Two Easy Tricks to Find Better WR Prospects: Subtitle – WR Age is Dead
Two Easy Tricks to Find Better WR Prospects: Subtitle – WR Age is Dead
When I first began writing about wide receiver prospects, I was fascinated with the concept of WR age. Shawn Siegele wrote about it back in 2014, and then later expanded that to include breakout age, the age at which a WR prospect reached a 30% Dominator Rating during his college career. Jon Moore’s Phenom Index also had a major age component.
It has been obvious to anyone paying attention that WR age matters, and a great bit. It has been a major component of every prospect model I’ve created and has been a big reason to target a variety of players over the years. Despite the catchy subtitle, nothing included in this piece is going to debunk any of that research.
But it is going to improve upon it.
Fantasy Football Wide Receiver Methodology
Recently, I read through a great study by Rich Hribar that looked at early-declare WRs for fantasy football. This led me to an earlier piece from 2019 by Blair Andrews discussing the same topic. Much like breakout age and draft age before it, it is clear that declaring for the draft early matters for WRs.
I was interested in potentially taking that signal even further, so I went to my WR database, containing 431 prospects between the 2007 and 2017 NFL Drafts, and recorded for each prospect the following:
— If the prospect declared early
— How many years the prospect was in school (including redshirt years)
— What year the prospect broke out (including redshirt years)
I then looked at how many of these players were “hits”, which I defined as at least one 200 point PPR season within the player’s first three years in the league. By looking at hit rates for different groups, I was able to come up with some rather interesting results.
Finding #1: Focus on the Years
Something that both player age and early declaration studies have signaled is that time spent in college matters…so why not focus on it? I broke down all prospects in the database by the number of years they spent in school. Note once again that this includes any redshirt years, so a redshirt freshman and true sophomore both count as two years in school
Years | # | Hit | % |
---|---|---|---|
3 | 70 | 25 | 36% |
4 | 233 | 9 | 4% |
5 | 122 | 4 | 3% |
6 | 5 | 0 | 0% |
7 | 1 | 0 | 0% |
Already we are cooking with some serious gas. Lining up with the work of Hribar and Andrews is that declaring early for the draft matters, but it goes even deeper than that. What you actually want to target are players who leave college after three seasons. Taking a look at four-year players tells us it is mostly immaterial if the prospect is a redshirt junior declaring early, or a true senior:
Early | # | Hit | % |
---|---|---|---|
1 | 33 | 2 | 6% |
0 | 200 | 7 | 4% |
We should be highly focused on acquiring three-year college players.
Finding #2: Breakouts Still Matter
I would also be remiss if I neglected the importance of breakouts in this piece. Once again, we should be focusing on the time of the breakout relative to the player’s career.
Breakout Year | # | Hit | % |
---|---|---|---|
1 | 26 | 11 | 42% |
2 | 53 | 13 | 25% |
3 | 71 | 9 | 13% |
4 | 72 | 1 | 1% |
5 | 26 | 1 | 4% |
6 | 2 | 0 | 0% |
7 | 1 | 0 | 0% |
None | 180 | 3 | 2% |
If a player breaks out as a true freshman, you should likely be targeting them in your leagues. This is perhaps the only instance where targeting a fourth-year player makes sense:
Years | # | Hit | % |
---|---|---|---|
3 | 16 | 7 | 44% |
4 | 10 | 4 | 40% |
Note that there were only nine four year players in the entire sample to become hits, so having four of them be first year breakouts is a strong signal that is the secret sauce. This contrasts strongly with second-year breakouts, which only produce a hit rate over 9% if that player is also a three-year collegiate athlete.
Breaking out even matters when looking at the three-year college players.
Breakout Year | # | Hit | % |
---|---|---|---|
1 | 16 | 7 | 44% |
2 | 25 | 11 | 44% |
3 | 13 | 5 | 38% |
None | 16 | 2 | 13% |
It may not matter when three-year college players break out, but it certainly matters that they do. And by focusing on the years instead of age, we avoid missing on player such as Calvin Ridley, who was an older prospect when he joined Alabama, but dominated early, and played only three seasons.
Finding #3: The Bottom Line
Using all of the data presented, we can summarize WR prospects to target as being a part of two groups:
— True freshman breakouts
— Three-year college players who broke out
Split | # | Hit | % |
---|---|---|---|
Target Group | 64 | 27 | 42% |
Other | 367 | 11 | 3% |
But anyone who has looked at NFL prospects knows that draft position matters too. What if we focused only on top-100 picks?
Split | # | Hit | % |
---|---|---|---|
Target Group | 40 | 25 | 63% |
Other | 70 | 9 | 13% |
The results are even more pronounced, with our hit rate rising to 63%. Now let’s take a look at only first-rounders.
Split | # | Hit | % |
---|---|---|---|
Target Group | 15 | 11 | 73% |
Other | 18 | 4 | 22% |
This underscores the importance of draft position, as first-rounders outside of our target group still hit at a rate higher than a lot of other groups presented. With that said, nearly three out of every four prospects to be both in our target group and in the first round have gone on to become hits.
Perhaps the best way to summarize the findings is by leaving you with these smoothed out loess plots showing hit rate and average PPR scoring by draft position, split out based on our target group.


Conclusion
It is clear that while age has proven valuable, it is likely a proxy for collegiate playing time. By targeting true freshman breakouts and three-year college players, we can find a cohort of players that have historically hit at a very high rate.
One of the best parts about working in this space is the ability to constantly evolve and grow over time. There has been a ton of great work that has set the table for this research, and my hope is that one day someone else uses this to develop something even better.