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 wide receivers.
Before I get to the results, I want to first discuss the process and its limitations. If all you care about is the numbers, feel free to jump down to the table at the bottom.
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 17.2% of players who were superficially similar to Calvin Johnson “died” at age 30, that doesn’t mean Calvin Johnson has a 17.2% chance of “dying” this year.
This is really easy to demonstrate conceptually. Randy Moss is among the pool of players who are “superficially similar to Calvin Johnson”. If Randy Moss had ruptured his achilles tendon at 30 and never played again, then the pool of comparable players would have a higher observed “death rate”. But would Randy Moss rupturing his achilles in 2007 make it more likely that Calvin Johnson declined today? Of course it would not.
Similarly, if Sterling Sharpe had stayed healthy, it wouldn’t make Calvin Johnson less likely to decline 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 Calvin Johnson has a 2.54 more “Expected Years Remaining”, this doesn’t mean it thinks Calvin Johnson in particular has 2.54 years left. It’s not predicting that he’s going to fall off a cliff halfway through the 2017 season. Instead, it’s saying “if you had 1,000 receivers like Calvin Johnson, then you’d expect the average remaining fantasy-relevant career length from the sample to be about two and a half years”.
Even that 2.54 average isn’t a specific prediction. We would expect only 1/3rd of the 1,000 receiver sample to “die” in either 2 or 3 years. We’d anticipate that 95 of those receivers would still be productive at 35. 21 of them would still be productive at 37.
This is very important: we are not predicting specific outcomes. We are simply measuring risk. I don’t know how Calvin Johnson’s career is going to play out. He could be the next Jerry Rice and last until he’s in his 40s. He could be the next Sterling Sharpe and get cut down in his prime to a gruesome injury. He could be the next Marvin Harrison or Randy Moss and put up a top-2 fantasy season at age 34 or 32 before disappearing from relevance entirely. He could fall off the planet early, then re-emerge in his mid-30s like Joey Galloway.
There are a lot of possible paths Calvin Johnson’s 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 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 40-year-old receivers age based on historical comparables, there’s only one player I can compare to. Jerry Rice is the only receiver in history to play so much as a single snap after his 40th birthday. The problem is that Jerry Rice is an outlier, so how he aged is not going to be representative of how some other receiver 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 issue. We’re only comparing players to their peers once they actually reach that age. Jerry Rice is probably not going to be representative of how a typical receiver will age… but once a receiver reaches age 40 in the first place, we already know he’s not a typical receiver. Suddenly Jerry Rice becomes a much more reasonable point of comparison.
This culling effect really kicks by the late 20s and early 30s. By that point, any receiver who is still producing fantasy value is undoubtedly on his second or third contract in the NFL. He’s managed to stick around for the better part of 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 receivers, the actual bust rate is going to be massive. Why? Because the overwhelming majority of young receivers aren’t any good. Included in that sample are a lot of 5th string receivers 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 Odell Beckham or Mike Evans. We already know that those receivers 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 receivers 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 receivers who looked amazing but flamed out quickly do not get included in the sample. David Boston and Germane Crowell are relevant comparisons to Mike Evans and Odell Beckham, but neither receiver 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 receiver has 7.01 expected years remaining, I don’t mean that Darren Waller, 6th-round-pick of the Baltimore Ravens, has 7.01 expected years remaining.
We don’t have any idea whether Waller 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 Waller.
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 Amari Cooper might be a good candidate, although not a perfect one. But our expectations for rookies drafted in the 2nd round or later, or for young and still-unproven receivers, 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 receivers. I would not be surprised if these EYR numbers underestimated the remaining careers of 30+ year old receivers to some degree or another.
Enough Talk, Let’s See Some Numbers
Based on a best-fit curve covering all relevant receivers over the last 30 years, this is how quality wide receivers 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.