Visualizing Market Share And Age: 2017 Wide Receivers, Summary View

Market share and player age are very important factors to consider when projecting wide receivers from college to the NFL. RotoViz has done some great work highlighting the importance of these pieces of information on a prospect’s outlook as a pro (see Kevin Cole’s work using decision trees and Jon Moore’s articles on market share and age). In that spirit, I’m going to take a look at market share, often in conjunction with player age, for the 2017 class of WR prospects.

I want to look at the data from both a high level point of view, across all prospects together, and also from a more granular point of view, with smaller subsets of prospects considered in chunks, so I’m going to give each perspective a separate article. The first part — what you’re reading this very moment — will take a high level view across all of the WR prospects in the 2017 draft class, and the subsequent parts will take a more granular view, looking looking at specific clusters of WRs grouped by RotoViz Scouting Index scores.

All Market Shares were taken from the 2017 FBS Receiver Market Share Database, and player ages were taken from the 2017 Draft Age Database.

One deliberate change I’ve made from some previous looks at this topic is that I’m reporting a player’s age as his age in years, rounded down to the next full year, as measured at the end of a calendar year, as opposed to age in days/months/decimal years/etc. This change is primarily to enable relevant comparisons between players at specific timepoints in their careers and to facilitate readability.

Alright, on to some graphs!

Average Market Shares Receiving Yards By Players

First, it can be helpful to see the average Market Shares (MS hereafter) for receiving yards for all of the prospects across their college careers; this is just the average of each of their season totals. I’ve plotted that using dots below, ordered by average MS receiving yards.

A couple of things pop out. First, it looks like most of the 2017 WR prospects had career averages between 15 percent and 30 percent. This is a big range, but is useful context, especially for identifying WRs with very high or very low market shares. For instance, it’s pretty easy to identify the five WRs with less than 15 percent average MS as possible players to avoid and the five WRs with greater than 30 percent MS as possible players to target.

Second, Corey Davis from Western Michigan University was a juggernaut over the last four years, with the best average MS (40 percent), which substantially outpaced the second best, Cooper Kupp (35 percent). Davis has long been a RotoViz favorite and deservedly so. More on him in a minute.

Kupp, from Eastern Washington University, is a year older (23) than Davis (22) but had the second highest average MS receiving yards, which, relatively speaking, is still a lot: besides Davis and Kupp, there were only three other WRs who averaged more than 30 percent MS over their collegiate careers:

Average Market Shares Receiving Yards By Player Age

Next, let’s look at the distributions of MS receiving yards across player ages. Continuing the narrative of Corey Davis as king of MS receiving yards, the boxplots below show the following statistics on full season, injury-adjusted MS receiving yards, separately for each age for players who were between the ages of 18 and 23:

  • The median (horizontal line in the middle of each box)
  • First and third quartiles (horizontal lines below and above the median)
  • Maximum and minimum values within a conventional range (the vertical lines above and below each box, which correspond to the 1.5 * interquartile range, i.e. difference between the first and third quartiles, subtracted from first quartile or added to the third quartile)
  • Outliers (points outside the conventional range specified above)
  • Maximum value for each age labeled with player and MS receiving yards, with the season that it occurred in parentheses


This graph tells me a few things:

  • First, older players tend to have greater MS receiving yards. The medians in the graph increase with player age.
  • Second, I can see that Noel Thomas from the University of Connecticut had the highest MS receiving yards of any WR prospect in this draft class, at 49 percent!
  • Third, it’s clear that, relative to other WR prospects in the 2017 draft class, Corey Davis played a bigger role over the course of his career in the WMU offense than other prospects did in their respective offenses, and he started contributing in a major way at a younger age than other prospects did as well.

Next Steps

Summary statistics are useful for getting the big picture, but aren’t as good for enabling player-by-player comparisons over time. In the next part of the Visualizing Market Share And Age: 2017 Wide Receiver Prospects Series, Shawn Siegele and I take a look at MS trends over time for WRs in the top tier of RotoViz Scouting Index scores.

Author Details
Kloet rhymes with flute. I’m a data scientist in Chicago.
By Jim Kloet | @jimkloet | Archive

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  1. Is there a database on RV that shows career MS and/or best season MS? I would like access to the full data set to help create a WR prospects model. Many thanks. Jesse

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