Final Thoughts – 2013 Draft

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Published on: April 29, 2013

Now that the draft is over, it’s time to post some final thoughts.

Thought #1: The rest of the league should be terrified of the quarterback situations the Broncos, Falcons, and Saints have put themselves in. Not only do they all have established veterans to run the offense, but they all picked up quality in the late rounds of the draft or free agency that they can develop. Scary.

Thought #2: Sad to hear that Ryan Aplin had some sort of issue passing a physical. I hope this isn’t the end for him because I think he’s incredibly talented.

Thought #3: Good to see Brent Leonard getting some tryouts. Kick the door in when you get that opportunity, man. Here’s to good things happening for you.

Thought #4: This tight end class will be very interesting to watch since my predictions are so far away from everyone else’s. I might have to revisit those predictions at the end of next season.

Thought #5: Oh you poor, sweet Jets fans. Good luck with all that. In all seriousness, good luck. I would be very happy to be wrong in this case. I just don’t think I am.

First Round Thoughts

Only five players that I have a number attached to were drafted in the first round.  This will be short…until we talk Vikings.

Pick #8:  Tavon Austin

The first wide receiver off the board is also my top rated wide receiver, for whatever that’s worth.  Not much to say here other than I think the Rams made the right choice.

Pick #16:  E.J. Manuel

The first quarterback taken in the draft goes to the Bills.  I like the pick in that I think he will do well in the NFL.  Our handy-dandy math equation tells us to expect Manuel to have a 74.79 NFL Passer Rating by the time his rookie contract is over.  I think Bills did well here.  They avoided the Geno Smith hype and got an interesting playmaker.  It will be interesting to see if they transition to a read-option offense or if they use E.J. as a more traditional quarterback.  I think they could do either.

Pick #21:  Tyler Eifert

I am not a fan of Tyler Eifert, at least as far as pass catching ability goes.  I think I might be alone in that opinion, but I’ll follow the numbers.

Pick #27:  DeAndre Hopkins

I have my wide receivers sorted by Completions Away from Average (CAA).  If you look at that list, you see Hopkins somewhere near the middle of the pack.  But I think that number doesn’t capture his true value.  One of the reasons I really hesitated about posting my wide receiver rankings is that the model really loves slot receivers.  This makes sense for two reasons.  First, slot receivers tend to be matched up against the 3rd or 4th best defensive back in the secondary.  They are working against the less talented, relatively speaking, members of the secondary.  Second, slot receivers typically run shorter routes than the guys on the outside do., so the throws are, relatively speaking, easier.  Combine those two together, shorter routes against the lesser talented defensive backs and you get a combination that loves completions.  So if you look down the list, a lot of slot receivers are at the top.

On the flip side, outside, deep receivers are punished by the model.  The passes are longer and more difficult to complete and are against higher quality defenders.  Given that, it is quite good for a receiver like Hopkins to get to #27.

Pick #29:  Cordarrelle Patterson

Here we go.  Now we’re going to get wordy and mildly upset.  I want to open by saying that I’m not upset with the outcome of this pick.  Cordarrelle Patterson may turn out to be an excellent wide receiver.  Or he may not.  Either way, that’s not what I’m upset about.  This isn’t about Cordarrelle Patterson being the “right pick” or not.  This biggest issue Vikings fans like myself should have about this pick is the process that brought it about.

I’ve read this argument in many other places (see Wages of Wins, Sloan Conference, etc. etc. etc) but it bears repeating here.  Sports are very outcome focused.  We talk endlessly about a particular player being the right fit or a can’t miss prospect, but that is the wrong way to think about the draft.  We should not be concerned about outcomes.  We should be concerned about the processes that are generating our outcomes.  Processes are what gets you sustained success.  Outcomes get first downs and touchdowns.  Processes build dynasties.  I don’t like the process that the Vikings used to make the 29th pick because it doesn’t reflect the reality of the draft.

What realities to I mean?  As economist Cade Massey says, the draft is dominated by randomness.  Predicting NFL performance from college data is remarkably difficult.  Even my own model only predicts 11% of the variance in NFL Passer Rating.  That’s very far away from lights out, sure thing picking.

Error Bars for my Quarterback Predictions

And even if you get it right, there is a chance your guy could get injured and all your perfect forecasting could be for not.  Massey goes on to recommend that in an environment dominated by tremendous uncertainty, your best option is to use as many selections as possible.  This is where the Vikings fell down horribly.

The teams that use the draft properly are teams that have accepted the random nature of the draft and act accordingly.  What you absolutely do not do is sacrifice three picks to move back into the first round.  There is absolutely no guarantee that this pick will work out well.  There is no guarantee that any pick will work out well.  It is a massive roll of the dice to give up all those picks and absolutely the wrong process to use in the draft.  The Patriots, our trade partner, are a team that gets the process right.  They accept that the draft is largely random, move back in the draft, add additional picks and wait for some of them to work out well.

That’s my breakdown of the first round.  Here’s hoping the Vikings hit on the selections they have remaining.  I think they gave up far too much to make a third first round pick.

Data Drop: Before the Draft Edition

Categories: NFL Draft, Statistics
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Published on: April 21, 2013

The 2013 NFL Draft is less than a week away.  All the draft analyzers are putting in their final push to get everything in order for the big day.  In the spirit of getting everything in order, I’ve added a load of new data to the site.  Check the Draft Numbers menu for everything.

I’ve added my entire list for wide receivers and pass receiving tight ends.  I’m a bit scared of putting that up, actually.  There was a time when I said I wasn’t going to do that.  I’m scared because I don’t have a good metric for understanding what these numbers predict.  The numbers are an indication of the pass catching talent of that player separate from the quarterback and offensive scheme.  However, I don’t think the numbers will predict receiving yards or touchdowns at the end of a season.  My major project for the summer will be to connect these numbers to something more meaningful and concrete, like I have with quarterbacks.  Until then, we can just look at the nice pretty list.

One note, the tight end data refers to pass catching ability only.  There are many other factors that make a quality tight end other than being able to catch passes, most notably blocking ability.  The tight end list only ranks tight ends on the single measure of pass catching.

Tweaking the Model

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Published on: April 14, 2013

Apologies for the unexpected hiatus last week.  I had to fly to Seattle for a wedding.  But now I’m back in the Frozen Tundra and ready to tell you about some stats.

I’ve been adjusting the statistical methodology I use in the last couple weeks.  Nothing has changed about how I calculate the Completions Away from Average metric, but I have been changing how I use that metric to predict NFL success.  Be advised that this is another post that is heavy on technical details.  If you are not a technical reader, the take-home point of this post is that the changes will make my predictions better in the long term, but add more uncertainty in the short-term.

Change #1:  Dependent Variable

The DV I’ve been using for predicting is NFL Passer Rating after three years in the league.  I chose three years arbitrarily.  I thought, “Well…most draft prospects have a decent chance of playing after three years in the league, and we don’t want to get too far away in time from college because we can’t account for growth and coaching, so let’s use three years.”  Arbitrarily isn’t the best reason to choose a DV, so let’s use something with a little more meaning.  I’m going to stick with Passer Rating as it’s a reasonably good per-attempt statistic.  The per-attempt part is what I really care about.  Yes, there are problems with it, but I think the good outweighs the bad.

But choosing three years as an arbitrary time-period doesn’t make much sense.  We don’t have to change this much to make it meaningful, but we should change it.  Rather than Passer Rating after three years in the league, I went with Passer Rating after four years in the league.  Four years is much more meaningful because it is the length of all rookie contracts under the new CBA.  Thus, I’m predicting what a player is most likely to do during his rookie contract.  I think that is much more meaningful than what I was doing previously.

Change #2:  Prediction Model

Changing the prediction model solves two problems, which is handy.  When I have two problems, I like being able to solve them both at the same time.  So what are the two problems that need solving?

Problem #1:  Adding Data

The typical method of designing a statistical model is you have some theory, you collect data in a way to test that theory, and you use the data you collected to create a mathematical formula that minimizes the errors between your theory and the data.  In most cases, my statistical model included, the mathematical formula is fairly simple.  We draw a scatterplot of our data, and draw a straight line through the cloud of data points.  Then all we need is our 8th grade math skills to solve for the important values of that line, y = mx + b, where m is the slope of the line and b is where the line crosses the vertical axis (a.k.a. intercept).  We also know x, in this case Career CAA, for each individual player, so we just solve for y, in this case Passer Rating after four years in the league, and we have our prediction.  This process is called regression modeling.

But then what?  New players will always be entering the NFL.  Also, some of the players that I have in the model haven’t been in the league for four years, so their data will change.  But that’s not a problem, right?  We just keep collecting the data and everything should get even better, right?

What I’m about to say might surprise some of you that are not familiar with traditional methods of statistical modeling.  Continually adding data and using traditional regression techniques will actually make your predictions worse.  Regression models hate adding new data.  They want to take the information you give them the first time and do the best they can with it.  When you take the same model, feed in the old data plus some new data, the model is more likely to follow blind alleys and tell you that unimportant things might be useful predictors.  So the realities of the NFL make traditional modeling techniques less useful.

Problem #2:  Distribution of the DV

Traditional regression assumes a normally distributed dependent variable.  For most things, this is fine.  For Passer Rating this is not fine as passer rating isn’t normally distributed.  In a normal distribution, most cases are in the middle with a few cases on the high and low ends.  However, a per-attempt statistic like passer rating isn’t normally distributed because some quarterbacks won’t have many attempts.  For example, let’s look at Ryan Mallet this year.  He had 4 attempts this season, one of which was intercepted.  Now, the passer rating metric just takes what it has and extrapolates from that information.  The number that is Ryan Mallet’s 2012 passer rating assumes he would throw an interception every 4 attempts, which we humans know is not true.  If he was on the field and made 100 attempts, he wouldn’t expect him to throw 25 interceptions.  To our brain, that’s ridiculous.  But the math equation doesn’t know this.  It’s just taking what we gave it and spitting out a result.  So Ryan Mallett’s passer rating is currently 5.  Some players, like Mallett, have really poor numbers.  Other players, like Kirk Cousins, have really good numbers. We shouldn’t believe either number just yet because they haven’t had enough attempts to get a good picture of what they can do when they are on the field.  This is the nature of the beast in the NFL; some quarterbacks have passer ratings that are extreme compared to others.  But regression models don’t like extreme values.  Regression models like everything to be nice and normal.  Extreme values confuse the model and exert undue influence on the final result.

Solution:  Bayesian Robust Linear Regression

I won’t say too much about the Bayesian portion of this.  When I start talking Bayesian methods in class, my students tend to glaze over like fully fed zombies.  Baaaaaayes.  Regardless, the Bayesian portion solves Problem #1.  Bayesian analyses are built to accept new data in ways that traditional regression models are not.

The Robust Linear Regression part is the really cool part.  In this analysis, we can change the assumption about the distribution of our DV.  We can, for example, assume our values are distributed as a t-distribution, which means most of the values are in the middle, but extreme values are expected and dealt with easily.  So, let’s revise the predictions with this analysis.

New Model

We will set some very general prior predictions before we run the analysis.  We will assume that our DV is distributed as a t-distribution.  We will also assume that possible intercepts and slopes are distributed normally.  The analysis will calculate which degrees of freedom by estimating a value called tau.  Tau is conceptually, but not quite the same as degrees of freedom.  It’s a measure of how fat the tails are.  The closer to 0 this is, the better this analysis will do compared to traditional techniques.  If tau is > 30, we shouldn’t see much difference.  We assume possible values of tau follow a gamma distribution beginning at 1.

So what do we find?  Note, we’re using 4-year NFL passer rating as our DV rather than 3-year as in past analyses.


The first question is if we made a wise choice in assuming our dependent variable is t-distributed rather than normally distributed.  Our most credible estimate of tau from these data is 2.56.  Remember, the closer to 30 this is, the less this matters.  Since the most credible estimate is much, much less than 30, we should improve our predictions substantially with this procedure.

Equation of the Line

We’re still trying to find a line that best predicts our outcomes.  Bayesian regression is a little different in that it gives us lots of different lines that are all credible given the data we have.

In the initial estimates, we have a small sadness.  0 remains a credible slope.  This is what I meant above about increasing uncertainty in the short term.  There is a chance that everything I’ve been talking about is not true and this CAA metric isn’t worth anything.  The nice thing about Bayesian analyses is that they put a probability on this chance.  Given the data we have, there is about a 6% chance that I’ve been blowing smoke at you this entire time.  I’m willing to keep going with that, but you can make your own decisions regarding that.

To create the actual equation, I took the most credible intercept and the most credible slope and stuck them in the same equation.  Note that I’m still new to Bayesian estimation techniques and I’m not quite sure if this is kosher or not.  If I find that it’s not, I will revise the model.

New Equation for Prediction

4-Year NFL Passer Rating = 70.5 + 0.143 * (Career NCAA CAA)

I’ve added a new column to my predictions for the 2013 draft class.  Note that this doesn’t change their relative rankings any, just the estimate of their NFL passer rating four years from now.

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