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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 tight ends.

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, running back aging, or quarterback aging, this will look familiar, so feel free to skip down to “Unique Challenges Facing Tight End 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 38.1% of players who were superficially similar to Jason Witten “died” at age 33, that doesn’t mean Jason Witten has an 38.1% chance of “dying” this year.

This is really easy to demonstrate conceptually. Tony Gonzalez is among the pool of players who are “superficially similar” to Jason Witten. If Tony Gonzalez had ruptured his achilles at 33 and never played again, the pool of comparable players would have a higher observed “death rate”. But would Tony Gonzalez rupturing his achilles in 2009 make it more likely that Jason Witten declined today? Of course it would not.

Similarly, if Steve Jordan hadn’t suffered a career-ending injury at 33, it wouldn’t make Jason Witten less likely to suffer a career-ending injury 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 Jason Witten has 1.22 more “Expected Years Remaining”, this doesn’t mean it thinks Jason Witten in particular has 1.22 years left. It’s not predicting that he’s going to fall off a cliff early in the 2016 season. Instead, it’s saying “if you had 1,000 tight ends like Jason Witten, then you’d expect the average remaining fantasy-relevant career length from the sample to be a bit over one year”.

Even that 1.22 average isn’t a specific prediction. We would expect only 65% of the 1,000 tight ends in the sample to “die” in either 1 or 2 years. We’d anticipate that 171 of those tight ends would still be productive at 35. Five of them would make it all the way to 37.

This is very important: we are not predicting specific outcomes. We are simply measuring risk. I don’t know how Jason Witten’s career is going to play out. He could be the next Tony Gonzalez, playing at a pro bowl level all the way up to his retirement at 38. He could be the next Steve Jordan and only have four games left in his career.

There are a lot of possible paths Jason Witten’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 37-year-old tight ends age based on historical comparables, there’s only one player I can compare to. Tony Gonzalez is the only tight end to produce any value at age 36 or later. The problem is that Tony Gonzalez is an outlier, so how he aged is not going to be representative of how some other quarterback 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. Tony Gonzalez is probably not going to be representative of how a typical tight end will age… but once a tight end reaches age 37 in the first place, we already know he’s not a typical player. Suddenly Gonzalez becomes a much more reasonable point of comparison.

This culling effect really kicks by the mid-to-late 20s. By that point, any player 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 majority of young tight ends aren’t any good. Included in the sample are a lot of late-round fill-ins receiving playing time because they’re cheap and they can help out on special teams.

Obviously this doesn’t represent a good group of comps for someone like Travis Kelce or Rob Gronkowski. We already know that those players are pretty good. I’d be willing to bet that, barring catastrophic injury, both players will still be starting in the NFL four years from now.

In order to generate decent comps, I limited myself to looking at the top 30 retired fantasy tight ends 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. The biggest 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 tight end has 5.88 expected years remaining, I don’t mean that Jace Amaro, 23-year-old tight end for the New York Jets, has 5.88 expected years remaining.

Jace Amaro’s first two seasons have been uneven and injury-marred, 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 Amaro.

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. Even highly-drafted tight ends like Maxx Williams are probably not great candidates for this analysis. Later round players like Jeff Heuermann would find their career prospects dramatically overstated by this methodology.

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 tight ends. I would not be surprised if these EYR numbers underestimated the remaining careers of 32+ year old tight ends to some degree or another.

Unique Challenges Facing Tight End Models

The wide receiver and running back models were written up first because they’re generally the two cleanest positions for this sort of analysis. Quarterbacks presented their own challenges, but none of those challenges were fundamental to the appropriateness of historical modeling.

Modeling tight ends based on historical trends, however, faces a foundational problem. Put simply, modern tight ends bear little resemblance to historical tight ends.

Consider, for example, Steve Jordan. Jordan was a very good tight end, earning six pro bowl appearances and finishing as the 7th-most-valuable retired player over the last 30 years. From 1986 to 1991, Jordan notched six consecutive top-6 finishes at his position. In 1989, for instance, Jordan was the #4 tight end in all of fantasy football. He finished that year with 35 receptions, 506 yards, and 3 touchdowns. Again, this was good enough for a pro bowl berth and a 4th-place fantasy finish.

For much of the past thirty years, teams simply did not involve their tight ends in the passing game. Players earned top-10 fantasy finishes for totals that would not crack the top 20 in the modern game. Compare Steve Jordan’s 35/506/3 receiving line in 1989 to Scott Chandler’s 47/497/3 receiving line last year. Scott Chandler finished as the 20th-best fantasy tight end.

The lack of tight ends who were receivers first and blockers second in the past limits the sample size from which we draw our conclusions. Sharp-eyed readers might have noticed that I only used the top 30 fantasy tight ends of the past 30 years, after using the top-50 players at each other position.

This is because the top 50 includes players like Jermaine Wiggins, who played seven seasons for five teams and finished his career with 2141 receiving yards. Jermaine Wiggins simply isn’t a good point of comparison for a modern pass-catching tight end.

Even with the smaller sample, many players simply aren’t terrific comps. The role that a tight end plays in an NFL offense is just too different today. Jimmy Graham lines up at wide receiver as much as he lines up at tight end. Such a split would be inconceivable for even the most prolific of pass-catchers twenty years ago.

To get around this problem, in addition to running the numbers strictly for tight ends, I have created a “blended EYR” chart. This blended chart combines tight ends and receivers over the last 30 years and re-calculates EYR.

The blending is something of a post-hoc solution to the problem, but I feel it is a reasonable and effective one. If today’s tight ends are really hybrids between the tight ends and receivers of years past, I suspect a hybrid sample will better predict modern aging trends.

Enough Talk, Let’s See Some Numbers

Based on a best-fit curve covering all relevant tight ends over the last 30 years, this is how quality tight ends 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.

The first table lists historical aging patterns of tight ends alone. The second table lists the same historical aging patterns if we combine tight ends and wide receivers to create a “blended” mortality table.

Tight Ends Only:

"Blended" Sample: 

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