           67.4882  2.5054
     Oakland           66.9111  0.6671
     Los Angeles       66.8301  4.0826
     San Francisco     66.7986  4.2805
     Chicago           66.4156 -0.1072
     Washington        66.3643  0.6884
     Indianapolis      66.2585  3.8799
     New Orleans       64.9137  3.6845
     Tampa Bay         64.1007 -1.1170
     Cleveland         63.3375  1.0727
     Tennessee         62.1488 -0.2521
     Jacksonville      61.2216  3.2504


Note: Ratings include games through Sept 26, 2016.
 







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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