Most of my work in evaluating running backs centers around their ability to run the football, because, well, most of the job of a running back is centered around running the football. They also do other things, though, and today I want to focus on perhaps the next-most consequential of their responsibilities: running routes and catching passes (though we shouldn’t forget that pass-blocking is just as important in many cases).

There’s a decent chance that my longwindedness will get the better of me, but my intention is for this article to serve more as a data dump useful for future reference (both for myself as well as for readers to self-reference in the meantime between now and my getting around to further analyzing these things in future articles) than as a prose-heavy think piece like much of my other stuff. The data being dumped will be of several varieties: simple, volume-based statistics like receptions, receptions per game, and Target Share; various efficiency metrics (yards per reception, yards per target, YAC per reception); and usage indicators like aDOT, positional alignment data, and – unique to this website and my own methodology – measurements of Route Diversity and Route-Adjusted Target Earnings that contextualize the receiving production and efficiency of running backs in ways that exceed the scope of traditional, route-blind metrics in their ability to inform our understanding of players’ receiving skill-sets (rather than simply their receiving results).

Let’s start with the most basic of data-dork fixations:

Target Share here refers to a player’s career-high mark, while Satellite Score is a metric that contextualizes a player’s Target Share (over the course of his career) with his Dominator Rating; higher isn’t necessarily “better” in this case, but simply indicates that a player’s overall role in his particular offense skewed toward receiving contributions. That provides insight from a couple different angles. For one, it can help differentiate between Target Shares that would seem to indicate equivalent receiving acumen; Jonathan Taylor and Chris Thompson both left school with career-high Target Share marks of 10.5%, but that number produced a Satellite Score of 59.6 in the case of Thompson and a 17.4 in the case of Taylor (this suggests that Taylor was involved in the passing game mostly by virtue of his simply being a big part of the offense in general, while Thompson’s receiving production was a fundamental part of his contributions). Along similar lines, the metric can illuminate players whose Target Shares over- or under-represent their receiving abilities; James Cook’s career-high Target Share at Georgia was 7.8%, a 39th-percentile mark that produced – in light of his small-ish role in the offense overall – a 92nd-percentile Satellite Score of 56.2.

Let’s bullet-point some quick notes and analysis based on the above numbers:

Now onto more interesting considerations: not how much, but how were these backs used by their teams in the receiving game? While the classic caveats – “it’s not that he can’t do it, he just wasn’t asked to” – certainly apply in some instances, the positional alignment, target distance, and route inventory of these backs can give us an excellent view into how their coaching staffs believed they might best impact defenses in the passing game. Guys who regularly line up out wide or in the slot, or who get targeted downfield, or who run a wide variety of different route types, typically don’t get to do those things by accident.

I’ll quickly define a few of these metrics and then move on to more notes and analysis. Basic route percentage refers to the portion of a player’s total routes run (over the course of his entire career) that were made up of basic route types: screens, flat routes, swing routes, and the like. Guys with higher numbers in this category are often your screen merchants and checkdown funnels, while those with lower numbers were being asked to do advanced stuff much more often. Route Diversity is a closely-related metric that measures the overall variety of a player’s route inventory, essentially indicating levels of versatility (as opposed to one-trick-pony-ness). These statistics use charting data from Sports Info Solutions. Here are some quick thoughts:

Now that we know where these players lined up and the general sorts of routes they ran, let’s examine how successful they were at earning targets on those routes. Route-Adjusted Target Earnings is a metric that indicates – relative to nation-wide targets per route run data unique to each route type – how frequently a player was being targeted given the unique makeup of his individual route tree. Marks above 100% indicate a player was being targeted x% more often than expected on a per-route basis, and vice-versa for marks below 100%.

It’s worth keeping in mind that these numbers can have just as much to do with offensive scheme, surrounding skill-position talent, and quarterback tendencies as they do with the skill of the running back running the routes, especially in the case of basic route types (Caleb Williams and Jayden Daniels probably aren’t looking to dump it off in the flats very frequently). Still, guys who are targeted more often than the national average – especially on advanced routes – are probably doing something to earn those targets at high per-route rate. By contrast, it often makes sense to be skeptical of backs who – nearly regardless of their raw volume numbers – are being targeted far below the national per-route averages.

I don’t think these numbers beg further explanation, so I’ll jump right into the notes and analysis:

Lastly, let’s take a look at simple measures of efficiency; who catches the ball cleanly, who gains yards at positive rates, and who makes things happen after the catch:

“True” Catch Rate here refers only to a player’s catch percentage on targets that Sports Info Solutions deemed catchable, and “true” yards per target considers a player’s total receiving yards divided by his total catchable targets. Our last batch of notes: