A few weeks ago I noticed hitters no longer improved, but instead their production only declined as they aged. Additionally, I answered at few questions on the first article in a second article. Today, I am going to look at the individual hitter traits to see which ones may be leading to the early decline.
First off, I will start off with some disclaimers.
1. For most of the two previous articles, I used wRC+ as the metric to determining aging. It is adjusted to the run scoring environments for stadiums and leagues. The following values in this article use raw datawhich is not league adjusted. As I stated in the first article:
A problem exists when using wOBA in the recent lower scoring environment. The league wOBA in 2006 was .337, and in 2013 it was at .318. That’s a drop of 19 points in seven seasons, or 2.7 points per season. Players will have the appearance of aging from season to season … This overall decline leads to a large year-to-year aging factor
So when looking at the upcoming data/graphs, remember the overall run environment has changed. Additionally, I have included the start and stop league values for the time intervals to help put the data change into perspective.
2. Note: The aging curves were created by the delta method which weighs the plate appearances using their harmonic mean. With this method, there’s a small survivor bias summarized by Mitchel Lichtman at the Hardball Times:
… survivor bias, an inherent defect in the delta method, which is that the pool of players who see the light of day at the end of a season (and live to play another day the following year) tend to have gotten lucky in Year 1 and will see a “false” drop in Year 2 even if their true talent were to remain the same. This survivor bias will tend to push down the overall peak age and magnify the decrease in performance (or mitigate the increase) at all age intervals.
OK, now it is time to look at some of the data. I decided to concentrate on four major hitter traits: walks, strikeouts, isolated power and BABIP
Walk rates have been fairly constant over the 14 year time frame, so the curve can be compared without too much adjustment. The two curves are somewhat similar with the current seven year time frame peaking at bitter earlier at age 26 to 28. In the past the peak was near 29 to 30. This earlier peak would move the aging curve some, but not much.
Strikeouts have exploded over the past few seasons with an over a 3% point overall increase. Looking at the curves, the early decline in strikeout rates to age 25 still exists. Previously, the strikeout rate stayed the same until a slow decline after a player’s age 30 season. Current players now see a 2% point increase in their K% from age 24 to 30. Just a handful of years ago no change would be expected.
The earlier increase would push the aging curve down more in the late 20’s, but again not enough to explain the early decline.
Finally we are getting somewhere. Players are now seeing their power decline almost immediately. Players no longer gain power, naturally or unnaturally, over their first few major league seasons. For example, home runs went from a peak of 5692 in 2000 to 4661 in 2013 (18% point drop). Besides the obvious PED elephant standing in the corner, I think players are more and more in shape when they reach the majors and they don’t have any more strength to gain.
OK, now we are cooking with propane. Players now see their BABIP drop immediately. It wasn’t until age 26 when the decline previously occurred. I base the early decline on the increase knowledge and use of defensive information. Teams are doing a better job of tracking players in the majors and minors and know their hit tendencies. Teams are also more willing to make adjustment, such as over shifting a pull happy player compared to the past.
In the 2014 Hardball Times Baseball Annual, I examined extreme shift tendencies. Matt Adams hit into a shift 22 times in the first half of the 2013 season and posted a .368 BABIP. This value is unique because Adams isn’t really a feared hitter and only had 91 PA in his first season in 2012. Teams still decided he pulled the ball enough to implement a shift against him in the first half of 2013. In the second half, he hit into 39 shifts and his BABIP dropped to .308. Teams seem more and more will take a small amount of hitter information and position their defense accordingly.
The immediate decline in BABIP is the leading cause for the early aging of players. I could see this trend continuing as more and more teams implement shifts on players earlier and earlier in their careers.
While some changes in BB% and K% rates have factored into the recent change in hitter aging curves, earlier declines in BABIP and ISO are the leading causes. Players are no longer hitting with as much power. Additionally, the number of balls they put into play are turning more and more into outs. The increase use of better defensive alignments against a hitter earlier in his career may lead the player to age faster. It will be interesting to follow the trend in the future.
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