Player Profile: David Fales

Categories: NCAA FBS, NFL Draft, Statistics
Comments: No Comments
Published on: December 18, 2013

Player:  David Fales

School:  San Jose State

Year:  Senior

Career CAA:  29.2

Predicted 4-Year NFL Passer Rating:  74.7

Predicted 4-Year ANY/A:  4.76*

Finally, a player with some actual buzz.  David Fales is a very interesting prospect to me, mostly because he stretches the bounds of what my prediction model can say about a prospect.  Of all the quarterback prospects in this year’s draft, I think Fales is one the model will be the most wrong about.  The thing is, I don’t know in which direction the model will be wrong, which makes Fales even more interesting to watch.  You could tell me that, four years from now David Fales will have a career passer rating of 80 and take a team to the playoffs two years running and I’d believe you.  Or you could tell me that David Fales won’t start a game in the NFL and I’d believe that too.

So why so much uncertainty around David Fales?  A number of reasons come to mind.  First there are the standard problems of not playing a full career at the FBS level.  I’ve only got two years of data on Fales when I have three or four on many other quarterbacks.  But there is an extra problem with the situation that surrounds David Fales that makes his future outcomes even more difficult to predict.  Nothing breaks my prediction model faster than constant changes to the offensive system.  Changing receivers is good, changing quarterbacks in the same offensive system is good, but as soon as the offensive system starts changing rapidly, the numbers go haywire.  And San Jose State has seen its fair share of change in the offensive system during the tenure of David Fales.

During Fales’s first year (2012), a new offensive coordinator was brought in.  This means that most of what we knew about San Jose State’s offense can be thrown out the window.  Not everything of course, because the head coach was still the same.  We can imagine that Mike MacIntyre had a particular philosophy in mind when hiring Brian Lindgren that wouldn’t be much of a change from previous offensive systems.  But we don’t know much about whether this new guy has a system works the same way, features the same personnel, or uses the personnel they have in the same way as the previous system.  This problem gets compounded when the head coach lands a new job for 2013 and takes the offensive coordinator with him.  Now we not only have a new head coach but another new offensive coordinator, which really throws everything out the window.

You can see this in the large variability in Fales’s numbers from 2012 to 2013.  During the 2012 season, Fales is third in the country on my metric (35.6 CAA).  In 2013, he’s 126th (-6.4) and below average (0 is perfectly average).  Part of the reason for this massive change is that the model is trying to account for circumstances like offensive system.  In this particular case it’s having a really difficult time calculating a precise number to put on those circumstances.

At the end of the day, it would be really nice if Fales had another year of eligibility so we could get additional information about his on field performance.  However, NCAA rules being what they are, we just aren’t going to get that.  Without more data, the prediction model can only throw up its hands and say “Might be great, might never work out.  Can’t tell you which one based on what I’ve got.”  It’s possible the 2012 numbers are the true David Fales, or it’s possible the 2013 numbers are the true David Fales, or it’s possible that neither is the true David Fales.  Everywhere we look with this guy, the future is cloudy.

Take Home Point

If I was working for a team, this is one I’d turn the Fales tape over to the highest paid scout they have and say “You deal with this one.  My skills are pretty useless here.”

Do you draft him?

Ask a scout.

* I’ve added a prediction of Adjusted Net Yards Per Attempt (ANY/A) for the advanced stats buffs out there.  Career CAA predicts ANY/A after 4 years in the league according to the following equation Y = 4.27 + 0.0167*Career CAA.  The equation was generated using Bayesian analysis assuming a t-distribution for the dependent variable.

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