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Previous research conducted by Football Outsiders suggests that “defensive ends and defensive backs generally begin to decline after age 29, linebackers…after age 30, and defensive tackles after age 31. However, because we still have so few statistics to use to study…defensive players, this research should not be considered definitive” (Schatz et al., 2013, p. xi).

While the position of Football Outsiders on the aging curve for defensive players may have changed since the previous quotation, the research that I have done directly disproves this statement.

Whether it is because of an increase in sample size, inadequate methodology, or a result of the value metric used to ascribe player performance, my research, which uses the most credible methodology to date, shows that defensive players begin to decline at age 25.

Population & Value Metric

The sample of players used in this research are all NFL defensive players from 2007 to 2014 (the last few weeks of the 2014 season are absent from the data, but this doesn’t matter because of the adjustment that is made for playing time in the prescribed methodology that follows) that there are Pro Football Focus Overall Grades for.

Pro Football Focus Overall Grades provide us with an objective way to look at the comprehensive value provided by a defensive player. To get a better idea of what goes into these grades, you can read about them here.

Methodology

Conflation

My last post looked at the aging curve of MLB players with different strikeout profiles and used the same methodology as the research that was conducted for this post. If you would like to see that post, you can find it here, but I suspect that it would be beneficial to translate the general methodology that was explained in baseball specific terms, in the previous post, into a decidedly football vernacular.

Because this research uses the same methodology as my last post, you can skip this section and go straight to the results if you read the last post.

The most common mistake that people make when it comes to aging curves is that they conflate two separate issues: how a player played and how a player aged.

Suppose that you looked at the average grade for middle linebackers that are 29-years-old and 30-years-old. The 29-year-olds have an average grade of +6, and the 30-year-olds have an average grade of +10.

For starters, the 30-year-old linebackers are the linebackers that survived until their age 30 season. In order to survive in the NFL until your age 30 season, you have to be a talented player. Because these 30-year-olds have been able to play for so long and have the players at the bottom of their population filtered out over time, this means that we have a selection bias in the sense that the group of players that we’ve decided to look at are inherently talented because of their ability to survive.

As the current group of 30-year-old middle linebackers moved from their age 29 season to their current age 30 season, they lost players at the bottom of their population. These lost players are the linebackers that were too slow and/or weak to continue to play. This same group of players that could no longer continue to play is still present in the current group of 29-year-old middle linebackers, while they are absent from the current 30-year-old group of linebackers; this group at the bottom deflates the average grade of the 29-year-old players and inflates the average grade of the 30-year-old players.

Because we know that the 30-year-old linebacker’s higher Overall Grade average is a product of a selection bias, it doesn’t really make sense to look at the average of player seasons. The current group of 30-year-old linebackers, on average, is more talented than the current group of 29-year-old linebackers, but that doesn’t mean that they 30-year-olds weren’t more talented as a group the year before in the previous season—their age 29 season.

Sure, there may be a player here and there that had a better age 30 season than age 29 season, but, as a whole, the current population of 30 year old players will have played better the previous year when they were 29.

The Delta Method 

Now that we know that it doesn’t make much sense to look at the average Overall Grade of players in a specific age bracket, we can shift our focus toward the analysis of how a player’s performance changes over time.

This research use the delta method, which is summarized in this article by Jeff Zimmerman, who helped me throughout this process and whom I owe a big thank you to. Thank you.

The delta method looks at change over time. For our purposes, the delta method looks at the change in specific player performance from year-to-year and weighs that difference in year-to-year performance by the harmonic mean of the player’s snap totals for the two years that were used to find the difference.

Lets use Luke Kuechly as an example. Kuechly had a grade of +28.4 in 1,015 snaps as a 23-year-old inside linebacker. As a 22-year-old, Kuechly had an overall grade of +11.1 in 1,012 snaps as an inside linebacker.

Under the delta method, we would follow these steps with Kuechly’s stats:

Year 1: Overall Grade = +11.1 in 1,012 snaps at age 22

Year 2: Overall Grade = +28.4 in 1,015 snaps at age 23

Calculate the harmonic mean for snaps: 2/((1/Snaps_y1)+(1/Snaps_y2))=Snaps_hm

2/((1/1,012)+(1/1,015)) = 1,013.49

Weight the Overall Grade to the Snaps_hm:

(Snaps_hm/Snaps_y1) * Overall Grade_y1 = Overall Grade_y1_hm

(1,013.49/1,012) * +11.1 = +11.1 Overall Grade for Year 1

(1,013.49/1,015) * +28.4 = +28.3 Overall Grade for Year 2

Kuechly gained +17.2 in his Overall Grade over 1,013.49 snaps from age 22 to 23.

Because the number of snaps played were so similar in each season, the weight of the harmonic mean in the case of Kuechly’s age 22 and 23 seasons did not make much of a difference, but a measurable difference can be seen when players have a large variance in snaps from one season to the next.

After this has been done for all players and seasons, the differences in Overall Grade and snaps are added together and then adjusted to 500 snaps.

Results

The graph below shows the aging curve for NFL defensive players from 2007 to 2014.

defensive aging curve nfl players

 

(This graph and the conclusions were updated on 6.25.14 to reflect an error in the calculations. The shape of the curve stayed nearly identical, but now the peak age for defensive players is 23, not 25; with little difference between the two, both ages–23 and 25–were the top two ages in the previous chart.)

There are several takeaways that can be seen immediately:

  1. Defensive players peak at 23.
  2. Since most players enter the league as 22 year-olds, from this data, we can see that players are almost at  their peak when they come into the league. Beside the 35-36 population and the 36-37 population, we can see from the error bars that we can be fairly confident in the sample size of all age groups.
  3. The first appreciable drop off in performance happens at 28.
  4. After a player peaks at 23, their decline is noticeable until 33, at which point performance declines rapidly.
  5. The pinnacle for defensive players seems to be from 23 to 25

One of the first implications of this knowledge that comes to mind is with reference to rookie contracts. If a player enters the NFL after his senior year in college when he is 21 years old, that would make his rookie season his age 22 season. For all players, their services are under team control (i.e. their salaries are locked in place) for a minimum of four years, and for first round picks, their services can be tied down for up to five years.

That means that by the time a player finishes his rookie contract, he will have just finished his age 25 season at the end of his peak performance and reach the open market while he is already on the decline. For first round picks that are held onto for the additional fifth year of team control, these players only have one more year of peak-level performance after free agency before they begin a more rapid decline at 28.

Long term free agent deals for exorbitant sums of money seem to rarely work out, and more often than not they turn ugly quick; Jairus Byrd signed a 6 year deal worth $26.3M in guaranteed money as he entered his age 28 season last year. Not only did Byrd sustain a season ending injury four games into the season, but his performance dipped while he was on the field as well.

Albert Haynesworth signed his infamous seven-year $100M contract, worth an NFL record $41M in guaranteed money, as he entered his age 28 season, and we all know how that worked out.

These examples are the worst-case scenarios, and there is still a chance that Jairus Byrd will be able to bounce back, but it seems like it would be unwise to commit a disproportionate amount of your team’s salary cap to a player whose best years are behind him and is headed for a steep decline.

To state the former in another way, it seems like defensive players that are able to play into their 30s at high level outliers, so, if you’re an NFL team, a high stakes bet on your ability to predict an outlier doesn’t seem like a chance you should want to take.

Conclusions

Fantasy Football & 2015 NFL Draft
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Because of the limited sample of our population, this research is unable to look at position specific aging curves. Players at different positions most likely age at different rates, but until the research is actually conducted, any guess about the relative peaks of players, relative to their position, is purely conjecture.

At some point in the near future, I hope to be able to acquire enough data to be able to conduct not only position specific research for defensive players, but also look at the aging curves for offensive players. Until that work has been done, this research will be able to serve as a general guide for how defenders age and show how defensive players in the NFL peak earlier than most people probably thought.

Citations:

Schatz, A., Benoit, A., Connelly, B., Farrar, D., Fremeau, B., & Gower, T. et al. (2013). Football outsiders almanac 2013. [Australia]: CreateSpace Independent Publishing Platform.

Photo credit of Luke Kuechly to Ivan Football

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Devin Jordan

Devin Jordan is obsessed with statistical analysis, non-fiction literature, and electronic music. If you enjoyed reading him, follow him on Twitter!
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