Dynasty, in Practice: How Do Running Backs Age?

An attempt to model running back aging patterns based on 30 years of historical data.

In an article last month, I made the case that we’re thinking about age in entirely the wrong way. The traditional thinking on aging is that it resulted in a steady and inevitable decline. After digging through 30 years of NFL history, however, I found that the “steady decline” model did not accurately represent what was actually happening to players on the field.

Instead, a better way to think of player aging is as a stable equilibrium prone to dramatic, sudden, and unexpected drops. Players essentially remain productive until one day when they’re not any more.

While I laid out evidence in favor of a new model of player aging, I didn’t actually provide specific data on what that model might look like. Today, I’ll examine 30 years of fantasy history to design a model that best represents observed aging patterns for running backs.

Before I get to the results, I want to first discuss the process and its limitations. If you’ve already read my piece on receiver aging, this will look familiar, so feel free to skip down to “Unique Challenges Facing Running Back Models”. If all you care about is the numbers, you’ll find them in a table down at the bottom of the article.

A Quick Primer On Terms

Instead of an “aging curve” model, where I predict how much players will improve or decline at any given age, I will be creating a “mortality table” model. Mortality tables are used most prominently in life insurance, where insurers want to make educated guesses on how likely a person is to die over any particular time span.

It’s important to note that this model is not actually making predictions about the players involved. Instead, I’m merely measuring observed patterns among players who were superficially similar. If 25.5% of players who were superficially similar to Jamaal Charles “died” at age 29, that doesn’t mean Jamaal Charles has a 25.5% chance of “dying” this year.

This is really easy to demonstrate conceptually. Emmitt Smith is among the pool of players who are “superficially similar to Jamaal Charles. If Emmitt Smith had ruptured his achilles tendon at 29 and never played again, then the pool of comparable players would have a higher observed “death rate”. But would Emmitt Smith rupturing his achilles in 1998 make it more likely that Jamaal Charles declined today? Of course it would not.

Similarly, if Shaun Alexander hadn’t lost a step seemingly overnight, it wouldn’t make Jamaal Charles less likely to lose a step today. These are different players whose careers are wholly independent, and what happened in one case really has no bearing on what will happen in the others.

Likewise, if the model says Jamaal Charles has 1.70 more “Expected Years Remaining”, this doesn’t mean it thinks Jamaal Charles in particular has 1.70 years left. It’s not predicting that he’s going to fall off a cliff halfway through the 2016 season. Instead, it’s saying “if you had 1,000 backs like Jamaal Charles, then you’d expect the average remaining fantasy-relevant career length from the sample to be a bit over a year and a half”.

Even that 1.70 average isn’t a specific prediction. We would expect only 46% of the 1,000 running back sample to “die” in either 2 or 3 years. We’d anticipate that 156 of those backs would still be productive at 32. Three of them would still be productive at 35.

This is very important: we are not predicting specific outcomes. We are simply measuring risk. I don’t know how Jamaal Charles’ career is going to play out. He could be the next Emmitt Smith and put up startable fantasy production for a horrible franchise at age 35. He could be the next Shaun Alexander and spend the rest of his very short career as a bare shadow of his former glory. He could fall off the planet, then re-emerge in his mid-30s like John Riggins.

There are a lot of possible paths Jamaal Charles’ career could take. Nobody knows what will happen, and anyone saying otherwise is misleading you. I’m not trying to make a prediction, I’m simply trying to clearly outline the risks so that owners can make an informed decision. I’m not dealing in certainty, I’m merely trying to quantify the uncertainty.

A Note On Methodology

One problem anyone will have to grapple with when dealing with aging is called survivorship bias. In short, it states that the survivors of a process are not necessarily representative of all members of the process.

If I want to model how 37-year-old backs age based on historic comparables, there’s only one player I can compare to. Marcus Allen is the only back in history to top 500 yards at 37 or older. The problem is that Marcus Allen is an outlier, so how he aged is not going to be representative of how some other back will age.

To a large extent, the advantage of a mortality table model is that it’s self-culling, allowing us to sidestep the survivorship bias. We’re only comparing players to their peers once they actually reach that age. Marcus Allen is probably not going to be representative of how a typical back will age… but once a back reaches age 37 in the first place, we already know he’s not a typical back. Suddenly Marcus Allen becomes a much more reasonable point of comparison.

This culling effect really kicks by the mid-to-late 20s. By that point, any back who is still producing fantasy value is undoubtedly on his second contract in the NFL. He’s managed to stick around for half a decade. He’s probably a pretty good player.

In the early 20s, finding good points of comparison is substantially harder. If you looked at all 21-25 year old backs, the observed bust rate is going to be massive. Why? Because the overwhelming majority of young backs aren’t any good. Included in that sample are a lot of 5th string backs drafted in the late rounds or undrafted entirely, guys who largely play special teams on tiny rookie contracts, never receiving a second deal in the NFL.

Obviously this doesn’t represent a good group of comps for someone like Le’Veon Bell or Eddie Lacy. We already know that those backs are pretty good. I’d be willing to bet that, barring catastrophic injury, both players will receive a second contract to be a starter somewhere in the NFL.

In order to generate decent comps, I limited myself to looking at the top 50 retired fantasy running backs since 1985. (The reason I’m only considering retired players should be self-evident.) This generates a pretty good list of guys who are largely “second-contract” type players.

This method is not without its flaws, though. There is going to be some selection bias, where young backs who looked amazing but flamed out quickly do not get included in the sample. Barry Foster and Bobby Humphrey are relevant comparisons to Le’Veon Bell and Eddie Lacy, but neither back made the cut.

The other flaw is that the conclusions from this method really only apply to guys who we already believe are “second-contract” type players. If I say that the average 23-year-old back has 5.18 expected years remaining, I don’t mean that Terron Ward, 23-year-old undrafted free agent for the Atlanta Falcons, has 5.18 expected years remaining.

We don’t have any idea whether Ward is any good yet, so a better set of comparables would be the larger list that includes all of those failed rookies who never received second contracts. Because right now, that career path is still very much a viable possibility for Ward.

In short, be careful when using these EYR values. They’re really only meant to apply to players who we already have strong reason to suspect are probably pretty good. A highly-drafted rookie like Todd Gurley might be a good candidate, although not a perfect one. But our expectations for rookies drafted in the 3rd round or later, or for young and still-unproven backs, should be much lower.

One final word of caution. These numbers model what happened between 1985 and 2014. They do not account for the possibility that things are dramatically different today. Improvements in modern medicine, for instance, could very well extend the careers of aging backs. I would not be surprised if these EYR numbers underestimated the remaining careers of 28+ year old backs to some degree or another.

Unique Challenges Facing Running Back Models

While the wide receiver model was fairly straightforward, there is one unique challenge that faces any attempt to model running back aging. In my original piece on how to model aging, I mentioned that running backs, unlike receivers, did indeed exhibit a small decline in the year before they fell out of fantasy relevance entirely.

This model does not currently account for that decline. As a result, running backs will be marginally riskier bets than the raw numbers suggest, because in addition to the risk of catastrophic decline, there is a further risk of marginal decline. Further, backs who experienced a decline last year will be at greater risk of “dying” than the table predicts, while backs who held steady or improved will be safer than the raw numbers suggest.

When I have time after the season, I will revisit the model and work on incorporating these observations to improve its accuracy. For the time being, this is merely something to make a mental note of while using the table below.

Enough Talk, Let’s See Some Numbers

Based on a best-fit curve covering all relevant backs over the last 30 years, this is how quality running backs age in the NFL. DR% stands for “Death rate”, and measures the chance that a receiver at that age will suffer a catastrophic and career-ending decline.

EYR stands for “Expected Years Remaining”, and represents a weighted average of remaining career lengths based on observed data. The estimated career length refers only to how long a player is expected to remain fantasy relevant— players can and typically do play several additional years at the end of their career where they are still on an NFL roster but no longer producing startable fantasy value.

Age 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
DR% 3.3 4.3 5.6 7.2 9.3 11.9 15.4 19.8 25.5 32.9 42.3 54.5 70.2 90.3 100
EYR 6.68 5.91 5.18 4.49 3.84 3.24 2.68 2.17 1.70 1.29 0.92 0.60 0.33 0.10 0

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