New Predictive Formula for College QBs

If you haven’t read yesterday’s post about 4th and 1 strategy, please do.  It’s important and uses concepts that I’ll be revisiting very soon (looking at other scenarios).

For today, I’m going to give you a taste of what’s featured in the Eagles Almanac.  If you haven’t done-so already, I strongly encourage you to purchase the PDF version (only $10). The paperback is also available now.  Both can be found here.

I made two contributions, one of which is a brief synopsis of the case in favor of starting Nick Foles this year.  My other article covers in-depth what I will discuss briefly today.  Basically, I’ve created a new formula for evaluating the professional potential of college quarterbacks.

You’ll have to read the Almanac for the complete breakdown, but here are the basics.  First, a couple of key charts:

For all QBs who were drafted between 1999 and 2012 and have at least 200 NFL pass attempts:

Screen Shot 2013-08-14 at 10.29.06 AM

The correlation value is -0.289.  While many “analysts” have expounded on the importance of college completion percentage (we’ll look at that next), Wonderlic scores, or games started, very few (if any) have highlighted College Interception Rate as a statistic to use as an indicator of professional success.  I looked at a lot of statistics for this article, and found very few that had the predictive value of Interception Rate.

Now, with the same sample, college completion percentage to NFL Rating:Screen Shot 2013-08-14 at 10.36.35 AM

The correlation value here is 0.35.

As you can imagine, we can use both of those stats (with apparently predictive value) to create a new formula for predicting QB success.  You can see the complete formula in the Almanac, but here are some of the results.  As far as scale goes, all you need to know is that receiving a negative score is bad.  How bad?

Here are the QBs who received a negative score:

Screen Shot 2013-08-14 at 10.55.49 AM

Depending on your definition of a good QB, there are potentially a few “false negatives” (most notably Donovan McNabb).  However, regardless of your definition, there aren’t many.  Conversely, I think it’s safe to say that, if they could do it over again, teams would not use 1st round picks on Kyle Boller, Joey Harrington, Cade McNown, J.P. Losman, Akili Smith, and Brady Quinn.

Similarly, though they’ve had good careers, have Michael Vick and Carson Palmer lived up to #1 pick status?  Maybe, again depending on your personal opinion, but the point is it’s very debatable.

Here is the complete correlation chart for “Formula Score” and NFL Rating:Screen Shot 2013-08-14 at 10.59.39 AM

The correlation value is .363.  That doesn’t sound like much, but given what we’re tackling here (predicting human development), it’s very good.  Additionally, in the Almanac I test this formula against the popular “26-27-60” rule, with encouraging results.

Lastly, I’ll leave you with a few specific points:

– Both Nick Foles and Matt Barkley registered positive scores.

– Robert Griffin the Third had, by far, the highest score in the sample (you can see him in the far top right of the chart above).

– Geno Smith registered the highest score overall (he wasn’t in the sample).

For more detail and specific scores, get the Almanac!  If you do, you’ll also get (among other things):

– An insider’s look at Chip Kelly’s time at Oregon (perhaps the best contribution in the entire Almanac)

– Diagrammed breakdowns of a few key “Oregon Offense” plays.

– A great reflection on the Andy Reid era.

In other words, everything you need to be ready for the start of the Chip Kelly era.


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