from Amazon
Sunday, February 22, 2009
“True” aging patterns
Looking at performances of the batter/pitcher matchups, year-to-year, in the same parks, at the same age and in the same role in the same park (see chart below).
Age is the age of both the hitter and pitcher of the first year. wOBA1 is the wOBA at “age”, and wOBA2 is the wOBA at “age"+1. diff is the difference between the two wOBA. PA is the minimum of the PA at age 1 and age 2, for the matchup of the batter/pitcher. I not only matched on making sure it’s the same batter facing the same pitcher, but in the same park and in the same role (starter/sub-relief).
What this shows is how much of an advantage one has over the other. Presumably the selective sampling cancels out for both sides? I’d only look at the data between ages 22 and 34. It would seem that the batter always has the advantage until at least his early 30s. From age 33 to the end of their careers, the matchups was a cumulative .344 in the first year and .339 in the next year (on 5872 PA). This at least points to the possibility that pitchers peak later than hitters. However, if I bring it back to age 32 (I only selected age 33 because I looked at the data), it’s .341 in year 1, .342 in year 2, on 9,883 PA. That data suggests they age the same in their 30s.
What say you?
age wOBA1 wOBA2 diff PA
19 0.650 1.350 0.700 2
20 0.291 0.226 (0.065) 34
21 0.304 0.320 0.016 271
22 0.325 0.323 (0.002) 1,457
23 0.335 0.336 0.001 4,212
24 0.332 0.337 0.005 8,564
25 0.330 0.333 0.003 13,154
26 0.332 0.342 0.010 16,043
27 0.335 0.333 (0.002) 16,557
28 0.336 0.342 0.006 13,455
29 0.334 0.334 0.000 10,979
30 0.332 0.343 0.011 7,806
31 0.340 0.346 0.006 5,955
32 0.336 0.346 0.011 4,011
33 0.347 0.337 (0.010) 2,606
34 0.335 0.357 0.022 1,574
35 0.364 0.326 (0.039) 819
36 0.341 0.352 0.011 462
37 0.349 0.300 (0.048) 223
38 0.295 0.304 0.009 106
39 0.231 0.357 0.126 43
40 0.293 0.244 (0.049) 24
41 0.580 0.180 (0.400) 5
42 0.300 0.200 (0.100) 10