An Intro to Evaluating and Predicting Pitching Performance

The crack of the bat as it makes contact with the ball at a live baseball game has to be one of the more nostalgic “American audible events.” There’s just something about it that makes us all remember summer afternoons at the ballpark: the smell of hot dogs, beer and peanuts – and lobster rolls and tacos? – lingering in the air and a good friend sitting at our side. More recently we have learned, however, that it also represents a significant sabermetric transition. Specifically, we are talking about the transition of responsibility associated with run prevention from the pitcher to factors beyond his control, such as his fielding defense behind him. Let’s take a look at what the heck I’m talking about here, and delve into the world of pitcher evaluation through the somewhat recent phenomenon that is Fielding or Defense Independent Pitching.

Defense Independent Pitching theory originated with a posting by Voros McCracken postulating that pitchers are not really able to control whether balls put in play against them are converted into outs or result in hits for the opposition. He based this opinion on the idea of the Defensive Responsibility Spectrum (DRS), explained brilliantly here by Tom Tango. Essentially, what DRS is able to do is assign responsibility to “all those things that the defense is responsible for,” approximated as follows:

Almost all Pitcher:  HBP, Balk, Pickoff, K, BB, HR
Mix: WP, SB, CS, 2B, 3B, 1B, Batting Outs, PB
Almost all Fielders: Other Running the Bases Outs

McCracken basically concluded that the best way to evaluate and predict pitcher performance was to analyze the data in the first row, namely that for which the pitcher bears just about all of the responsibility, as opposed to any of the more contextually dependent phenomena in the rows below.

This bold thought seemed counterintuitive, and sparked a great deal of response, debate, and ground-breaking analysis after McCracken expanded on his theory on this website in early 2001. Tom Tippett produced an incredibly in-depth follow up on McCracken’s piece using 90 years of accumulated baseball data, and found while McCracken’s initial conclusion may have gone a bit too far – something McCracken had already begun to take into account himself – this new way of thinking was dead on.

Mitchel Lichtman then followed Tippett’s analysis with one of his own, where he made the breakthrough of correlating certain types of batted balls allowed into expected performance on % of balls in play allowed resulting in hits, or Batting Average on Balls in Play (BABIP). Lichtman concluded that although pitchers cannot really control their BABIP against, they do have a significant amount of control over the types of batted balls they allow, and this in turn allows us to be intelligent in our predictions in their future BABIP performance. In English now, Lichtman is basically the man who put numbers behind the idea that there are “ground-ball pitchers,” i.e. pitchers who induce a greater than average percentage of ground balls on balls in play, and “fly-ball pitchers.” Pitchers do maintain some control over a hitter’s ability to stroke line drives and pop the ball up as well, but the correlations are not as strong. As is intuitive, good pitchers generally allow slightly fewer line drives (balls that are hit pretty hard) and slightly more infield pop flies (contact is not strong) than your average pitcher.

David Appelman posted a great piece on the topic of BABIP against for pitchers over at FanGraphs which allows us to generate the following estimation table on the rates of different types of batted balls being turned into outs:

Hit Type     Expected Out %
Fly Balls        85%
Ground Balls     76%
Line Drives      27%

Looking at this you may be wondering – why are ground ball pitchers all the rage today if fly balls are actually converted into outs at a higher rate than are ground balls? This has to do with the fact that one of the worst things a pitcher can do is to give up a home run. In general, home runs seem to occur on about 10% or 11% of all fly balls allowed, somewhat independently of which pitcher is allowing them. As such, it follows that a pitcher who allows more fly balls will allow more home runs, even if he’s an otherwise outstanding pitcher – think Johan Santana or Roy Oswalt. While the ground ball pitcher may give up a few more singles – as well as a few more unearned runs – the fly ball pitcher is going to get hit with the dinger more frequently, thereby limiting his effectiveness somewhat if his other peripheral statistics (i.e. the defense independent numbers we looked at above) do not remain strong.

So what do we do with all of this information now that we have it? Let’s review the two main tenets of the conversation so far:

  • The best indicators of a pitcher’s future performance are defense-independent rate stats including: K%, BB%, and to some extent HR% allowed.
  • A closer look at specific batted ball data can tell us even more about what to expect from a pitcher in terms of his BABIP against

Let’s go ahead now and take a look at one of the actual statistics used to express the concept of Defense Independent Pitching and then apply it to a real example that might even help a few fantasy teams along the way.

If you clicked the link with Tom Tango earlier in the piece you probably ran into his statistic Fielding Independent Pitching (FIP). It takes into account exactly what we’ve been talking about so far, and does so in a simple, easy to understand format. While some of the newer, slightly different, statistics in this area take batted ball data into account, FIP purely covers the stuff that the pitcher alone is responsible for, and is calculated based on the following formula:

FIP = NC + (HR*13 + (BB+HBPIBB)*3 – K*2)/IP

Now, I’ve added in “NC,” which we’ll call our normalizing constant to help make the number that FIP generates match what would be a projected ERA – or about what a pitcher’s ERA should be based on proprietary performance. This number is generally adjusted for current league conditions, but right now generally sits at around 3.2. Dave Studeman went ahead and refined this statistic a little bit by normalizing the home run rate included in an attempt to eliminate fluky FIP numbers as a result of extreme HR luckiness or unluckiness, an example of which can be found here. For the purposes of our discussion, however, we’ll just use the simplest format as it is most readily available to all, standard FIP.

Consider the following two players based on their standard fantasy league statistics:

Pitcher      ERA   WHIP    K/9   W
Pitcher A   7.78   1.68   7.56   2
Pitcher B   2.65   1.35   6.00   4

That’s not particularly close, right? But hold on; take a look at their FIP, noting that these numbers represent RA approximations, not ERA as we look to distinguish simply between pitcher dependent and independent data:

Pitcher       FIP
Pitcher A    4.31
Pitcher B    4.85

How can this be and what does it mean? What we are looking at here is a situation where Pitcher A has actually performed better than Pitcher B in the defensive independent categories we’ve been looking at, namely, BB%, K% and HR% allowed. Now, I’ve manipulated this example to be sure that we aren’t looking at quirky home run data or major discrepancies in park effects, so that’s not the explanation. The difference between these two pitchers fantasy numbers so far comes down to a combination of luck and defensive support. There’s not much we can do about the luck factor, but in terms of fantasy baseball, defensive support is going to be an ongoing concern so let’s unveil the names in order to take that into account:

Pitcher A   Ricky Nolasco
Pitcher B   Matt Cain

No matter which way you look at it, the Marlins defense has been significantly worse than that of the Giants, so we’ll take that into consideration by noting that pitchers playing in front of a good defensive team will generally outperform their FIP as compared to those in front of poor defensive teams. Even accounting for this, FIP suggests Nolasco may be pitching better than Cain right now, so we aren’t necessarily looking for an improvement in Nolasco’s own performance, but rather an improvement in his luck on balls in play which will in turn result in improved fantasy numbers. This is obviously a somewhat simplified analysis, but it remains insightful. I’d bet most Nolasco owners out there right now would be thrilled if you dangled a little Matt Cain Sampler in front of their nose. This is a good example of how using Defense Independent Pitching Statistics can help predict future performance, so keep an eye on pitchers who’s FIP and ERA just don’t line up, as they may be due for a regression soon.

Defense Independent Pitching is an incredible research field full of brilliant minds and big equations, and incredible advances are being made every day. Don’t be overwhelmed, however, as the concept remains accessible to all: when the pitcher turns his head after the crack of the bat to watch where the ball is going, he’s just about as much of a spectator as you are.