The Dr. Edward Kambour NFL Football Ratings

2015 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       80.1000  2.4225
     Seattle           78.4757  5.9991
     Denver            76.9663  3.6055
     Green Bay         74.9480  5.5841
     Dallas            74.3401  1.7991
     Kansas City       73.8961  2.4381
     Philadelphia      73.4243  1.0539
     Carolina          72.8143 -1.1760
     Cincinnati        72.6722  1.1376
     Baltimore         72.6438  6.6373
     San Francisco     72.3588  1.9569
     Miami             72.3510 -0.4548
     Indianapolis      72.3434  4.5307
     San Diego         71.4075  2.7631
     Detroit           70.6920  4.7958
     Pittsburgh        70.6706  3.9696
     New Orleans       70.0616  3.9031
     Buffalo           70.0394  6.4358
     Arizona           69.4761  6.7835
     Houston           69.3670  2.3228
     NY Giants         69.1603  0.4137
     St Louis          68.2683  4.1119
     Minnesota         67.9854  3.1851
     Atlanta           67.9355  1.8513
     Cleveland         66.5296  0.8942
     Chicago           66.3569  1.8942
     Tampa Bay         64.0554  0.6026
     Washington        63.8909 -0.4799
     NY Jets           63.7456  3.0616
     Oakland           61.7096  1.3370
     Tennessee         61.4206  0.6505
     Jacksonville      59.8936  0.9484


Note: Ratings don't include any games from 2015 (yet).
 







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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