September 10, 2012

Resident Fantasy Genius

Using ISO to Legitimize High/Low BABIPs?

by Derek Carty

Last week, industry colleague Michael Salfino penned an interesting article for Yahoo! Sports discussing BABIP and how we might be able to tell legitimately good BABIPs apart from lucky ones (and legitimately bad BABIPs apart from unlucky ones).  A couple of you pointed the article out to me and asked for my take on it, so I thought it best to simply write up a post in case others were interested.  The article opens with:

I want to drive home one more time that big breakthrough that I think we've had here this year, which is using isolated slugging allowed (slugging average minus batting average) as a projection tool.

I love the batting average on balls in play stats (BABIP). But I can't believe it took me this long to see how limited it was since it was based on the most overrated stat of all time – batting average.

Because it's never too early throw an aside into an article or make a point that's only tangential to the topic at hand, I want to take a second to step on my soap box and talk about batting average.  People love to talk about batting average as a flawed or overrated stat, but I want to point out that the stat itself isn’t flawed any more than an “advanced” stat like BABIP or HR/FB or FIP is.  It does what it’s designed to do and nothing more.  It’s only overrated insofar as the prominence the mainstream media and old-school thinkers tend to place on it (which Salfino understands and hints at—this is more of a general rant than one pointed at anyone in particular, to be clear).  Take batting average for what it is, and it’s no more flawed than strikeout rate.

The main problem pundits point out with batting average is that all hits are weighted the same.  Salfino mentions this as the reason why batting average is overrated and as the reason why BABIP may be flawed:

With BABIP we're sort of stuck with judging all hits as hits, period, because we're already subtracting homers (which are out of play). We at least need to check BABIP against isolated slugging.

When talking about a hitter’s contribution to his team, of course it’s incorrect to assume that a home run and a single are worth the same amount—a fact many fans and analysts fail to recognize.  Used like this, batting average is a descriptive stat.  But when we’re talking about a pitcher’s BABIP, it’s usually as a predictive stat.  These are two very different things, and the distinction shapes how we use a particular stat.  After all, if we don’t know what we want a stat to accomplish, how can we possible hope to use it correctly?

The way we generally use BABIP is to look at pitchers with extreme values and say that, in the future, the pitcher’s BABIP will be league average, dragging the pitcher’s ERA along for the ride.  It’s a shortcut for regressing to the mean.  Essentially, because there is so much noise in BABIP, we say it’s safe enough to assume 100 percent regression to the mean when dealing with single-year samples.  And because doing a full regression would give us something close to this anyway, we’re generally okay with using this shortcut.

My feeling now is that if a pitcher has a good ERA and a good BABIP allowed (which has been viewed as lucky by default) PLUS a good ISO allowed, then he's probably not lucky. At least, he's not nearly as lucky as we think. On the other hand, if a high ERA pitcher has a bad/high BABIP allowed but also a high ISO allowed (about .133 is the league average), then he's probably not unlucky – meaning we wouldn't expect the ERA to be significantly lower the next year.

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