This project is a continuation on a recent fascination of mine: Quantitatively evaluating quarterbacks based on NCAA statistics, Wonderlic scores, and their offensive scheme in college. I am far from any expert on quarterback play, but I do enjoy the seemingly impossible challenge of accurately predicting NFL stars. Effective production from the quarterback is the biggest disparity between winning and losing teams in the NFL. Value is based on what you need the most, and competent quarterbacks are in the shortest supply.

I chose to explore quarterback data through a method called recursive partitioning. I collected statistics from the final full year (>85% schedule played) of a QB’s collegiate career, Wonderlic scores from the NFL Combine, and partitioned QBs out based on their average Approximate Value (see pro-football-reference.com) per 16 games started in the NFL. My sample set is QBs drafted between 2000 and 2010, who have collegiate statistics, a Wonderlic score, and made an NFL roster.

Through recursive partitioning, QBs were separated into four distinct cohorts or sub-populations.

These four sub-populations were classified based on three main parameters:

1) A Passer Rating above or below 147.1.

If Passer Rating is above 147.1:

2) A Wonderlic Score above or below 23.5

If Passer Rating is below 147.1:

3) An Adjusted Yards per Pass Attempt above or below 7.35 yards.

Based on these criteria, it is plausible that Passer Ratings above a certain threshold are indicative of better play in the NFL, given that the Passer Rating is the product of better decision making (i.e. More intelligence, better Wonderlic score), and not because of their offensive scheme in college (i.e. Highly efficient offense, more yards per passing attempt).

Distributions and summary statistics of these cohorts are shown in the figures below.

It is interesting to note that differences in mean Approximate Values (AV) are most likely influenced by the number of QBs who never accumulate any Career AV (aka never play). You can see in the boxplots above that the maximum approximate values are fairly similar between each group, but the lower percentiles are drastically different.

Any questions, comments, advice, or criticism can be directed to my email: casanscott@gmail.com. Thanks for checking out my work!