What do Andrew McCutchen, Buster Posey, Jay Bruce, and 12 other players have in common? They will all be in their age 27 season for 2014 and so we should expect that as a group their wOBAs will decline by 3 points on average. That may not sound like a lot, but it is the start of what will likely be the slow, steady offensive decline phase of their careers. Some will defy those odds, but which ones, and what might be signs of imminent decline?
To begin to answer these questions, I examined how hitters’ aging trends would be affected if certain skills did not decline with age. For example, how would a hitter age if his BABIP was consistently league average? I used wOBA as the measure of performance. The two age profiles in Figure 1 show this story. The solid line shows how players typically age, peaking at age 26, and declining steadily from there (see below for technical details on how this figure was constructed). The dashed line draws out a hypothetical age curve that assumes players’ BABIP is constant over time. Because players under age 30 tend to have above average BABIP, the dashed line is below the solid line for these young players. Conversely, older players would benefit from having their actual BABIP replaced by an average BABIP. The overall consequence of adjusting for BABIP is an age profile that is flatter—meaning that the effect of aging on wOBA is reduced.
Figure 1. Change in wOBA Age Profile when Adjusting for BABIP
The effect is reduced, but not eliminated. What other skills decline with age and how important are they to the decline in wOBA with age? Although swinging strike percentage, K rate, BB/K, and fly ball percentage all have important relationships with wOBA, these factors have little impact on the aging of wOBA. The wOBA age profile adjusted for these factors in Figure 2 is just slightly flatter than the unadjusted profile. This is because swinging strike percentage and K rate typically peak before age 26 and show little decline with age. To the extent that the adjusted curve in Figure 2 is flatter, trends with age in BB/K are most responsible.
Figure 2. Change in wOBA Age Profile when Adjusting for Swinging Strike Percentage, K%, BB/K, and Fly Ball Percentage
Figure 1 demonstrated that BABIP plays an important role in the aging of wOBA. Adding both BABIP and HR/FB skills to the others from Figure 2 explains the entire decline in wOBA after age 26. Indeed, if a player’s BABIP and HR/FB skills (along with the others I’ve mentioned) remained average throughout his career, he would actually show continuous improvement through at least age 30. Figure 3 shows this result. The flatness of the adjusted line indicates that the full set of statistics used in the adjustment does a very good job of accounting for trends in wOBA with age.
Figure 3. Change in wOBA Age Profile when Adjusting for All of the Above and HR/FB
So what does this mean for McCutchen and the others in our list? We may learn the most about how they will age based on observing trends in their BABIP and HR/FB rates from here on out. Doing so will be challenging because these are also among the least reliable measures of performance in a single season. Even so, a decline in these skills could indicate substantial performance losses to come. Additionally, players whose value derives from high walk or contact rates may age less precipitously than others.
A productive avenue for future analysis might be to assess whether there is a relationship between the amount of improvement in BABIP or HR/FB skills a player experiences before age 26 and the amount of decline in those skills after age 26. If so, then we might be able to better predict how a player’s offensive skills will age. However, we can learn a lot about averages, and our long-run projection for any particular player’s performance might improve, but it will always remain uncertain.
The age profiles adjust for what is often referred to as survivor bias—the fact that not all of the players in the sample at age 20 are also in the sample at age 35. To do this I used a technique commonly used by economists and others called fixed effects regression (see Jonah Rockoff’s work on changes in teachers’ performance with experience for one example). I run a regression that includes individual player fixed effects, ensuring that the relationship between wOBA and age is calculated using within-player variation in wOBA over time, rather than variation in performance across players. Consequently, the results are not affected by changes in the composition of MLB players by age. To calculate the adjusted profiles, I account for the other statistics as additional controls. Doing so means that when I adjust for BABIP, I am also adjusting for skills that are related to BABIP. Therefore my results do not depend on any particular model of how BABIP is related to performance. The above results are based on the 1,346 players who played in the majors for at least two seasons between 2002 and 2013. An alternative sample that is restricted to just the 304 players who played at least eight seasons during this period produces similar results, although the average wOBA levels are higher and the curves are less precisely estimated.
About the Author: Elias Walsh spends too much of his free time working with baseball data and trying to win his fantasy baseball league (or so his lovely wife informs him). His day job is a research economist at Mathematica Policy Research, where he conducts research to inform education policy decisions.
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