### The Bradbury aging study, re-explained (Part II)

This is a follow-up to my previous post on J.C. Bradbury's aging study ... check out that previous post first if you haven't already.

My argument was that players with shorter careers should peak earlier than players with longer careers. Bradbury disagreed. He reran his study with a lower minimum, 1000 PA instead of 5000. He found that there was "no drop".

I decided to try to run his study myself, the part where he looks at batter performance in Linear Weights. I think my results are close enough to his that they can be trusted. Skip the details unless you're really interested. I'll put them in a quote box so you can ignore them if you choose.

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Technical details:

Here's what I did. I took all players whose careers began in 1921 or later, and looked at their stats until the end of 2008 (even if they were still active). They had to have had a plate appearance in each of at least ten separate seasons. In seasons in which their age was 24 to 35 (as of July 1), they had to have had at least 5000 plate appearances.

Any player who did not meet the above criteria was not included in the regression. Also, the regression included only seasons from age 24-35 in which the player had at least 300 PA.

Each of those seasons was a row in the regression. The model I used was:

Z-score this season = a * age this season + b * age^2 this season + c * career average Z-score + d * player dummy + constant + error term

I didn't include dummy variables for individual seasons (Bradbury's "D" term, if you look at his paper) or park factors. I think those would change the results only slightly.

Another difference I noticed later is that when I calculated the Z-scores, I used the standard deviation only of players who were 24-35 and had 300 PA. Bradbury, I believe, used the SD of all players, regardless of PA. Again, I don't think that affects the results much (although it makes his coefficients about twice as big as mine).

Finally, I'm not 100% sure that I did exactly what Bradbury did in other respects. The study is vague about the details of the selection criteria. For instance, I'm not sure if any ten seasons qualified a player, or only ten seasons of only 300 PA. I'm not sure if the player need 300 PA every season between 24 and 35, or if that didn't matter as long as the total was over 5000. So I guessed. Also, for Linear Weights, I used a version that adjusts the out for the specific season, whereas Bradbury used -0.25 for all seasons (and compensated somewhat by having a dummy variable for league/season).

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Anyway, here is my best-fit equation, followed by Bradbury's:

Mine: Z = 0.760 * age - 0.0133 * age^2 - 0.901 * mean - 10.6802 + dummies

J.C.: Z = 1.322 * age - 0.0224 * age^2 - 1.205 * mean + other stuff + dummies

These equations look different, but that's mostly because Bradbury used a different definition of the Z-score. If you look at the significance levels, they're similar: for mine, about 12 SDs; for Bradbury, about 11 SDs. Bradbury might be smaller because his regression was more sophisticated, with certain corrections that likely brought the significance down.

More importantly, our estimates of peak age, which can be calculated as - ( coeff for age ) / ( 2 * coeff for age^2 ):

Mine: 28.62 peak age

J.C.: 29.41 peak age

Why the difference? My guess is that there was something different about our criteria for selecting players for the sample. Again, I don't think the difference affects the arguments to follow.

Now, this is where J.C. says he ran the regression again, for 1000PA and no 10-year-requirement, and got no difference in peak age. I did the same thing, and I *did* get a difference:

Mine, for 5000 PA: 28.62

Mine, for 1000 PA: 28.06

It looks like a small difference, only .56 years -- and the total of 28.06 is still above the previous studies' conclusion that the peak is in the 27s. However, as it turns out, the way the study is structured, that small difference is really a big difference. Let me show you.

First, I ran the same regression, but this time only for players with 3000-5000 PA:

3000-5000 PA: 27.61

So, these guys with shorter careers did have an earlier peak, about a year earlier than the guys with the longer careers. What if we now look at the guys with really short careers, 1000-3000 PA?

1000-3000 PA: 147.00

That's not a misprint: the peak came out to age 147! But the coefficients of the age curve were not close to statistical significance -- neither the age, nor the age-squared. Effectively, these guys performed almost the same regardless of age. They didn't peak at 29, but neither did they peak at 27. They just didn't peak.

And so, it's reasonable to conclude that one of the reasons the peak age dropped so little, when we added more players like Bradbury did, is that the regression wasn't able to find the peak for the players with the shorter careers. And so the sample still consists of mostly players with longer careers.

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Can we solve this problem? Yes, I think so. The procedure cut off the sample of players at 24 and 35 years of age. If we eliminate the cutoff, the results start to work.

I reran the regression with no age restrictions: players had to have 5000 or 1000 PA anywhere in their careers, not just between 24 and 35. Also, I considered all seasons in which they had 300 PA, regardless of how old they were that year. The numbers are similar:

28.97 for 5000 PA+

28.66 for 1000 PA+

The difference is smaller now, 0.31 years. But the important result is the breakdown of the 1000+ group:

28.97 for 5000 PA+

27.72 for 3000-5000 PA

26.61 for 1000-3000 PA (now significant)

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28.66 for the overall sample

It seems like the shorter the career, the earlier the peak.

But, still, the overall average seems to only have dropped 0.31 of a year, and it's still around 29 years. Isn't that still evidence against the 27 theory?

No, it's not.

Take a look at the above table again: we have three peaks, 28.97, 27.72, and 26.61. Those three numbers average to 27.77. Why, then, is the "overall" number so much higher, at 28.66?

It's because there were a lot more datapoints in the 5000 PA+ category than the others. And that makes sense. The more PA, the more seasons played. And each season gets a datapoint. So the top category is full of batters with 10 or more seasons, while the bottom category is full of batters with only a few seasons. In fact, some of them may have only 1-2 qualifying seasons of 300 PA or more.

If a player has a 15-year career, with a peak at age 29, he gets fifteen "29" entries in the database. If another player has a 3-year career with a peak of 27, he gets only three "27" entries. So instead of the result working out to 28, which is truly the average peak of the two players, it works out to 28.7.

Another way to look at it: Player A has a 12-year career. Player B has a 2-year career. What's the average career? It's 7 years, right? And you get that by averaging 12 and 2.

But the way Bradbury's study is designed, it would figure the average career is 10.57 years. Instead of averaging 12 and 2, it would average 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 2, and 2. That's not the result we're looking to find.

This is less of a problem in Bradbury's original study, because, by limiting players to 12 years of their career, and requiring them to play 10 seasons, most of the batters in the study would be between 10 and 12 years, so the weightings would be closer. Still, this feature of the study means that it's probably overestimating the peak at least a little bit, even for that sample of players.

So, anyway, if 28.66 is not the right average because of the wrong weights, how can we fix it? Simple: instead of weighting by the number of regression rows in each group, we weight by the number of players in each group:

28.97 for: 640 players with 5000+PA

27.72 for: 595 players with 3000-5000 PA

26.61 for 1148 players with 1000-3000 PA

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27.52 overall average

So what looked like a small drop when we added the shorter-career players -- 0.31 years -- turns into a big drop -- 1.55 years -- when we weight the data properly.

Now, this only works when there actually IS a drop between the 5000+ and the 1000+ groups. We found a drop of 0.31. But on his blog, Bradbury said that with his data, he found no drop at all.

How come? I'm not sure. But one reason might be random variation (if he used different selection criteria). Another might be his age restriction causing nonsensical results in the important 1000-3000 group. And there are his other variables for "missed information resulting from playing conditions". Or, of course, I may have done something wrong.

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So we're down to 27.52. That's pretty close to the traditional estimates of 27ish. But I think we're not necessarily done: there are at least two factors I can think of that suggest that the real value is lower than even 27.52.

First, we showed that the regression overestimates the peak age by overweighting long careers relative to short careers. We were able to get the average to drop from 28.66 to 27.52 just by breaking the sample down and reweighting.

By the same logic, all three groups above must also be overestimates! In the middle group, players with 5000 PA are going to be weighted 67% higher than players with only 3000 PA. If we were to rerun the regression after breaking the group down further, into (say) 3000-4000 and 4000-5000, we'd get a lower estimate than 27.52. In fact, we could break those new groups down into smaller groups, and break those groups down into smaller groups, and so on. The problem is that the sample size would get too small to get reasonable results. But I'm betting the average would drop significantly.

Second, the study leaves out players with less than 1000 PA. That's probably a good thing, because with only 1 or 2 seasons, it's hard to fit a trajectory properly. Still, it seems likely that if there were a way of figuring it out, we'd find those players would peak fairly early, bringing the average down further.

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So, in summary:

-- If we use the Bradbury model on groups of players with fewer PA, we find that those players are estimated to have lower peak age. This supports the hypothesis that choosing only 5000+ PA players biases the result too high.

-- The model used in Bradbury's study consistently overestimates peak age for another reason. That's the weighting problem -- it figures the peak for the average *season*, not for the average *player*.

-- Correcting for that shows that if we look at players with 1000 PA, instead of just players with 5000 PA, the peak age drops to the mid 27s.

-- Other corrections that we can't make, because of sample size issues, would drop the peak age even further.

-- There is good evidence that the shorter the career, the younger the peak age.

-- It doesn't seem possible, with this method, to get a precise estimate of average peak age. "Somewhere in the low 27s" is probably the best it can do, if even that.

## 12 Comments:

"There is good evidence that the shorter the career, the younger the peak age"

While there might be evidence that the shorter the career the younger the peak age, I don't particularly find it either convincing or persuasive evidence.

Careers get cut short for any number of reasons, but I suspect that players with short careers tend to be done in by injuries. Injuries necessarily truncate a players career and make them fall off the cliff. This skews the data in that you've got a whole subset of a sample that isn't comparable to the players with longer careers.

The trick is to come up with a sensible and defensible way in which to select your sample. Bradbury's study does this I believe where as your selections in your post do not.

Christopher: Injuries do play a role. But you need to distinguish between 2 kinds of injuries. There are sudden, career-ending injuries (think Juan Encarnacion). It's reasonable to say these have nothing to do with aging. But they are also rather rare, and will have only a minor effect on Phil's analysis.

Then there are routine injuries that just reduce performance, in the short- or long-term. As players age, they get these injuries more often AND have a harder time recovering from them. To say this isn't part of the effect of aging on performance is not plausible or helpful. If you do, you are only analyzing the effect of aging on a very special injury-resistant subpopulation. And unless you can predict who those players will be, the analysis really has no value whatsoever. And this is essentially what Bradbury has given us here.

Moreover, we know that the vast majority of careers are ended simply by poor performance, not devastating injury. And these short careers tend to start around age 23-24, center on 27, and end around 30-31. If players are at their best at ages 29-30, why do these players get promoted so early? If Bradbury's model were correct, the most common career path we should see is players arriving in the majors around age 27-28, and exiting at 32-33.

And short careers should actually be rather rare. Because the aging curve is so flat (according to Bradbury), a player good enough to play at age 24 should typically last until about age 35. But that's not what we see at all. In short, the model just doesn't comport with reality.

Is there a difference in peak age between pitchers and hitters?

>"Is there a difference in peak age between pitchers and hitters?"

According to the Bradbury study, which is probably reasonably accurate for that group of players with long careers, here are the peak ages for the various stats for hitters and pitchers:

Hitters:

LWTS 29.41, OPS 29.13, OBP 30.04, SLG 28.58, BA 28.35, BB 32.30, 2B/3B 32.30, HR 29.89

Pitchers:

ERA 29.16, RA 29.05, K 23.56, BB 32.47, HR 27.39

A simple way to test Bradbury's thesis is to compare the performance of players who played at both age 27 and age 30 (300+ PAs). This removes the injury problem that arguably afflicts the modal peak approach -- these guys were all still playing at age 30. Bradbury projects a very small improvement in OPS over these ages, about 4 points. However, if we look at players who debuted in 1921 or later and played at these ages (933 players), we find that 55% of them had a higher OPS at age 27. Their OPS drops 10 points on average, rather than rising 4 points as predicted. (And the average league OPS change over these years was .000, so that's not a problem.)

Moreover, this is actually a comparison that should understate the real 27-to-30 performance decline, since a lot of 27-yr-olds decline enough that they aren't even getting 300 PA at age 30.

I think we can close this case.....

Anon/Phil: I don't think you can place a lot of stock in Bradbury's pitching numbers, even for his very limited sample of pitchers. He doesn't account for the "reliever advantage" of about 1.00 in ERA. I'm sure you would find that long-career pitchers throw a disproportionate number of relief innings when they are very young and very old, while mainly starting in their prime seasons. (If not, teams are using pitchers very poorly.) So this will serve to make the aging curves look much flatter than they really are.

It may be that the peak results would still look the same if you corrected for this. But my guess is it would push them earlier...

Hmmm ... that's a point. I still suspect that you can trust them relative to each other ... walks quite late, strikeouts very early. But, yeah, maybe we shouldn't put too much trust in the actual numbers.

Are you kidding? I applaud anyone who works hard to research their interests, but this is some real dubious work. The MATH looks sound, but the hypothesis is as flawed as any I've ever seen.

Of COURSE peak age is lower for shorter careers. A player with a shorter career, on average, will necessarily peak and, hence, begin to decline, earlier. That's why their careers are shorter!! To quote Adam Samberg, DUH!

If your career, with benefit of hindsight, was 5 years long, and someone else's was 15, is it not obvious that one would expect your peak to occur at an earlier age?

Simply put, short-career players don't last as long because they peak earlier and shallower, and decline earlier, than players who are actually good enough to stick around for a longish career. Well done disproving the other guy, but this is like losing 2 steps to gain 2: we're back where we started.

Talk about stating the blindingly obvious!

This comment has been removed by the author.

Brian B, I think the point is that Phil's thesis isn't obvious to everyone. And to prove the "obvious" one must have data to back up their claims. So phil does an amazing job with that.

Christopher, I disagree about your injuries point. If a career ending injury took place to a player in Phil's query said player probably wouldn't have a decline, they would be left out because they didn't have 10 consecutive seasons.

In case you're interested, my rebuttal.

Thanks! I saw it, been a little bit slow replying.

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