Background
In the offseason I’m always reading articles and consuming fantasy football content trying to catch the latest new theory in the fantasy analysis world. I came across a theory by Scott Simpson (@NimbleWNumbers) he calls “Wide Receiver 2 Theory.” The theory is that drafters tend to overdraft the team’s WR1, but often the player with the higher ADP can end up finishing the season with more fantasy points. In his article he lists three criteria for identifying these WR2 theory targets:
- They should be from a team with a high-powered passing offense, that is a team with an above average passing volume.
- They should be on a team with a high number of vacated targets, indicating roster depth chart uncertainty.
- They should be young / emerging players, ideally behind an aging vet.
I wanted to test the theory, and then use it for 2024.
Method
From the RotoViz Best Ball Win Rate Explorer I was able to download wide receiver ADP from prior years. I then used the RotoViz Screener to pull WR player age and total PPR points for the 2023 NFL season. After cleaning and merging the data, I sorted by team and ADP and took note of which player was each team’s WR1, WR2, and WR3, by ADP. I also took note of overall positional ADP and end-of-season positional rank based on total points for the season. I define a “value” as any player whose final 2023 point rank exceeds their draft ADP rank by any amount.
Results
All wide receivers had a 40% success rate. This is important to anchor our numbers. Our model should be able to identify successful wide receivers at a rate higher than 40%.
WR1s had only a 38% success rate, which validates the assumption that team’s WR1s are being overdrafted. Filtering by teams with above average targets from 2022 had no effect. It’s worth noting that WR1s have an above-average best ball win rate; you do want to draft them, but it’s less likely for an elite WR to outperform ADP value.
When looking at the entire population, WR2s and WR3s had the lowest rate of success. A team’s second- or third-drafted WR only exceeds ADP value 29% of the time. Accounting for player age and high-power offenses improves that significantly to 57%, which confirms that WR2 theory does indeed statistically improve your chances of identifying undervalued players. Drafting WR2 theory players nearly doubles your success rate over drafting WR1s (38% vs 57%). In 2023 this model identified breakout players such as Brandon Aiyuk, Rashee Rice, George Pickens, Jordan Addison, and Jayden Reed.
2023
| Model: | Exceeds ADP | Does Not Exceed ADP | % Success Rate |
| ALL WIDE RECEIVERS | 38 | 56 | 40% |
| ALL WR1s | 12 | 20 | 38% |
| ALL WR2+ | 26 | 36 | 42% |
| WR2+ Younger than the WR1 | 14 | 15 | 42% |
| ALL WR on High Target Offenses | 12 | 18 | 40% |
| Young WR2+ on High Target Offenses | 8 | 6 | 57% |
Next, I went back and pulled the data from 2022 to confirm this theory. In that year, the results were less pronounced, but confirmed that the criteria of drafting young WR2 or WR3 on a high-powered offense is the best way to find potential sleepers. WR2 Theory in 2022 identified valuable sleepers such as DeVonta Smith and Brandon Aiyuk. It’s not just related to rookies either, as Chris Godwin met this criteria, being drafted as WR27 and ending up WR11 in his sixth (age 26) season behind 29-year-old Mike Evans.
2022
| Model: | Exceeds ADP | Does Not Exceed ADP | % Success Rate |
| ALL WIDE RECEIVERS | 46 | 63 | 42% |
| ALL WR1s | 12 | 20 | 38% |
| ALL WR2+ | 34 | 43 | 44% |
| WR2+ Younger than the WR1 | 17 | 22 | 44% |
| ALL WR on High Target Offenses | 20 | 25 | 44% |
| Young WR2+ on High Target Offenses | 10 | 11 | 48% |
Based on this analysis I’m slightly tweaking the WR2 theory to help identify sleeper WR candidates for 2024. I’ll be looking for receivers who are the second or third options behind older veterans, and on teams with above average pass attempts from 2023. When I combine the results from the past two years, this has the best outcome at a 51% success rate.
Looking Ahead
Listed below are the 2024 receivers meeting this criteria sorted by recent (May 2024) ADP. I might update this article as we get closer to the season to account for shifting ADP during camp and preseason games.
