Fantasy Football Beneath the Surface: RB Receiving Trends
Beneath the Surface: Historical RB Receiving Trends
Another season of NFL football, another pile of data to sift through looking for clues. Clues as to what leads to certain results and clues for speculating what might be coming next. They say, “looks can be deceiving.” The same can be said for surface-level statistics in fantasy football. During the 2022 NFL season, this weekly article series looked beneath the statistical surface, beyond the standard box score, using the premium statistics provided by FantasyData, including advanced and efficiency metrics across the fantasy skill positions, to search for puzzle pieces that fit together. I will be continuing the “Beneath the Surface” series with multiple “2022 Season Recap” articles during the offseason.
The metaphorical dust has settled on the 2022 fantasy football season. There are many important and interesting forward-looking topics, like incoming rookie prospects, the year-long grind of dynasty formats, NFL Draft content, and speculation about free agency and potential trades (to name a few). And I will be delving into each of these topics as the offseason progresses. But I find it important to put the 2022 season under the statistical microscope while it is still somewhat fresh in my mind. In this “2022 Season Recap” series, I will be looking beneath the statistical surface across each fantasy football skill position in order to add important context to the season-long numbers most will come back to during the summer as the 2023 season gets closer.
In Part 1 I looked at RB consistency in weekly fantasy scoring. I will be doing the same for each position but I wanted to pivot for this article based on a trend I discovered while doing research. Specifically, the trend involves RBs and RB receiving output. Later in this article, I will discuss the recent downward trend in overall RB targets per game across the NFL. But I want to start with a deep dive into the value of receiving output for RBs in PPR formats, including some patterns I discovered when looking at the proportion of an RB’s fantasy points coming from receiving.
RB Points From Receiving (PFR)
It is no secret that receiving output is a valuable piece of the RB fantasy equation, especially in scoring formats that award points for receptions. There are RBs (past and present) who do not garner a large portion of a team’s rush attempts, whose values are boosted significantly by their receiving production, such as JD McKissic, Nyheim Hines, Darren Sproles, James White, and Tarik Cohen. Then you have your three-down studs who excel at both rushing and receiving, such as Christian McCaffrey, Austin Ekeler, and Saquon Barkley.
Last year I decided to do a deep dive into what I call “Points From Receiving” (PFR) for RBs, looking at the historical PFR trends across different groups of RBs. I then show %PFR, which is the percentage of an RB’s total fantasy points scored via the typical receiving criteria (receptions, receiving yards, and receiving TDs). For instance, in 2021 among the Top 24 RBs (PPR), Damien Harris had the lowest %PFR at 11.0%, meaning only 11.0% of Harris’s total PPR fantasy points were generated by receptions, receiving yards, and receiving TDs. The highest PFR of that group was Cordarrelle Patterson, with a %PFR of 53.1%. I ended up calculating RB %PFR for the Top 24 RBs in each season, going back to 2002. My goal with this research was to observe any meaningful trends, especially in the last 10 years, and to determine if any significant PFR thresholds are visible that indicate better odds of an RB finishing in the Top 24 or Top 12.
To start, among RBs who finished in the Top 24 of a given season with a %PFR above 60%, here are the PFR leaders since 2010 (%PFRu: %Fantasy points from rushing only). I chose 60% as an initial threshold because it helps to convey the fact that %PFR and fantasy points do not necessarily result in a positive linear correlation.
A total of 32 RBs have finished as a Top 24 RB (PPR, PPG) with a %PFR above 60% since 2010. The average finish amongst that group is RB14.7. Twelve of those RBs (37.5%) finished as a Top 12 RB. That number surprised me at first as it seemed like a low proportion of this group finishing as an RB1. But you can see this group is filled with what I call the HELV (High Efficiency-Low Volume) RBs. Highly efficient RBs with lower total volume who are often near the top of the fantasy points per touch leaderboard (hence the “high efficiency”). A player like Derrick Henry or Josh Jacobs will never lead the league in points per touch simply because they receive too much volume to threaten that category. In general, the higher the denominator climbs in any “variable per variable” equation, the lower the product becomes and the more difficult it is to sustain a higher number. This is why the HELV RBs tend to have high points per touch numbers, but do not commonly finish in the Top 12. It is why, personally, even in PPR, I always have a twinge of nervousness when I feel my roster construction is relying too heavily on this type of RB. Every now and then, they smash. Three of the quintessential HELV RBs were Darren Sproles, Danny Woodhead, and James White. And you can see each of those RBs, when above the 60% PFR threshold, did finish with an RB1 season. Once. On average, targets are worth more fantasy points than rush attempts in PPR. For more on that, I’ll again reference the excellent work done by Marvin Elquin (@FF_MarvinE on Twitter) in his series for The Fantasy Footballers on Expected Points. And it is the lifeblood of the HELV RBs in PPR formats. Once you move to half PPR or the dreaded standard formats, the value of these RBs decreases significantly, because even with a target being worth more fantasy points than a rush attempt, the one simple rule/cliché of RBs cannot be superseded. Volume rules the day.
So, this shows the value of %PFR but also shows that a higher %PFR by itself does not always equate to more fantasy points or a higher fantasy finish. But if you lower the %PFR threshold you do see the proverbial “sweet spot” materialize. The table below shows the average touches per game and average PPR finish of the RBs in the following %PFR ranges (since 2010).
- %PFR: 30.0%-39.9% (30% Range)}
(For each range: Top 24 RBs whose PFR comprised [% Range] of their total fantasy points)
- %PFR: 40.0%-49.9% (40% Range)
- %PFR: 50.0%-59.9% (50% Range)
- %PFR: 60.0%-69.9% (60% Range)
- %PFR: 70.0%+ (70%+ Range)
You can see the 40.0%-49.9% range (40% range) jumps off the page/screen with an average RB PPG rank of RB10.5 and 67.7% of the RBs in this group finishing as an RB1 (Top 12 RB). Both of these are the best fantasy scoring numbers of the five ranges, followed closely by the 30% range. And this is not skewed by a smaller sample size, as this %PFR Range includes the second-highest number of total RBs (64) after the 30% Range. Remember earlier when I said volume rules the day over efficiency? That is reflected in this data as well. It is not a coincidence that the RBs in the “sweet spot” %PFR range (40% Range) averaged the most opportunities and touches per game of the five ranges, but averaged the second-lowest PPR points per touch. On the flip side, as discussed previously, the highest PPR points per touch averages (50%, 60%, and 70%+ Ranges) include the lower average rank and lower %Top 12 finishes.
Also worth noting are the average opportunity ratios. As an RB’s opportunities swing in favor of targets over rush attempts, average total opportunities and touches per game creep closer to single digits. These are the HELV RBs I described earlier. The opportunity ratio associated with the “sweet spot” %PFR range is 76% rush attempts and 24% targets. Please note that this study, like any involving analytics, is not meant to be definitive or absolute. It is not my intent to arrive at some sort of end-all-be-all with any of the research I conduct. The purpose is to look at a relatively large sample size through different lenses (in this case %PFR) to arrive at patterns/trends that can potentially increase my (and more importantly your) odds of making the best decisions possible. The reason I bring this up is related to opportunity ratio. The 76%-24% average opportunity ratio tied to the most successful %PFR range (40% Range) does not apply across the board. As you descend from the elevation of a study including 214 total RBs from 2010-2022 things can become player-specific. For instance, I recently looked closer at Alvin Kamara after his disappointing finish in 2022. Part of the issue as I see it was how Kamara’s usage shifted over the last two seasons.
Curious to see what NO does at RB. Kamara had a down year & I think he needs an RB like Ingram of a few years ago (David Montgomery?)
Historically, Kamara’s usage sweet spot has been an opp ratio of ~65% Rush 35% Targets (2018-2020) That shot up to 75%-25% the last 2 seasons
Curious to see what NO does at RB. Kamara had a down year & I think he needs an RB like Ingram of a few years ago (David Montgomery?)— Scott Rinear (@MunderDifflinFF) February 11, 2023
Historically, Kamara’s usage sweet spot has been an opp ratio of ~65% Rush 35% Targets (2018-2020) That shot up to 75%-25% the last 2 seasons pic.twitter.com/GE1N0uvSvp
With Kamara, a noticeable difference between his stellar seasons (2017-2020) and his down seasons (2021 and 2022) was a change in his opportunity ratio. From 2018-2020, when Kamara finished as the RB4, RB8, and RB1, his opportunity ratio was approximately 65%-35% in favor of rush attempts. In 2021 and 2022, where he finished as the RB8 and RB14, that ratio was closer to 75%-25% in favor of rush attempts. If Kamara plays in 2023, the Saints need to bring in another RB to take more of the early down rush attempts (a 2023 version of Mark Ingram’s hay days in New Orleans) for Kamara to move back toward his 2018-2020 usage. Now, this wouldn’t make sense if we used the %PFR data as all-inclusive, because as we saw in that data, a 75%-25% opportunity ratio is the “sweet spot” (which has not proven ideal for Kamara) and the 65%-35% ratio (which is Kamara’s sweet spot) is an average that is tied to the least successful range (60% Range).
It may not be surprising that my biggest inspiration in the fantasy football industry is JJ Zachariason (@LateRoundQB on Twitter). JJ is a master at building large sample sizes, focusing through many different analytical lenses, and finding actionable trends and micro trends with the data. And JJ often has to issue reminders that he is not searching for a fantasy silver bullet or panacea. There is no combination of metrics that will produce 100% hit rates, no matter how many angles you look from or how far you drill down. There is way too much variance and unpredictability in a game like football. It’s about an attempt to increase the likelihood of a hit. Looking back at PFR, yes, only 26.7% of the Top 24 RBs in the 60% Range finished as RB1s since 2010, easily the lowest of the ranges. But it is not 0.0%. None of this is absolute. What the data is saying is that, historically, a Top 24 RB with a %PFR in the 40% Range and an opportunity ratio around 75%-25% in favor of rush attempts have more often finished in the first half of that Top 24 than an RB within the other %PFR Ranges.
Historical Examples in Each %PFR Range
To add some context and briefly ascend out of the weeds, here are the notable RBs from the different %PFR Ranges:
Theo Riddick’s 92.7% (2015) is the highest %PFR since 2010. The lowest %PFR from a Top 24 RB since 2010 belongs to LeGarrette Blount at 4.7% (2016). In that same time, out of 312 total Top 24 RBs, only nine have finished with a single-digit %PFR:
- Alfred Morris: 9.4% (2013)
- Jamaal Williams: 9.3% (2022)
- Derrick Henry: 9.2% (2020)
- Michael Turner: 9.1% (2010)
- Chris Wells: 8.6% (2011)
- Alfred Morris: 7.4% (2012)
- Stevan Ridley: 5.4% (2012)
- LeGarrette Blount: 4.6% (2016)
Using 30.0% as the minimum %PFR threshold as I have up until this point is somewhat arbitrary. There are plenty of RBs in the 0.0%-29.9% Range. The following is the same %PFR Range data as shown above, but with the remaining two Ranges included (0.0%-19.9% and 20.0%-29.9%):
You can see that while the 20% Range still has a decent hit rate with a %Top 12 Finishes of 49.1% and an average rank of RB12.7, the <20% Range has the second-lowest numbers in those same categories. With the full data set in front of us now, it is clear that the ideal Range is 30.0%-49.9%, with the 40% Range slightly better than the 30% Range. The hit rates in the higher ranges are lower, but that is where some of the higher-ceiling players reside (Kamara, McCaffrey, and Ekeler.
2022 RB Points From Receiving
Armed with this historical data, we can now look specifically at 2022 numbers to see if any RBs outside the Top 12 are realistically within striking distance of this range, increasing the chances they break into RB1 territory in 2023. The following chart shows the Top 36 RBs in 2022 and includes the following:
- Opportunities (rush attempts + targets) per game
- Touches per game
- PPR points per touch
- Opportunity Ratio
- %Opps Rush Att (Rush attempts divided by total opportunities)
- %Opps Targets (Targets divided by total opportunities)
You can see that the top four RBs include two of the aforementioned 50%+ Range studs (Ekeler and McCaffrey) and two of the 20% Range studs (Jacobs and Henry. This is another example of the %PFR “sweet spot” not being an all-or-none metric. However, after those four RBs, five of the next eight Top 12 RBs fall in the 30% and 40% Ranges. Typically I set a minimum games threshold of eight to qualify for PPG leaderboards. Here, I left Breece Hall at RB8 on purpose, even though he was one game under the threshold. Hall’s per-game volume was the lowest in this Top 12, but that is not surprising considering it was the first seven games of his rookie season. But his %PFR (39.8%) and Opportunity Ratio (71.4%-28.6%) are in the heart of the “sweet spot.” It isn’t groundbreaking to say Breece Hall is good, but this is further evidence that he could be a perennial Top 12 RB (and would have been in 2022 were it not for the injury).
Nick Chubb had the lowest %PFR in the Top 12, but, as is the norm with Chubb, he made up for that with volume and rushing efficiency (5+ yards per carry), and was right at the 20 touches per game mark. Only one RB outside the Top 12 had more touches per game than Chubb (Jonathan Taylor).
Another RB in the ideal range was Devin Singletary, with a %PFR of 39.1% and Opportunity Ratio of 76.6%-23.4%. Singletary’s lack of volume kept him out of the Top 24 with only 14.8 opportunities per game. Swift was the only RB in the Top 24 with both opportunities and touches per game below 15. I doubt that changes much if he re-signs with Buffalo, but he is a free-agent RB I’m keeping my eyes on. The numbers described above are promising if he lands somewhere with a path to increased volume.
Najee Harris is interesting here as well. Harris had a disappointing season compared to his rookie campaign, but still had a solid %PFR (38.2%) and volume (18.8 opportunities per game). His Opportunity Ratio skewed toward rush attempts in 2022 (82.7%-17.3%) but he is only one year removed from the more ideal ratio he put up in 2021 (76.6%-23.4%). I know, Ben Roethlisberger and his frequent check downs retired after the 2021 season, and Harris’s relatively high 2021 %PFR (46.1%) took a hit with rookie Kenny Pickett. But rookie QBs typically have this effect on incumbent RBs. Over the final five games, with Pickett’s feet more firmly beneath him, Harris was the RB10 in PPG.
The last two RBs I’ll discuss are Rhamondre Stevenson and James Conner. Both were well within the ideal range:
- %PFR of 45.9%
- Opportunity Ratio of 71.1%-28.9%
- %PFR of 40.5%
- Opportunity Ratio of 75.9%-24.1%
Stevenson barely missed the Top 12 so he is a prime candidate to break into that club in 2023. Conner had one of the quietest RB1 seasons I can remember. Conner is signed through the 2024 season. The Cardinals have a potential out next year that could turn Conner into a cut candidate, but as if now he’s slated to be the Cardinals’ primary RB again in 2023.
I will be continuing to track RB PFR and digging deeper to see if any more useful data can be gleaned. I’ll conclude this article with the %PFR trends year by year. The following are the average %PFR for both Top 24 and Top 12 RBs since 2010. You’ll notice the recent downward trend in the Top 24 group, basically having decreased steadily since 2017. The same trend showed up with Top 12 RBs from 2018 to 2020, but that group’s average %PFR has increased in the last two seasons. The downward trend shown with the Top 24 group has been the case across multiple league-wide RB receiving statistics (including average targets per game). What is causing this? Is it a trend we can expect to continue or will it level out and/or turn upward as the Top 12 %PFR has over the last two seasons? What impact is it having on fantasy-relevant RBs and fantasy scoring? I will be diving into this topic in a future article.
Thanks for reading! If you have any questions about the data used in this article or about fantasy football in general, feel free to hit me up on Twitter.
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My name is Scott Rinear and I live in Seattle, Washington with my wife, two daughters and golden retriever (Jasper). Our biggest passion as a family is camping. We camp at least 10 times a year. My biggest passion personally is fantasy football. I have been playing fantasy football since 2006 and started producing content in 2020. I am a lifelong Seahawks and Mariners fan and will continue my fandom for the SuperSonics once they return to Seattle. I love everything about football, especially analytics and data analysis, and I’m a sucker for a good spreadsheet. I am a proud member of the Fantasy Sports Writers Association (FSWA).