The Dr. Edward Kambour NFL Football Ratings

2023 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
     San Francisco     78.9601  2.2355
     Baltimore         78.0279  2.3831
     Buffalo           77.7319  2.2815
     Kansas City       76.7753  0.7401
     Dallas            76.7485  3.9434
     New Orleans       73.6137 -1.1432
     Tampa Bay         73.2028 -1.1296
     Green Bay         71.8355  3.8032
     Cincinnati        71.7021  1.7280
     Miami             71.6803  4.5620
     Detroit           71.4523  3.3562
     LA Rams           71.4219  1.9191
     Minnesota         71.1220 -0.3219
     Philadelphia      70.9251  3.4583
     LA Chargers       70.6307 -0.9062
     Jacksonville      70.3248  0.6916
     Cleveland         69.5995  4.0496
     Pittsburgh        68.1907  4.0585
     Las Vegas         67.9688  4.1008
     Indianapolis      67.7006  1.1383
     Houston           67.4909  2.9565
     Denver            67.3225  2.9784
     New England       67.1742 -0.1531
     Seattle           66.6032  3.2898
     Arizona           66.4106 -1.1412
     Tennessee         66.3295  2.5315
     Chicago           65.6489  4.1861
     Atlanta           65.6318  3.0126
     NY Giants         65.6010  2.1590
     Washington        65.5032 -4.0983
     NY Jets           64.5213  3.7303
     Carolina          62.1483  1.6706


Note: Ratings include game results through 1/28/24
 

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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