You’ve probably seen or heard (even on this site, perhaps) reference to David Murphy’s batting average on balls in play (BABIP).
For those still a little unclear about BABIP, the concept is fairly simple. It’s just batting average with home runs and strikeouts removed. In other words, everything on which the defense has a chance to make a play (for more, check out a detailed explanation on Fan Graphs).
With a large enough sample size, BABIP should almost always be right around .300. Therefor players with an unusually high or unusually low BABIP from one season are considered strong candidates to either bounce back or fall off the following seasons.
David Murphy, thanks to his .227 BABIP last season, has been labeled a bounce-back candidate by many.
But is this a realistic expectation?
BABIP is not purely luck, especially for hitters. As players age and skill levels decline, so does a hitters BABIP.
Supporters of the BABIP bounce-back theory sometimes put too much faith in the stat, without taking into account the fact that not all balls in play are created equal. And a hitter’s skill level absolutely controls the quality of the ball in play.
Whether those balls find a whole or not may be luck, but the difference between a linedrive and a weak ground ball is most definitely influenced by skill.
Murphy finished in the bottom 10 of BABIP (minimum 200 PA) last season. So to get an idea of his expected bounce back, I took a look at the bottom 10 from the previous five seasons and how they performed the following year.
The results aren’t pretty.
For 20 of the 50 players included on this list, their low BABIP essentially signaled the end of their career – or at the very least, the end of their career as starters. These 20 players failed to even earn 200 plate appearances the following season. It’s a list that includes former stars such Andruw Jones, Brian Giles, Ken Griffey Jr and Chone Figgins, as well as a strong group of other quality starters such as Pedro Feliz, Mike Cameron, Geovany Soto and Joe Crede.
Of the other 30 players, 29 improved their BABIP, but not necessarily by meaningful measures.
Only seven of the 30 players boosted the BABIP above .300. And of those seven, four were under the age of 27.
14 of these 30 players were over the age of 30, and half of them failed to raise their BABIP even to the .250 mark. This list includes guys such as Vernon Wells, Jason Giambi, Andruw Jones and Jason Bay.
From this group of 30 players, the 14 guys over the age of 30 improved their BABIP by 21.7 percent – an encouraging, but hardly dramatic shift considering their pathetic numbers the prior season.
The 16 players under the age of 30, however, boosted their BABIP by 37.8 percent – a far more significant increase. And the 10 players under the age of 27 improved by a whopping 45.2 percent.
These numbers are hard to ignore, and don’t bode well for a 32-year-old Murphy, but the sample size is small.
To get a more complete picture of Murphy’s bounce-back potential, I decided to dig deeper.
Thanks to the wonders of Fan Graphs, I was able to compile a a database of every player’s BABIP and age from the previous 10 years, along with their BABIP the following season.
When viewing the stats as a whole, as expected, there’s no rhyme or reason to changes in BABIP based on age.
However, when we isolate the lowest BABIPs (in this case everyone under .250), we start to see a developing trend. It’s clearly not a perfect correlation, but the downward slope is becoming apparent.
When we take a look at these low-BABIP players based on age group, the decline becomes blatantly obvious. Take a look at the average percentage increase in BABIP following a sub-.250 year based on three age categories.
Age 27 and under – 25.9 percent
Age 28 to 31 – 20.0 percent
Age 32 and over – 16.2 percent
So if we assume Murphy is an average player in the 32-and-up category, we can expect an increase of 16.2 percent, which would lead to a BABIP of .264.
Assuming everything else remains constant from Murphy’s 2013 stat line, this bump in BABIP would lead to a .249 batting average and a .309 on-base percentage.
Will we be satisfied with those numbers?
Before answering that question, take a look at David Dellucci’s stats from 2008.