The Dr. Edward Kambour NFL Football Ratings

2009 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 
     Indianapolis      78.2657   0.4853
     NY Jets           77.1001   0.1459
     San Diego         76.8873  -0.1457
     New England       76.7684   5.1359
     New Orleans       76.7347   4.8951
     Green Bay         75.9501   3.1715
     Atlanta           75.8558   0.0554
     Baltimore         75.6670   8.2711
     Philadelphia      74.5559   2.7459
     Minnesota         73.6988  10.6807
     Carolina          73.5111   0.9421
     Pittsburgh        72.6677   2.5060
     NY Giants         72.3450   0.7895
     Dallas            71.6959  10.0401
     Miami             71.6283   0.1811
     San Francisco     71.1924   1.6086
     Houston           70.7889   3.4788
     Denver            70.3481  -1.6529
     Arizona           70.0617   1.4775
     Cincinnati        69.3815   2.9716
     Washington        68.9593  -5.2999
     Tennessee         68.5177   4.8390
     Chicago           67.1368   3.9025
     Buffalo           65.8805   4.4424
     Tampa Bay         65.8054  -1.0790
     Jacksonville      63.9778   3.9615
     Kansas City       63.8902  -3.8028
     Oakland           61.9491  -2.0159
     Cleveland         60.9058   4.1809
     Seattle           59.2791   9.3906
     Detroit           59.0019  -1.2018
     St Louis          52.6387   3.4347


Note: Ratings include games up to 1/25/10.
 

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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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