Where Do High-Target Running Back Seasons Come From?

by | Jan 19, 2022

Because targets are worth so much more than rushing attempts (2.83 times more, to be exact, per analysis here by Scott Barrett), a high-volume role in the passing game can be either a shortcut to fantasy utility for role players or a launching pad to difference-making production for guys who lead a backfield. Since 2007, an average of only five running backs per season have earned more than 75 targets (a threshold for “high-target season” that I decided on because of that fact). And those 75 backs’ mean performance of 17.5 points per game in PPR would land them in mid-RB1 territory in the average season (using data back to 2007). Further, over a quarter of them (28-percent) finished at the average level of a top-3 PPR running back, while 62.7-percent finished at least an RB1 level, and 84-percent at at least an RB2 level. Clearly, identifying who these high-target runners might be is important, as it’s a near guarantee that — at the very least — they’ll be a starting-quality player in fantasy.

In order to better do this, I collected data on all 75 of those high-target running back seasons to find if there is anything common to them that would make future predictions of such seasons easier. I entered my exploration with a few hypotheses about things that would be common among these campaigns:

No. 1) I hypothesized that these high-target runners would be players who were inherently talented pass-catchers, rather than guys of various levels of ability who might simply have been thrusted into opportunity.

No. 2) I hypothesized that these high-target runners would come from high-volume passing attacks.

No. 3) I hypothesized that these high-target runners would come from teams on the extreme ends of the target distribution spectrum — either passing attacks that funneled targets to only one or maybe two other talented receivers, or passing attacks that had very flat and wide distributions of targets due to a lack of surrounding talent on the roster.


Hypothesis No. 1 was a slam dunk. In my own evaluation process, I use a composite of metrics to construct a Receiving Chops Score for running back prospects. Scores range from 0-100 based on a player’s percentile ranks in those various metrics. A Score of 50 or higher indicates that a prospect is above average as a receiver among all backs drafted since 2007. Of the 75 high-target running back seasons, 84-percent come from players with Receiving Chops Scores above the 50 mark, 63-percent come from those above the 60 mark, and despite only 25 players in the entire database boasting Scores above the 80 mark, 21.4-percent of 75-plus target running back seasons have come from that small group.

A high mark in The Breakout Finder’s Receiver Rating metric for running back prospects is also consistent throughout the profiles of these high-target running backs. 65 of these players (86.7-percent) boast Receiver Ratings above the 50 mark, 58 of them (77.3-percent) score at least a 75, and a full 44 of them (58.9-percent) score above the 90 mark. The vast majority of high-target running back seasons come from players who were good receivers as college prospects.


Hypothesis No. 2 was also a hit. Since 2007, teams have thrown the ball an average of 553 times each season, while our high-target runner’s teams have thrown the ball 575 times in an average year. And 68-percent of these teams have thrown the ball more times than league average in their particular season. It’s more likely than not that a high-target running back season comes from a high-volume passing attack.


Hypothesis No. 3 was probably a miss. My process for measuring an offense’s target distribution was to find the Target Share of an offense’s four most-targeted players (excluding our high-target running backs) and then calculate the standard deviation from the mean among the Target Shares of those players on each offense. The higher the standard deviation, the more concentrated the distribution of targets is on one or two (presumably more talented) players. The lower the standard deviation, the flatter the distribution is among (presumably similarly talented, closer-to-average) players. I assumed that high-target running back seasons would come from extreme ends of the spectrum. Those teams with only a few talented pass-catching options that would have room for many targets to a second or third option out of the backfield; or those teams lacking standout pass-catchers that would be prime environments for talented running backs to earn opportunities in the passing game.

Instead, I found no pattern in the target distributions of these high-target running backs’ teams. The average standard deviation from these teams’ top-4 option Target Shares is 5.8-percent, and only 21 of these teams (28-percent) had target distributions more than one standard deviation from that spectrum’s mean. In other words, almost three-quarters of high-target runners come from teams with target distributions in the normal range, not particularly concentrated or particularly flat.

Other features of target distribution were similarly inconclusive. Teams with No. 1 passing options with Target Shares at least one standard deviation above the average Target Share of top options (or in English: teams with unusually heavily-targeted number-one options) represented only 11 of 75 high-target runner teams. So it wouldn’t be accurate to say that high-target runners more often come from teams with one clear mega-alpha (teams with unusually lowly-targeted No. 1 options were equally scarce). Teams whose top two options unusually dominated their team’s targets (think in a Roddy White/Julio Jones duo kind of way) were also not inordinately represented.


What was clear was that high-target running back seasons most often come from players who are no less than the third option in their team’s passing game pecking order. 64 of the 75 seasons came from runners who were at least third in their team’s target totals. While 40 of them (so more than half) came from dudes who were at least second, and 11 of them (14.7-percent) were their team’s single most targeted player. Leonard Fournette is the only running back in the last 15 years to earn at least 75 targets in a season while finishing outside the top four on his team in targets. And it took his Buccaneers throwing the ball a league-high 741 times (their 43.0 pass attempts per game were the third-most of any team in any season going back to 2007) to get him there in 2021.

Within this population of 75-plus target runners (the numbers obviously might look different if we examined every running back who saw the field in the last 15 years, not just those with high-volume receiving roles), the stats that most correlated with total targets in their high-target season were:

At least among high-volume target earners at running back, the most predictive metric to hone in on in determining just how high a player’s target total might climb is that player’s place in their team’s target pecking order. It’s intuitive that players who are higher on their team’s passing game hierarchy are more likely to have high target totals.

In order to receive a lot of targets, a player must be talented enough to earn them, and the next most predictive metric is Receiver Rating. Followed by that is the team’s total passing attempts, serving as more proof that it’s better to bet on players from high-volume passing offenses. It is less important to focus on how consolidated a team’s target distribution might be or the ability of other players to soak up targets to any particular degree. But if you were to give any weight to the sizes of a running back’s teammate’s roles, you’d prefer to have a running back from a team that doesn’t go four deep at wideout or that doesn’t have a target-hogging No. 1 option.

Ultimately though, this analysis further strengthened my conviction in focusing on player talent over situational factors when forecasting fantasy production.


We’re notoriously bad at predicting which teams will be good and which teams will be bad, and it’s impossible to know how a given factor will affect a particular team. So you might as well just bet on the best players. Based on the numbers, that’s certainly the case when it comes to identifying which running backs could vault their fantasy output to the next level via high involvement in the passing game. Pay no mind to target distribution or surrounding talent. Just filter for the best players on high-volume teams and hammer away.

Outside the obvious Christian McCaffrey and Alvin Kamara-type candidates going into next season, would-be first-timers I’d say have a shot at 75-plus targets based on talent and 2021 passing volume are Clyde Edwards-Helaire, Travis Etienne, and Michael Carter, with Kenny Gainwell as my favorite super darkhorse.

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