Age and Batting Performance Continued

Since my earlier post, I have had some more time to analyze the data and examine a few other studies on aging in baseball. The literature tends to support the conventional wisdom that players peak around 27. Here are some links to studies and their predictions of peak age. Keep in mind that this is a brief summary on my part and my reporting does not reflect the caveats of the authors. I think all of these studies are good and anyone who is interested in the issue should read them.

Bill James (from the 1982 BJHA): 27 is the peak including all offense and defense. This is a must-read article if you can get your hands on it. Luckily, I have a friend who loaned me his tattered copy.
David Luciani: 24-26
Don Malcolm: Doesn’t really take a stand on this issue, but it is an interesting study on age and performance by hitting components.
Tangotiger: 27
Keith Woolner: 25-28, but probably a little less than 27.

Given my earlier results, I was a bit concerned. My model seems to produce different conclusions, but I have quite a bit of confidence in my empirical approach. So, I decided to break out the numbers in several different ways. The two most logical ways to parse the data are by player quality and career length. Recall, I only include player seasons where the batter has more the 300 ABs. The first table reports the results for players with career OPS+s of <80, <90, <100, >100, >110, and >120.

Variable All Players <90 <100 >100 >110 >120
Age 8.876 7.792 8.737 9.109 9.953 10.275
Age^2 -0.152 -0.135 -0.149 -0.156 -0.170 -0.162
Lag(OPS+) -0.261 -0.253 -0.303 -0.242 -0.226 -0.109
R-sq. 0.3 0.35 0.35 0.27 0.32 0.33
Obs. 2848 321 1203 1645 576 151
Players 621 103 332 289 79 21
Peak Age 29.21 28.88 29.37 29.2 29.28 31.62

The results indicate that players of different quality age the same, except for the superstars (>120). While most other players peak at age 29, the superstars peak between 31 and 32.

Now, I want to estimate the model on players with different career lengths. Maybe there is some bias from bad players leaving early and good players sticking around. In all of these samples I include players with at least two consecutive seasons with 300 ABs. The career seaons categories are 5 or less, 10 or less, 5 or more, 10 or more, and 15 or more.

Variable All Players 5- 10- 5+ 10+ 15+
Age 8.876 12.212 9.780 8.872 8.906 9.414
Age^2 -0.152 -0.238 -0.177 -0.152 -0.150 -0.155
Lag(OPS+) -0.261 -0.452 -0.299 -0.259 -0.228 -0.340
R-sq. 0.3 0.4 0.32 0.31 0.28 0.23
Obs. 2848 434 1755 2651 1307 252
Players 621 250 515 467 135 18
Peak Age 29.21 25.64 27.57 29.22 29.62 30.29

Players with shorter careers seem to peak earlier than players with longer careers. This is not surprising since these players are likely to be the weakest of the talent pool, plus they do not get the opportunity to improve since they are no longer in the league. But, that number 29 keeps popping up as the peak age for players, and this runs against the conventional wisdom.

I have three explanations for my difference. First, I use OPS as a measure of offense, and most of the studies discussed above use other measures. If you think OPS is a bad measure of offense, then my study probably doesn’t mean that much to you. Second, I focus on a relatively modern sample, in which players are playing longer due to better nutrition, conditioning, and medical technology. Third, I may have made a data error somewhere in the mix. While I doubt this, I do plan to double-check my numbers to make sure I did not make a mistake in generating. I will update this if I find out this is the case. I plan to think more on the subject, and I hope others do as well. Please feel free to pass along any comments.

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