2014 NFL Draft Predictions: Quarterbacks

Categories: NCAA FBS, NFL Draft, Statistics
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Published on: February 12, 2014

Welcome back everyone.  I took a couple weeks off to recharge and enjoy the Super Bowl.  Now it’s time to get to the heart of the matter.

This post marks my predictions for the 2014 NFL Draft Quarterback class.  On the Draft Numbers page you will see predictions for both NFL Passer Rating and ANY/A for every draft eligible prospect.  This class is a very interesting one.  It’s very similar to the 2011 class in its number of potential starters, one.  Unlike the 2011 class, though, the one player that has the potential to start in the NFL isn’t getting very much buzz, is unlikely to be drafted highly, and probably won’t be a first year starter.  That player is Keith Price from Washington.  Many of the other potential prospects will get playing time, some have potential to be career backups in the league, but this class will be very short on quality starters.  All the numbers are available here.

Prediction Model Details

These predictions are generated using Bayesian analysis procedures.  If you would like details on the priors, you can ask in the comments.  The data set used to generate the equation includes all quarterbacks that played FBS football for at least one season from 2007-2012 and threw at least one pass in the NFL during the 2008-2013 seasons.

First off, the analysis finds that Career Completions Away from Average effectively predicts both 4-year passer rating and 4-year ANY/A.

When we make predictions like this, it’s important to evaluate the model to see how precise it is.  When I tell you that Aaron Murray is predicted to have a Passer Rating of 77.5 after four years in the NFL, how much uncertainty is there in that prediction?  Below you will see a plot showing how much we can reasonably expect the predictions to be off.

The plot gives you an indication of how much the predictions based on CAA can be expected to be off.  The circles represent one quarterback in the data set that was used to generate the prediction model.  The vertical blue lines represent the region that is 95% likely that the prediction will fall into.  You might look at that plot and rightly say that there is a lot of uncertainty in these predictions.  And you would be right.  There is a lot that isn’t accounted for by this single number.  However, let’s make a comparison.  One of the best ways to gauge the general league’s opinion of a prospect’s chances of being successful is using relative draft rank broken down by position.  In other words, was this quarterback the 1st, 2nd, 3rd, etc. quarterback selected.  The plot below uses the same data set that was used to generate the CAA plot, but uses positional draft rank to generate the regions.

The direction of the effect is reversed, so it might be difficult to see, but the length of those blue lines is almost exactly the same as with CAA.  Which I like to see.  It tells me I’m on the right track with this thing.  For completeness sake, here is the same plot predicting ANY/A.

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