The NFL and its fans have never had a wild devotion to statistics the way Baseball fans have.  After watching the Super Bowl this past Sunday and seeing much of the playoffs leading up to the big game, I noticed more announcers use statistics to implore the greatness of a particular player.  The comparison of the Quarterbacks of today to the gunslingers on yesteryear is especially appealing, since the numbers racked up by today’s QBs are so much better than those of the past.  This is primarily due to rule changes made by the NFL over the years making it easier for the passing game to succeed, and not because of increasingly talented players at the Quarterback position.

During the pregame banter leading up to the game, one pundit mentioned how Aaron Rodgers had already had a two passer ratings of more than 100 and Brett Favre had only done it once in his career.  Well, in 1996 when Favre went to his first Superbowl at age 27, he had a completion percentage of 59.9 and a passer rating of 95.8.  The league that year had a completion percentage of 57.6 and passer rating of 76.9  In 2010, Aaron Rodgers won the big game at age 27 with numbers of 65.7 and 101.2.  The league averaged 60.8 and 82.2.  Comparing the individual performance to the league’s, and without doing much more analysis, it seems Rodgers and Favre had essentially the same season.

This pattern of improving QB statistics goes back a long time and has trended upwards over time.  In 1970 the completion percentage for Quarterbacks averaged 51.1 percent and the Passer Rating was 65.6, well below today’s numbers and still well below numbers from the 1990s.

In the Business Intelligence and Data Architecture professions, we need to make sure the data we deliver is put in the proper context.  The business world has seen improvements in productivity and new processes over the years and we need to make sure our data models continue to be relevant as things change.