How Important Is The Combine For Wide Receivers?

The NFL combine has come and gone and now we’re left to make sense of what we learned. Who are the risers and fallers? How will the results affect draft positions? And most importantly, does it even matter?

The combine regression trees were helpful to figure out which combine drills matter for wide receivers and running backs, and allowed us to identify the 2016 prospects who “won” the combine at the respective positions. That said, a major piece of what we currently know is missing from the combine regression trees: collegiate production.

I added collegiate production to the wide receiver combine model to see how production and measurables work in conjunction with each other.

This is the result:


I’ll save you some time looking through all the different decision nodes: none relate to the combine measurables. When I put age, combine measurables, and a boatload of different collegiate production stats into the regression tree, none of the combine measurables were seen as significant. This shouldn’t come as a total surprise, the Harvard Sports Analysis Collective found as much in its combine research, and I’ve conducted other research hinting that the combine didn’t matter all that much for wide receivers. But the regression tree gives us another angle to look at the question, and an easily interpreted tree to make sense of.

The first and most important split of the tree is at 29 percent of career market share of receiving yards, meaning that share is more important than raw numbers, and the arc of a prospect’s career is more important than only his final-year results.

However, the final year does matter. All the other splits relate to only final-year statistics, with the market share of receiving yards in the season of 42 percent or greater leading to the most successful node. There are 33 receivers who have accomplished this in the data set, including all-time-greats, like Calvin Johnson and Larry Fitzgerald; younger studs like Allen Robinson and Amari Cooper; plus RotoViz favorites who never panned out,1 like Marvin McNutt and Kenny Britt.2

In additional to high market shares of receiving yards, the model also prefers receivers who can stretch the field; at least that’s how I interpret its all-else-being-equal approval of receivers who average higher yards per reception, or have lower reception totals.

Age also makes it onto the tree, but not for the higher market share branches. If someone hasn’t been a dominant from a market share perspective, but meets the threshold of at least 933 receiving yards his final year, the model gives a higher success rate if he is 21 years old or younger. Age does matter, just perhaps not as much for those with gaudy production.

The power of the regression tree analysis isn’t just it’s ability to find what’s important, but also to identify what isn’t. As draft boards are reshuffled post-combine, a profitable approach should be to generally fade wide receiver prospects who rise, and buy those who are falling.

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  1. At least not yet.  (back)
  2. Okay, Britt basically burned out, but Shawn Siegele’s Britt-flame was still bright last offseason.  (back)
By Kevin Cole | @Cole_Kev | Archive

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  1. Awesome stuff Kev, have been debating this in regards to players like Boyd and Higgins. Now I know my favoring of the production is at least logically sound.

  2. Three questions Kevin:
    1) I know some other people and myself have considered the possibility that workout measures don't show as productive for WRs because there is such a huge disparity in what WRs do and what kind of players play it (The difference between Megatron and Jarvis Landry for instance) and that specific workout measures might matter for specific types of WRs. Am I correct in that if that were the case this analysis would have shown as much, and it did not?

    2) Have you ran this analysis with draft position?

    3) Am I correct in assuming you're already working on a followup showing what WRs end up in what bracket?

  3. One thing I've begun wondering is as follows.

    Do prospects ever "game" their weight/height adjusted speed by adding or losing water weight?

    For instance a WR or RB could over hydrate, have a belly full of water for weigh ins and then flush it for his 40 two days later.

    I know it is a popular technique in boxing and MMA just wondering how much has that practice crossed over if at all?

    Not saying the metrics have no value, but back to the point of this article it will still come back to their production in college.

  4. Can anyone tracking this thread point me to a database / list that shows 2016 NFL WR prospects final year market share? I have one that has career MS but I'm trying to incorporate all @colekev_FF's regression tree data for RBs & WRs (the combine trees and production trees) into my rookie drafts spreadsheets


  5. Just doing some napkin math on Juju (since he seems to be the most controversial), and the tree doesn't love him as a prospect, but seems like it could be worse (if my math is right). I have him in the 3rd node from the right, which looks like the 30% success node.

    It looks like the regression tree hates Mike Williams (I excluded his freshman year since (a) he was a freshman, but more importantly (b) Sammy Watkins was there, so perhaps not fair to include. Also completely excluded the year he got hurt). It appears that even if you run ding him for his freshman year and put him on the other side of the tree, the model STILL hates him.

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