This question came up when I was looking up the last year in the playoffs for seven probable NFC playoff teams. Both New Orleans and Philadelphia last played in the playoffs four years ago, in 2013. And then the thought came up in my head, “But Drew Brees is a veteran QB.” This seems intuitive, but wanting to actually create such a definition and then later to test this using a logistic regression, there is the rub.

There are any number of QBs a fan can point to and see that the QB mattered. Roger Staubach seemed a veteran in this context back in the 1970s, Joe Montana in the 1980s, Ben Roethlisberger in the 21st century, Eli Manning in 2011, and Aaron Rogers last year. But plenty of questions abound. If a veteran QB is an independent variable whose presence or absence changes the odds of winning a playoff game, what tools do we use to define such a person? What tools would we use to eliminate entanglement, in this case between the team’s overall offensive strength and the QB himself?

The difference between a good metric and a bad metric can be seen when looking at the effect of the running game on winning. The correlation between rushing yards per carry and winning is pretty small. The correlation between run success rate and winning are larger. In short, being able to reliably make it on 3rd and 1 contributes more to success than running 5 yards a carry as opposed to 4.

At this point I’m just discussing the idea. With a definition in mind, we can do one independent variable logistic regression tests. Then with a big enough data set – 15 years of playoff data should be enough, we can start testing three independent variable logistic models (QB + SOS + PPX).

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