My previous FanPosts projecting Adrian Peterson's production over the next few years, one based on age and the other based on number of carries, made it pretty clear to me that either one was insufficient by itself. And while I indicated in the second post that I would not try to merge the two, curiosity won out—so here we are with post #3. Without further ado, and with the same resignation to inevitability as displayed in the opening scene of Family Guy's "It's a Trap!" episode, I give you "What to expect out of a (once?) elite RB with age and usage".

Methods

This analysis ended up being a little different from the other two for a couple of reasons. First, I used deviations from per game production in the other two cases, but that was much more problematic here; the main difficulty is that to use deviations, one needs to have a standard reference point from which to deviate. In this case, there was no obvious consistent choice (like career prior to age 30 or cumulative number of career carries). So, I based my projections on overall season production, specifically yards from scrimmage (which I call Yscm, and is conveniently provided by PFR). Which brings me to the second difference in methodology: I chose to factor in receptions this time (to generate total number of touches, T). After all, both rushing attempts and receptions lead to running and tackling, so both constitute usage. To maintain some sort of efficiency metric, I examined yards per touch (Y/T) as well. The third main difference is that I also tried to project some expectations for time lost due to injury, which for aging players becomes increasingly significant.

The first issue which I had to address was how exactly to define the concept of "age + usage". I opted to create a new quantity, which I call Age+, to account for both quantities. The age piece is easy—all one needs is a date of birth and a calendar (or PFR's "age" data). For usage, I assigned 300 touches a value of one year. This was an arbitrary decision on my part, but I chose it for two reasons: 1) in a 16 game schedule, that's ~19 touches per game, which is a fairly average usage rate for a starting RB; and 2) a healthy RB would peak after ~6 years by usage, just as he reaches physical peak (~28 years old) after ~6 years in the NFL. To generate the Age+ number, I then simply added the number of age-based years to the number of usage-based years.

I started with the same group of 45 players i used previously (i.e. top 30 in career rushing yards + other HOF RBs). I then created an adjusted (adj) group by removing those players who never reached at least 2000 career touches*, which reduced the pool by 12 (to 33, including AP himself). The players thus removed from the list were almost all pre- or early-Super Bowl era (among those removed, Larry Csonka last played in the NFL, in 1979), so this adjustment basically served to reduce the pool to only RBs from the last few NFL generations (which seems like a fair result).

*Larry Csonka just missed the cut; he accumulated 1997 touches in his NFL career, not including those from his year in the nascent WFL. However, I wasn't comfortable that WFL production was equivalent to NFL production, and figured his inclusion wouldn't be pivotal for overall trends in the data—so I left him out.

Comparison to the whole adjusted group

After calculating Age+, I plotted Yscm, T, Y/T, and games missed (as a percentage, to account for impact on seasons of different length; %Gmiss) as a function of Age+ for the entire adjusted group, as shown below.

The small black dots are individual player seasons; the red triangles are averages over one-year Age+ intervals (e.g. the average of all seasons in which a player started with an Age+ between 21 and 22, 22 and 23, 23 and 24, etc.); the blue Xs are AP's seasons; and the green circles are projections for AP based on the the fit (I used second order polynomials, because the data—especially Yscm and T—are clearly nonlinear)—more on those below. I didn't extend the averages/trendlines beyond Age+ of 40 (or, more precisely, I averaged all >39 seasons into one value), because the number of such seasons, combined with their variability, basically make them noise. There are a couple of things I'd like to point out about these plots.

First, I think it's worth noticing that AP's career numbers straddle the trendlines pretty well, which suggests that he has not been an exceptional RB compared to other HOF and/or highly productive RBs. Exceptional for one season, yes; compared to the pool of all NFL RBs, absolutely—but on average, a "fairly typical" elite RB. If he were truly exceptional, even among the greats, one should expect his data points to lie at a level consistently higher than the trendlines over the duration of his career. That's not a knock on AP, just a recognition that while he certainly has a place among them, he hasn't shown that he is significantly better than the best (and there's no shame in that).

Second, the average elite RB peaks at an Age+ of ~30; AP started his monster 2012 year at an Age+ of 32.14, which is reasonably near the average peak. Furthermore, this analysis rationalizes, at least in part, why he was so good that year in particular: he was at/near his physical peak (he entered the season at 27) and was at/near peak effectiveness (he entered the season with 1543 career touches, just entering the prime zone before the ~2k falloff). In other words, his age and experience worked in concert to make him a peak player in both regards simultaneously; add in both an offense that had to rely on him heavily because they had no real passing game and the fact that he was healthy the whole season, and we have a perfect storm of RB production. (Incidentally, that also puts into perspective just how hard it will be for anyone to eclipse Dickerson's single-season rushing record; it's not impossible, but it will be very difficult.)

Third, the data clearly show (or at least as clearly as such a variable data set can show) that time lost due to injury is expected to increase as a RB gets older and/or sees more usage. We talk about this phenomenon anecdotally, but it seems to be true—even for the elites.

Identifying a peer group

Once again, I wished to identify a smaller peer group with a career arc most similar to AP's. To do so, I considered yards from scrimmage (with a weight of 0.3), touches (0.35), yards/touch (0.25) and percentage of games missed (0.1)**. The resulting peer group (nine players) is summarized below.

**Warning: statistics ahead! The weights were applied after scaling the residuals to similar magnitudes.

Player Score Dickerson, Eric 1.25 Campbell, Earl 1.26 Tomlinson, LaDainian 1.43 Allen, Marcus 1.55 George, Eddie 1.91 Anderson, Otis 2.21 Portis, Clinton 2.26 Thomas, Thurman 2.61 Lewis, Jamal 2.63

For a third time, AP is in pretty good company (seriously though, who had Eddie George making the cut a third time!). The same plots as shown above, but considering only the peer group, are shown below.

The overall shape of the plots is basically the same as those for the entire adjusted group; only the actual fits are recognizably different.

Predictions

With the model equations in hand, it's now time to project for AP's next few seasons. In addition to projecting Yscm, T, and Y/T, I calculated the number of games one would expect him to play (from %Gmiss), which I then used to predict his per-game usage (i.e. touches per game, or T/G) and yards from scrimmage (Y/G). When appropriate, I added (green) points to the plots above to show where along the trendline the projections place AP***. The results of both sets of projections are below.

***I used the projected number of touches to calculate Age+ for each subsequent year to keep the projections self-consistent.

Projections based on the entire adjusted group:

Year Age Age+ Yscr T Y/T G T/G Y/G 2015 30 36.54 1059 237 4.40 14.0 16.9 75.7 2016 31 38.33 864 200 4.28 13.9 14.5 62.4 2017 32 40.00 653 160 4.18 13.7 11.7 47.6 2018 33 41.53 432 118 4.07 13.6 8.7 31.8

Projections based on the peer group:

Year Age Age+ Yscr T Y/T G T/G Y/G 2015 30 36.54 1072 251 4.34 13.6 18.5 78.8 2016 31 38.38 880 217 4.26 13.4 16.2 65.9 2017 32 40.10 678 180 4.18 13.1 13.7 51.6 2018 33 41.70 471 142 4.12 12.9 11.0 36.4

AP's peer group does predict slightly higher productivity than the entire adjusted group—but either way, the numbers show a pretty steep decline in production.

One other item of note: I looked at the Age+ at which each player entered his final season. The average for the entire adjusted group was 39.39****; that of AP's peer group was 35.08 (and notice that Barry Sanders and Jim Brown, the two players that are typically recognized as having retired too early, are not in this peer group). Playing the historical precedent card, it is not at all far-fetched to believe that AP may be hanging up the ole' cleats sometime in the next three years (he enters the 2015 season with an Age+ of 36.54).

****Emmitt Smith retired with an Age+ of 51.4 (!); the next closest player was Marcus Allen at 49.0. If Dickerson's single-season rushing record will be hard to beat, what will it take to become the all-time rushing yardage leader??

Conclusions

This analysis is really the most complete of the three I've done. The projections seem a bit low to me, especially for the coming year; as I've said, I expect he'll post ~1200 rushing yards, maybe ~1500 total yards this year (primarily because I don't think his Y/T will fall off quite that quickly). Apart from that, the larger story the numbers tell—how quickly the average once-elite RB falls off the infamous "cliff"—is pretty sobering. Injuries pile up, usage and productivity decrease; retirement is lurking just around the corner. If AP is to do anything different, he will definitely need to be an outlier in the landscape of HOF-level NFL RBs.

And with that, I'm done analyzing AP's future.

I think.