DynastyFootball

Second Year WR Projections: Round One

chris baldwin

chris baldwin

For this article, I want to lay down some groundwork for evaluating WR prospects based on their rookie year performance. I’ll walk you through the methodology here, and give some actionable examples for players drafted in the first round. Then I’ll refer back to this piece later, when I do more projections for those drafted in later rounds.

The genesis for this idea came from last year, when I took this surly and dim view of A.J. Jenkins’ prospects. This time around I built a database of 330 WRs drafted between 2000 and 2013. Then I compared their rookie performance to their rest-of-career performance. There are definitely discernible trends that can shed light on how we should project 2013 (and eventually 2014) rookies.

The opportunity here is that, entering a player’s second year, fantasy expectations are still largely framed by the player’s draft pedigree, and whether or not owners think the player lived up to that expectation in their rookie season. Identifying which second year players may be over or under valued allows you to exploit those expectations. Join me as we take a round-by-round look at WR performance and projection.

Draft Board

It’s been acknowledged elsewhere at RotoViz, but a player’s draft slot is an important piece of the projection puzzle. First, it’s a proxy for all the pre-draft scouting NFL teams have done, and represents the NFL’s opinion of the player’s prospects. It also, most likely, has a bearing on how invested a franchise will be in seeing the player succeed. NFL draft position is also correlated with fantasy draft position, and definitely impacts our expectations of player performance.

As Jonathan Bales has noted, however, once a player has some actual NFL experience, pre-NFL data has less impact on player projections. So after a player has completed their rookie season, where they were drafted still matters- but not as much as some other measures.  We can see this in the following table, which shows the R^, or R-squared, value for several metrics, based on a player’s rookie season. As a reminder, R^ is just the percentage of variation in a dependent variable (career FPG, 0.5 PPR scoring, in this case) that is explained by the variation in another variable. A score of “1” means the variables move in lockstep. A score of “0” means the two variables move randomly vis-a-vis each other:

Rookie Stat Career FPG R^
Fantasy Pts 0.45
Rec Yards 0.44
Receptions 0.42
msRecYards 0.4
Fantasy Pts/G 0.39
Receiving TD 0.36
Games Started 0.25
Rd Drafted 0.24

As you can see, after their rookie season a player’s draft position still has some relationship to their rest-of-career performance. But the players’ rookie performance metrics have a stronger relationship. These are pretty strong relationships, especially since there is no adjustment made for injuries, for example. Let’s dive into the data.

Round One

Set R Games R GS R Rec R Yds R msYds R TD R FPG ROC FPG Pct Increase
All 13.9 7.5 37.7 526.7 0.16 3.2 6.3 8.3 24%
Hit 14.3 10.3 48.9 710.7 0.21 4.8 8.6 14.5 40%

Here’s how to read the table: The two sets are All (all WRs drafted in round one since 2000) and Hit (the top 20%, as measured by ROC FPG). Then comes the average rookie year games started, receptions, receiving yards, market share of receiving yards, receiving TDs, and rookie year fantasy points/game (FPG). The penultimate column shows rest of career FPG (rest of career equals players career, minus the rookie season), and then the percent change in FPG from rookie season to rest of career. An important note: the ROC FPG average isn’t necessarily attained in year 2. It just represents the average FPG for all games in years 2+.

The immediate takeaway from this table is that even round one WRs who go on to have stellar careers (Hits averaging 14.5 FPG in a 0.5 PPR format), don’t (as a group) post huge rookie seasons. The opportunity here comes in the form of leaguemates who live in the now and are disappointed by their rookie WRs, making them potentially available at a discount via trade. The reward for identifying the players likely to be Hits? About six FPG. It’s also worth noting that, although they appear in a similar number of games, and post similar receptions, the eventual Hits start more often, and dominate the other rookie categories.

First Run

Player R Games R GS R Rec R Yds R msRYds R  Rec TD R FPG
DeAndre Hopkins 16 16 52 802 0.21 2 7.4
Cordarrelle Patterson 16 5 45 469 0.14 4 5.8
Tavon Austin 13 3 40 418 0.13 4 6.6
  • No surprise, but DeAndre Hopkins looks like a Hit. He certainly has WR1 Footprint written all over him. The actionable bit here is twofold. First, if anyone in your league suffered through some irrational Hopkins exuberance last year, you might be able to pry him loose via trade.
  • The second bit of actionable intel here is actually related to 2014. Take a look at which rookie WR looks A LOT like Hopkins. Did I mention how similar they are?
  • Things don’t look nearly as good for Patterson and Austin. Take a look at Patterson’s projections using the WR Sim App. I tossed out his three lowest-targeted games from last season to get this “best” projection:
Patterson Standard Half PPR PPR
Low 4.1 5.5 6.9
Median 5.6 7.2 8.6
High 6.6 8.1 10.1
  • Patterson is being drafted as WR 14 in redraft leagues and as WR8 (EIGHT!) in dynasty start ups, with an average ADP of 16.8. Wow. Look, I’m not saying Patterson’s future is bleak – there’s a case to be made for his eventual breakout –  but I do think he’s pretty clearly being overvalued right now. I’m actually trying to trade him where I own him. If I don’t own him, I’m definitely not touching him at his current ADP.
  • Although he slightly outproduced Patterson on an FPG basis, Austin’s rookie campaign was nothing to write home about either, coming in below the historical round one averages (let alone the round one Hits). Here’s Austin’s 2014 projection:
Standard Half PPR PPR
Low 2.7 3.8 4.7
Median 5 6.2 7.7
High 7.1 8.7 10.4

 

Round Two is here.

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By James Todd | @spidr2ybanana | Archive

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