Visualizing Market Share And Age: Josh Malone and the Tier 3 WR Prospects
Over the last several years, Jon Moore has done a tremendous job advancing our understanding of the intersection of age and market share receiving production. Today, I’ve enlisted Jim Kloet to help present these visualizations for the 2017 class. In this post, we’re focusing on Tier Three receiver prospects as reflected in the RotoViz Scouting Index. – Shawn Siegele
How important is market share and age?
In Part One, we talked about the large gaps in age-adjusted production between first-round busts and second-round hits. You’ll be surprised how much older and less productive those first-round misses were.
In creating our Tier Three, we used the post-combine RotoViz Scouting Index. Check out this week’s edition for the latest updates in player movement.
|Player||RSI||Birthday||Age (At NFL Draft)||Career msYD|
|Josh Reynolds||81||16-Feb-1995||22 years, 2 months||26.3%|
|Stacy Coley||80||13-May-1994||22 years, 11 months||16.8%|
|Jehu Chesson||79||29-Dec-1993||23 years, 3 months||14.5%|
|Artavis Scott||78||12-Oct-1994||22 years, 6 months||20.7%|
|Ryan Switzer||66||4-Nov-1994||22 years, 5 months||19.8%|
|Travis Rudolph||62||15-Sep-1995||21 years, 7 months||21.7%|
|Josh Malone||53||21-Mar-1996||21 years, 1 months||18.3%|
|Fred Ross||52||19-May-1995||21 years, 11 months||19.3%|
Jim explains why he will be rounding down the player ages to the next full year in his High-Level View of the 2017 Class.
The third tier of 2017 WR prospects has lower market shares of receiving yards (msYDs) than the second tier prospects (most player-seasons appear to be below 25 percent) but again show trends of increasing over time.
Josh Malone and Fred Ross had similar slopes of consistently increasing msYD starting at age 18. Malone finished in a tie for the second-best Freak Score in this class at 71. His athleticism, status as a former top prospect, and 36 percent share of his team’s receiving yards all point to solid sleeper status.
The 35 percent msYD posted by Ross in his age-21 season was the highest of the tier three WR prospects. Unfortunately, it required his fourth year of competition. Unless he’s drafted much earlier than expected, he won’t factor into the rookie draft discussion.
Josh Reynolds also recorded consistently increasing msYD over his career, but the slope of that trend wasn’t as steep as observed with Malone and Ross. His 4.52 forty is sluggish for a 193-pound prospect, but the six-foot-three receiver managed a 37-inch vertical and 6.83 three-cone. He’s a prospect to monitor if those numbers land him in the first three rounds.
Artavis Scott (78 RSI Score) is the first prospect plotted here who had consistently dropping msYD, starting his college career at age 20 with nearly 30 percent of Clemson’s receiving yards but wrapping his career at 22 with closer to 12 percent of his team’s receiving yards. His poor athleticism undermines the idea that he’s a solid prospect overshadowed by Clemson’s higher-profile talent.
Stacy Coley, Jehu Chesson, and Ryan Switzer do not look like legitimate prospects by this analysis, and receivers drafted in their expected range have to overcome long odds even with good production profiles.
Looking for trendy prospects like Chris Godwin and Carlos Henderson? They can be found in Tier 2.
If you’re researching the QB position, RotoDoc’s groundbreaking QB model explains why Mitchell Trubisky looks like a star and Deshaun Watson a player to avoid. On the RB front, Kevin Cole provides his logistic regression model and demonstrates why D’Onta Foreman is undervalued. Meanwhile, Phil Watkins has you covered at TE, showing that 4 Elite Sleepers Join the 4 Stars to Make This The Best TE Class in Years.
We have a wealth of research on the WRs. If you’re looking for the freakiest of the freaks at the ultra-athletic WR position, take a peak at the 2017 WR Freak Scores. You can take a class-by-class stroll through the career trajectories of 2017 prospects starting with the true juniors, or peruse the final age and production numbers with Moore’s Phenom Index.