More on Pitchers and BABIP

Alright. We seemed to have a good response to the post on pitchers, hitability, and BABIP when I posted it two days ago. I'll now go over some of these comments and some mistaken assumptions that I made.

The first of these assumptions was that league average BABIP is .290. I'm not sure why I was under this impression (I must have read it incorrectly somewhere), but it seems that league average BABIP is generally closer to .300. This changes a lot of what I said and a lot of the assumptions that I had made about BABIP.

After reading up on BABIP this morning at the Hardball Times, Baseball Prospectus, and the Diamond Mine, it seems that pitchers can have an affect on how many hits they give up, but that this effect is quite small and much of this effect can be explained by their DIPS (Defense Independent Pitching Statistics - K/9, BB/9, and GB rate).

Pitchers can, in fact, affect their hits per ball in play a little bit, but at this time there is no statistic that can truly capture this skill. For now, DIPS is the best means we have of predicting future performance. Future performance can also be roughly predicted by looking at BABIPs that stray significantly from league average (such as Jason Marquis's .206 BABIP) and realizing that at some point it will regress to a point much closer to the league average.

Now we'll take a look at some of the comments people had about the previous post on this subject. One reader mentioned the exclusion of HRs and how this can skew BABIP because a lot of HRs are balls that are hit so well that they would be considered hits if left in the field of play. I can come up with a couple of reasons why I'm not overly concerned about their exclusion.

as they are more a result of a pitcher's tendency to give up The first is that HRs have a very strong correlation with Groundball rate. The more GBs a player induces, the less HRs that player should give up. (This is a subject I'll talk a little more about in the not so distant future, but this is the primary reason why I talk a lot about GB rate when analyzing pitchers.) Because this is the case, I don't really have a problem excluding HRs. I see a high HR rate as more a result of a pitcher giving up a lot of flyballs than it is of his ability to limit hits.

Roughly 90% of HRs are flyballs. If - hypothetically - the field of play were extended another couple hundred feet (and outfielders were fast enough to cover this entire area at the same efficiency they cover current outfields) these flyballs would still be caught at the same 78% that normal, non-HR flyballs are caught at now. Of course that notion is absurd, but I think it shows that HRs are mostly just farther traveling flyballs, which we've already established has a direct connection with Flyball Percentage (or Groundball Percentage, by default).

That's why we can look at BABIP and GB% separately. GB% represents how many HRs a pitcher should give up, and BABIP represents the number of non-HR hits that a pitcher has given up. Of course GB% is more stable than BABIP, and that is why HRs are easier to predict than hits and should be treated differently.

A couple of readers asked to see another list of pitchers and their career BABIPs, except this time using average and poor pitchers. It is tough to get a list of poor pitchers who have pitched for as many seasons as the guys on the previous list because if a pitcher is that bad there is little chance he will survive in the big leagues that long. Therefore, I'll give a list of some average pitchers that were able to last for a while.

Woody Williams - .285
Pat Hentgen - .288
Andy Benes - .292
Kevin Appier - .292
Hideo Nomo - .293
Denny Neagle - .295
Andy Ashby - .296
Darren Dreifort - .307

If I had swapped the names on this list with the previous one I doubt anyone would be able to tell. I think this further proves that while a pitcher may have some control over the hits he gives up, there is a lot of luck involved and DIPS is a much more effective way to analyze a pitcher's merits.

Alright, I think that's enough about that for now. Comment away on any of this stuff, and if you ever see an article that has a contradictory viewpoint feel free to bring it to my attention. The quest for knowledge is never-ending, and DIPS Theory is far from perfect, though the closest thing we have at the present time.

Explanation of Sabermetrics - DIPS Theory

Since many of you reading this may not know much about Sabermetrics, I've decided to go over some of the basic principles so that you can better trust what we're saying. We'll start with DIPS Theory (Defense Independent Pitching Statistics). We'll look at hitting theories later today.

Pitching
Voros McCracken is thought of as the pioneer for Sabermetric Pitching Principles. It is now understood in the Sabermetric community that pitchers have little-to-no control over what happens to a ball once it is put in play. Due to this, strikeouts are the best outcome a pitcher can hope for as there is no opportunity for him to get unlucky, as he could get if the ball gets put in play.

Walks are another important stat for pitchers as they are also defense independent. Obviously, walks are a negative event for pitchers.

Home Runs were once thought of as a defense and luck independent outcome, but now there are some who think they are not luck independent. We agree. Too many conditions, such as differing outfield fence distances, wind factors, and other atmospheric conditions make it impossible to compare HR rates between two pitchers. Some pitchers also draw the unlucky assignment of facing the wrong hitter at the wrong time, and that hitter decides to hit a home run. Due to these factors, in our stats we don't use actual Home Runs rates. We calculate the rate at which Outfield Flyballs and Line Drives turn into Home Runs and then apply this rate to each pitchers Flyball + Line Drive Rate to get an 'Expected Home Run' total.

For a while this is where DIPS stopped. But now there are many sabermetricians who include all batted ball data in pitcher analysis. The infield flyball (or popup) is the most efficient as it almost never turns into a hit - just 3% of the time.

The groundball is the second most efficient as it never turns into a home run, despite being turned into an out at a slightly worse rate than flyballs (72%). It also has the added bonus of being turned into a double play at times.

Flyballs are the second worst outcome. While they turn into outs 75% of the time, they turn into home runs 12% of the time. Line Drives are the worst for pitchers because they turn into outs just 26% of the time. Because it is often difficult to tell which hits are flyballs and which are line drives, those who record these stats are making a subjective opinion on which are which. For this reason, we combine line drives and flyballs into one stat (airballs) in our pitching formulas.

And that's the basis of DIPS Theory. We use a complex formula using all of these factors to determine a pitcher's effectiveness.

Sources:
Batted ball percentages were found at The Hardball Times.