Hello, and welcome back to another analytics-based article! Today, we’ll be looking at the 2023 wide receiver class. More specifically, I’ll show you which college production and efficiency metrics correlated most closely with fantasy points per game (FPPG) as rookies. I plan to run these numbers on some prior classes as well to see which college metrics are most reliable for fantasy projections year over year. If we find metrics that are consistently more predictive than others, we know to place a bigger emphasis on those when we’re ranking incoming rookie prospects.
Quick disclosure: I am just diving into some college analytics and sharing my findings. If you’re looking for an actual rookie model, I highly advise you to check out Chris Museezer’s WR1 Prospect Ranking Model. You can view his model breakdown here, which combines analytics with other factors like film grade, relative athletic score, and conference competition.
Receiving Metrics Used
For this study, I pulled numbers from the Top 18 dynasty wide receivers of this 2023 class, according to Superflex rankings from KeepTradeCut. The players in this study include Puka Nacua, Tank Dell, Rashee Rice, Jordan Addison, Zay Flowers, Jaxon Smith-Njigba, Jayden Reed, Josh Downs, Marvin Mims, Dontayvion Wicks, Demario Douglas, Michael Wilson, Quentin Johnston, Jalin Hyatt, Jonathan Mingo, Trey Palmer, A.T. Perry, and Cedric Tillman.
For the 2023 WR class, I looked at the following college statistics: Targets per route run (TPRR), Yards per route run (YPRR), Dominator Rating, Breakout Age, PFF Receiving Grade, Draft Capital, College FPPG, and yards per game (YPG). All the numbers I pulled were based on the prospect’s best collegiate season, with a minimum threshold of 100 routes run.
One more quick disclosure: You’ll notice an R^2 number in the charts below. This number shows how much a particular metric can be explained by the independent variable (in this case, rookie FPPG). R^2 numbers range from 0-1, with 0 meaning zero correlation and 1 meaning 100% predictiveness. The numbers on these charts will look pretty low for two reasons:
- I’m only including 18 wide receivers. If I included every single WR who made an NFL roster in this class, I could manipulate the numbers to show a closer correlation, as there would be more guys at the bottom who produced less in college and didn’t produce as rookies.
- These are just singular efficiency metrics we are comparing alongside their rookie FPPG. If there were a single college stat that could predict NFL fantasy production at an extremely high rate, there’d be no point in looking at the other numbers.
This is all to say; take the actual R^2 number with a grain of salt. We’re just looking to see which statistical categories were more telling than others so we know what to focus on with future classes.
Ranking Metrics By Predictiveness
The metrics can be broken down into three tiers of predictiveness for the 2023 class. They are as follows:
Most Predictive: College FPPG and TPRR
Some Predictiveness: PFF Receiving Grade, YPG, Breakout Age
Completely Random: YPRR, Draft Capital, Dominator Rating
As I mentioned, this is a small sample size (Top 18 Dynasty WRs for the class), so we must keep that in mind. As I review some prior draft classes, I expect some of these metrics to be more predictive. For instance, draft capital is typically predictive of future fantasy production (or at least opportunity for production). But in this class, several guys completely skewed the numbers. Later-round picks like Tank Dell and Puka Nacua poured on the fantasy points, while earlier picks like Jonathan Mingo, Jaxon Smith-Njigba, Quentin Johnston, and Marvin Mims didn’t produce in Year 1, relative to DC.
To keep this article short(ish) and sweet, I won’t show you the graphs I put together for each statistic, but I’ll show the two most predictive (College FPPG and TPRR).
College FPPG
Let’s look at a couple of the more predictive metrics for this WR class. College FPPG (best season) led the way, which was mostly unsurprising. This was based on PPR formats. There were a few outliers, like Jalin Hyatt. He has the fourth-best college FPPG of the group (23.64 PPG) but the second worst rookie PPG of this group. But for the most part, college fantasy points translated to rookie NFL production decently well.
Targets Per Route Run
I was pleased to see TPRR second in the rankings here, as I rely on that metric heavily in my dynasty leagues. The biggest outlier here was Trey Palmer, who finished with a 34.38% target rate at Nebraska but just 5.6 PPG as a rookie. But this shouldn’t be surprising when we look at the whole picture. Palmer was stuck (and still is) behind two stud receivers, Mike Evans and Chris Godwin, limiting his playing opportunity in 2023. Also, Palmer had to transfer from LSU to Nebraska to hit that mark. Before transferring, Palmer didn’t see the field a ton, and his best TPRR was 20.94%.
Other Takeaways
I wasn’t too surprised to see YPRR significantly less correlated than TPRR when it came to predicting rookie production. I found this to be true in some other independent studies I’ve done. For instance, I reviewed the Top 18 FPPG scorers amongst WRs last season and found TPRR was more predictive of fantasy points here as well. The R^2 for YPRR was .393, while the R^2 for TPRR was .483.
Dominator rating is one metric that analysts love to reference when they are debating prospects. Dominator rating primarily looks at the share of touchdowns and receiving yards a player commands in their offense. We know that touchdowns are not an overly sticky metric in the NFL, so I don’t usually look at this number first when studying prospects, but I do see the merit in using the algorithm to grade out prospects. As I review earlier classes, I imagine this number will become more predictive of future FPPG.
Puka was one of the reasons the Dominator rating wasn’t predictive, ranking 18th out of 18 (21%) but was 1st in rookie FPPG (17.6). This is because Puka simply didn’t see the field at ton at BYU. A similar case may occur in the 2024 class with Ladd McConkey. Ladd was pretty efficient on a per-route basis but ran less than 150 routes for Georgia in 2023, so his dominator rating is terrible.
This is why it’s essential to understand why a player scores higher or lower in certain statistical categories. Often, it can be explained away. If we don’t look at the whole picture, we’ll miss some massive rookie hits every season. Several signals in Puka’s college analytical profile suggested he was massively undervalued coming into the NFL. He was atop his draft class in TPRR, YPRR, and PFF receiving scores, had a very low drop rate, a low contested target rate, and a great contested catch percentage. His flaws were simply that he played at BYU and didn’t run many routes.
Conclusion
If you’re looking at college production for incoming rookies and want to simplify things, I’d focus primarily on their college FPPG and targets per route run. I think, more often than not, these two metrics will lead you in the right direction when deciding on rookie prospects for your dynasty team. I know this was based on a limited sample size, so take what you will from this exercise. I’ll be working on the 2022 class next and am interested to see how these results compare to the 2023 class, so stay tuned for that!
Thank you for taking the time to read this article, and I hope you got some valuable information you can use for your fantasy team! If you’d like additional insight into Dynasty Football news and analysis, please follow me on Twitter at @jim_DFF. Until next time, keep grinding out there, DFF family! #DFFArmy #AlwaysBeBuilding
