Take Your Pick

Eric Hardter

We’re all familiar with the off-season phenomenon known simply as Rookie Fever.  Every year the newest crop of freshmen footballers becomes an assembly of demi-celebrities, instantly taking over as some of fantasy’s most desirable assets.  Regardless of whether or not these professional neophytes will actually pan out, our collective dynasty impulse is to anoint them as our saviors, deifying them before they’ve even set a single foot on the field.

As the totality of player values essentially represents a zero-sum game, the rise of the rookie must therefore occur concurrently with devaluation elsewhere in the dynasty landscape.  One prominent theory is that last year’s rookies, now sophomores, could be the ones set up for a fall.  Aligning with the standard “out of sight, out of mind” philosophy, if a player didn’t show enough during his first year in the league, he quickly becomes yesterday’s news.

In order to test this hypothesis I assembled ten of DLF’s finest, provided them with a truncated list of 2013 rookies and had them each answer the following question – what’s the highest 2014 rookie pick you’d give up for each player?  The answers were aggregated, averages were obtained, and these values were then compared to the average data obtained from 209 2013 rookie-only drafts courtesy of MyFantasyLeague.com.  Ultimately, the combination of the two data sets should provide a relative glimpse as to how these players are valuated now compared to a year ago.

In order to obtain a uniform set of figures, the following specifications were enacted for the study:

  • The data sets employed PPR scoring.
  • The data sets were based on a 12-team league.
  • The data sets only considered draft picks through the first three rounds (picks 1.01 – 3.12, or 1 – 36).  Players not selected within that range were designated N/A.

Continuing with the disclaimers, I understand this is an imperfect practice.  If these respective sets of rookie picks were viewed as currency, you might as well be comparing dollars to pesos – put simply, no two draft classes are alike.  However, this study is designed to provide a snapshot of relative valuations, and a comparison of draft picks is the most direct way to do so.

Additional consideration was given to comparing the average draft position (ADP) data between the two years.  However, I believe the ADP data is derived from equal parts accumulating talent and roster construction – in other words, during the mock drafts attempts are made to fill out a team and not just pick the best players.  As such, this somewhat muddies the valuation waters and could potentially influence the data.

Data

[am4show  have=’p2;p3;p4;p5;p6;’  guest_error=’sub_message’  user_error=’sub_message’ ]

With that preamble in hand, let’s jump to the data.  The table below illuminates the any disparity between the two data sets, and is broken down by position and ordered by descending 2013 MFL ranks:

Name

2013 MFL Rank

Average 2014 Pick (DLF)

Difference

EJ Manuel

2.05

3.08

-15

Geno Smith

2.07

3.09

-14

Matt Barkley

3.07

N/A

-6

Mike Glennon

N/A

3.12

+1

Montee Ball

1.02

1.05

-3

Eddie Lacy

1.03

1.03

0

Le’Veon Bell

1.04

1.05

-1

Gio Bernard

1.05

1.02

+3

Marcus Lattimore

1.09

2.04

-7

Johnathan Franklin

1.10

3.08

-22

Zac Stacy

2.02

1.07

+7

Christine Michael

2.03

1.11

+4

Joseph Randle

2.08

3.12

-16

Andre Ellington

3.03

1.09

+18

Knile Davis

3.06

3.02

+4

Stepfan Taylor

3.08

3.07

+1

Latavius Murray

3.10

3.04

+6

Khiry Robinson

N/A

2.10

+15

CJ Anderson

N/A

3.04

+9

Tavon Austin

1.01

1.11

-10

DeAndre Hopkins

1.06

1.06

0

Cordarrelle Patterson

1.07

1.03

+4

Keenan Allen

1.11

1.03

+8

Justin Hunter

1.12

1.06

+6

Robert Woods

2.01

2.07

-6

Markus Wheaton

2.04

2.03

+1

Aaron Dobson

2.06

2.05

+1

Terrance Williams

2.11

1.09

+14

Quinton Patton

3.01

3.06

-5

Stedman Bailey

3.02

3.02

0

Da’Rick Rogers

3.04

3.03

+1

Kenbrell Thompkins

3.09

3.09

0

Kenny Stills

N/A

2.05

+20

Marquess Wilson

N/A

3.02

+11

Marquise Goodwin

N/A

3.09

+5

Tavarres King

N/A

3.09

+4

Marlon Brown

N/A

3.10

+3

Josh Boyce

N/A

3.12

+1

Aaron Mellette

N/A

N/A

0

Tyler Eifert

1.08

1.12

-4

Zach Ertz

2.09

2.01

+8

Travis Kelce

2.10

2.09

+1

Gavin Escobar

3.11

3.09

+2

Jordan Reed

N/A

1.09

+28

Ryan Griffin

N/A

3.05

+8

Joseph Fauria

N/A

3.07

+6

Luke Willson

N/A

3.07

+6

Vance McDonald

N/A

3.08

+5

Mychal Rivera

N/A

3.10

+3

Tim Wright

N/A

3.11

+2

Levine Toilolo

N/A

3.11

+2

Itemized in a different fashion, here’s a table highlighting any movement of greater than ±6 slots in a descending manner:

Name

2013 MFL Rank

Average 2014 Pick

Difference

Jordan Reed

N/A

1.09

+28

Kenny Stills

N/A

2.05

+20

Andre Ellington

3.03

1.09

+18

Khiry Robinson

N/A

2.10

+15

Terrance Williams

2.11

1.09

+14

Marquess Wilson

N/A

3.02

+11

CJ Anderson

N/A

3.04

+9

Keenan Allen

1.11

1.03

+8

Ryan Griffin

N/A

3.05

+8

Zach Ertz

2.09

2.01

+8

Zac Stacy

2.02

1.07

+7

Latavius Murray

3.10

3.04

+6

Justin Hunter

1.12

1.06

+6

Luke Willson

N/A

3.07

+6

Joseph Fauria

N/A

3.07

+6

Matt Barkley

3.07

N/A

-6

Robert Woods

2.01

2.07

-6

Marcus Lattimore

1.09

2.04

-7

Tavon Austin

1.01

1.11

-10

Geno Smith

2.07

3.09

-14

EJ Manuel

2.05

3.08

-15

Joseph Randle

2.08

3.12

-16

Johnathan Franklin

1.10

3.08

-22

Finally, below is a breakdown of the positional variance showing the percent of players whose values depreciated, as well as the average movement amongst all players within each positional grouping:

Position

% Depreciation

Average Movement

QB

75.0

-8.5

RB

33.3

+1.2

WR

15.0

+2.9

TE

8.3

+5.6

All

23.5

+2.1

Discussion

When I set this study in motion, I firmly believed there would be a distinct and substantial devaluation of sophomores when comparing this year to last.  As can be seen above, that couldn’t have been much further from the truth.  It would have been convenient to frame the results in such a way so as to skew the actuality of the data, but I don’t believe in confirmation bias – the results are what they are regardless of any preconceived notions.  Never trust anyone seeking solely to confirm his or her beliefs.

So with that said, I’ll let the data function as what it is – a nice guide of the current cost of 2013 rookie players in terms of 2014 rookie picks.  If you have interest in trading picks away for some relatively proven talent, the first table above provides a nice blueprint.  No measure of evaluation is perfect, but I truly believe this is a good start.

However, even given the unexpected results, I believe there are a few common themes:

  • There is a distinct Sammy Watkins/Mike Evans effect:  It’s my belief these two players are more highly regarded than any 2013 prospect.  As such, it’s no surprise to see that every player (save for Gio Bernard) is valuated at the 2014 1.03 selection or below.
  • The quarterbacks had better develop quickly:  Perhaps this is a byproduct of the spoils of the class of 2012.  75% of the 2013 rookie signal callers suffered a drop in value, and both EJ Manuel and Geno Smith were two of the four biggest fallers (second table).  The days of a grace period at the position are apparently behind us.
  • First round running backs and receivers generally maintained value:  With the exceptions of Marcus Lattimore, Johnathan Franklin and Tavon Austin, ball carriers and pass catchers selected in the first round were fairly stable.  The 2014 crop of running backs is viewed as weaker than that of 2013, potentially explaining the lack of drop-off at the position, and the receivers each flashed enough to be considered risers.  More likely, it’s a general combination of 2013 performance and an unwillingness to sell on perceived young studs.
  • Second-round receivers essentially flatlined:  Outside of Terrance Williams, the second-round receivers didn’t show any appreciable movement.  This is likely due to a marriage of decreased expectations and marginal statistics.
  • We’ve become smarter with regards to tight ends:  Tight ends rarely if ever flash in year one.  Previously this would’ve led to a mass exodus at the position, but in this study only one player (Tyler Eifert) saw his value decrease.  On the whole, 91.7% of the position saw an increase in value.
  • Early-round selections better make the most of their use:  I’m looking at you, Franklin and Austin.  When first-round picks seemingly fail to produce, they bottom out rather quickly.
  • Look for players with unsettled situations:  What do the 12 biggest movers (table two) have in common?  Each was drafted into a fluid situation, and 2014 could be the year where they truly blossom.  If a 2014 player you like finds himself in a favorable depth chart, snag him now!  You might not be able to afford him next year.
  • The biggest movers were late-round selections:  Well duh, right?  The ability to rise higher is definitively tied to draft position, as later-round selections can appreciate to a larger extent.  Once again, if there’s a late-round player you like, try and trade for him early in the season before he gets his shot (i.e. Andre Ellington, Jordan Reed, Kenny Stills, etc.).
  • Inaction works in mysterious ways:  While some draft-isms are predictable, others remain cryptic.  For example, even though it was known that Lattimore wouldn’t play this year, his value dropped nonetheless.  Compare that to players like Christine Michael, Knile Davis, Khiry Robinson, Marquess Wilson and Travis Kelce who hardly saw the field (if at all) but nonetheless appreciated in value.  Sometimes, beauty is simply in the eye of the beholder.

Conclusion

Rookie Fever is a very real yearly spectacle.  With that said, even though these fresh-faced footballers launch themselves into our hearts, it doesn’t necessarily mean that their direct predecessors will suffer a drop-off.  However, observance of the general yearly trends can help you game the system, ensuring a greater likelihood of “picking” the correct move.

Follow me on Twitter @EDH_27

[/am4show]

eric hardter