Frank DuPont

Draft Analysis

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Value Based Drafting: Part II

Friday, June 15, 2012


The following article is a guest post by Frank DuPont, author of Game Plan: A Radical Approach to Decision Making in the NFL. You can also follow Frank on Twitter.


Earlier in the week we started to look at Value Based Drafting by covering some basics about what VBD is and how you can create a baseline that will allow you to compare players at different positions.  If you haven't read that piece, it's probably pretty important that you go read it now before going any further in this article.  In this article I'm going to expand into using VBD, which also requires a pretty healthy discussion of forecasting.

 

First though, some readers emailed me to ask how I accounted for the Flex position in coming up with the baselines.  I've actually updated the original post to explain, but it's worth going over again.  Forgetting about Flex for a minute, the baselines for RB and WR end up being RB35 and WR29.  That gets both positions to 408 games.  You can tell from those numbers that it takes more players to fill out the RB slots because RBs are less reliable in terms of how many games they play.  Then when you add in the Flex requirement, it ends up being filled mostly with WRs, but you go to WR44 and RB44 to fill out the Flex position.  It's pure coincidence that it happens to be the same number for both positions.  But RB44 and WR44 end up being the baseline for those positions when Flex is accounted for because it takes all of those players to fill out the 408 games for each position, along with the 204 games for the Flex.

 

A Word About Predicting the Future


After you've established your baseline, you have to come up with some projections in order to rank your players.  This is at least as important as coming up with the baseline.  Ok, it's actually probably a lot more important than coming up with the baseline so I'm going to spend a little time talking about forecasting.

 

There are three important things that we should never forget when we're talking about projections.  First, we're essentially trying to predict the future.  That's an inherently difficult task.  Don't be tricked into thinking that the recent past could have easily been predicted just because we can look back on it today and talk ourselves into believing that it was obvious all along.  Prediction is a difficult game. 

 

The second thing to keep in mind is that we should plan for reversion to the mean.  Remember Peyton Manning's 2004 season when he threw for 49 touchdowns?  He threw 28 the next year.  That's really close to being half of the 2004 total.  Remember Chris Johnson's 2000 yard season in 2009?  He ran for 1300 yards the next year.  After Randy Moss' 23 touchdown year he caught 11 the next year.  The point is, reversion to the mean happens and a lot of the time we won't be able to see the causes of the mean reversion ahead of time.  This might sound obvious, but we're going to be faced with a similar problem this year when we look at the draft board and see about 5 QBs coming off of what were all essentially record breaking years.

 

The last thing to keep in mind regarding projections is that oftentimes the biggest fantasy seasons from the year before were really just guys who managed to stay healthy all year.  If we take the guys who managed to stay healthy all of last year and pencil them in for a few missed games this year, and then take the guys who missed a few games last year and pencil them in for a little better luck in the injury department, those small changes will have a pretty dramatic impact on our projections.  Here's a hypothetical that might make this more concrete for you.  Imagine if Tom Brady had lost Rob Gronkowski for a few games last year and then imagine that Wes Welker had played most of the year at 70% health.  Now imagine that Brady missed a game due to injury and then played through a few others with that same injury.  What would his numbers have looked like?  What I've just described is pretty close to what actually did happen to Tony Romo last year.  The details might have been a little off, but I'm sure you can see that luck (or randomness) had a pretty big impact on the difference between Brady and Romo last year.  When we create our projections we have to acknowledge that luck and randomness play a part in football.

 

Similarity Based Forecasts


One of my favorite ways to project players is by using a similarity method.  I take a player like Cam Newton, look at players who had seasons similar to Newton's 2011 campaign (Daunte Culpepper for instance), and then just look at what those players did the following year.  This is a method that is similar to the way that Pandora chooses music, except that instead of looking for attributes like vocals and percussion that songs might have in common, I'm looking for things like yards per attempt, touchdowns, and rushing yards that can be used to demonstrate QB similarity.  I also compare players on things like height, weight and age.  A similarity based comparison does one critical thing in creating forecasts and that is account for some reversion to the mean.  But because we can actually see players who were similar to the players we're studying, similarity comparisons have a powerful psychological effect as well.  They make the idea of mean reversion real because we can see actual examples.

 

These things are always easier to visualize, so let's do that.  Below is a table that contains a list of receivers who had similar seasons to Jordy Nelson's 2011 season.

 

PlayerYearTmAgeWeightHeightY/GTD/GYPR
Jordy Nelson 2011 GB 26 215 75 78.94 0.94 18.57
-------------------------                
PATRICK JEFFERS 1999 CAR 26 217 75 83.23 0.92 17.17
TERRELL OWENS 1998 SF 25 226 75 68.56 0.88 16.37
RANDY MOSS 2000 MIN 23 215 76 89.81 0.94 18.66
DWAYNE BOWE 2010 KC 26 221 74 77.47 1.00 16.14
BRAYLON EDWARDS 2007 CLE 24 211 75 80.56 1.00 16.11
MICHAEL WESTBROOK 1999 WAS 27 220 75 74.44 0.56 18.32
RANDY MOSS 1998 MIN 21 215 76 82.06 1.06 19.03
MICHAEL JACKSON 1996 BAL 27 195 76 80.07 0.93 15.80
KENNY BRITT 2010 TEN 22 215 75 77.50 0.90 18.45
HERMAN MOORE 1994 DET 25 210 76 73.31 0.69 16.29
JAVON WALKER 2004 GB 26 220 75 86.38 0.75 15.53
ROD SMITH 1997 DEN 27 200 73 73.75 0.75 16.86
MILES AUSTIN 2009 DAL 25 215 75 88.00 0.73 16.30
ERIC MOULDS 1998 BUF 25 210 74 85.50 0.56 20.42
JAKE REED 1995 MIN 28 216 75 72.94 0.56 16.21
REGGIE WAYNE 2004 IND 26 198 73 75.63 0.75 15.71
JAKE REED 1996 MIN 29 216 75 82.50 0.44 18.33
ANDRE RISON 1993 ATL 26 188 73 77.63 0.94 14.44
JERRY RICE 1991 SF 29 200 74 75.38 0.88 15.08
DERRICK ALEXANDER 1996 BAL 25 195 74 73.27 0.60 17.73
CARL PICKENS 1994 CIN 24 206 74 75.13 0.73 15.87
Averages     25.5 210.0 74.7 78.7 0.8 16.9

 

You can see that even while some of these players might differ from Nelson in one area or another, as a composite, they are very close to Nelson.  Then I look at what these players did in the year after they were similar to Nelson.  Those results are shown in the following table (with some names missing for guys who didn't record any stats in the following year).

 


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