The stats we use: FIP

Hank speaketh and Derek benefiteth
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In previous editions of this series we’ve discussed UZR, a defensive statistic, and wOBA, an offensive one. Today we’ll move onto a pitching one. It won’t be the only pitching one we’ll discuss, just as wOBA won’t be the only offensive one. To the best of my abilities, here’s an explanation of Fielding Independent Pitching, or FIP.

Understanding DIPS

The roots of FIP extend back to 2001. In Baseball Prospectus’s annual book, Voros McCracken presented the case that pitchers have little to no control over what happens to balls put in play. The article itself is pretty easy to understand, so if you have a spare five minutes I suggest giving it a read. If not, I’ll provide the most important of McCracken’s findings.

He looked at how hits per balls in play fluctuated from year to year, and found that “pitchers who are the best at preventing hits on balls in play one year are often the worst at it in the next.” He then cites Greg Maddux, who had a poor rate of hits on balls in play in 1999, but was among the best in 1998. Pedro Martinez saw a similar trend, performing horribly in 1999 and excellently in 2000 on balls in play.

You can see for yourself. Here’s Pedro’s BABIP in 2000, .253, tops in the majors, and here’s his BABIP in 1999, third worst among qualifying starters. You can see Greg Maddux on that list as well, seventh worst among qualifying pitchers, while he finished sixth best in 1999. So if pitchers as prolific as Maddux and Martinez can go from among the best to among the worst in the span of one season, it should say something about the nature of a pitcher’s ability to control the outcome of balls put in play.

So what does a pitcher have control over? Tom Tango lists it in a spectrum, from 100 percent pitching to 100 percent fielding. On the 100 percent, or near-100 percent, pitching side: balks, pick-offs, HBP, K, BB, HR. Then there’s a gray area, where it’s partly the pitcher, partly the fielding, though tough to determine which. These outcomes include wild pitches, stolen bases, caught stealings, singles, doubles, triples, batting outs, and passed balls. On the 100 percent fielding side are running outs. The focus of FIP, then, is on the 100 percent pitching part of the spectrum.

Weighing homers, walks, and strikeouts

In our wOBA and UZR primers, we talked about linear run estimators. As a one-sentence recap, linear run estimators put a value on outcomes based on how they contribute to actual run scoring, based on years of historical data. In order to weigh home runs and walks as negative outcomes, and strikeouts as positives, we need to use the linear run estimators to create a ratio, so that we properly weigh the value of each. For those who don’t want to see formulas, skip to the next section. For those who want to see the actual numbers, here goes.

Why the 13:3:2 ratio? We need look no further than the linear run estimator. That’s the ratio of value between homers, walks, and strikeouts.

Scaling it to ERA

One attractive quality of many new statistics is that they scale to existing stats. That makes it easier for us to transition. Looking at raw wOBA, for instance, you might not be able to immediately recognize how good a player performed. But, because it’s scaled to OBP, we can look at the number with a sense of familiarity. It runs along the same scale, so if we know that a hitter with a .335 OBP is near league average, we can assume the same of a player with a .335 wOBA. Except, of course, that wOBA tells us more than OBP by itself.

To align to ERA, we simply add 3.2 to the FIP. That number can apparently fluctuate sometimes — I’ve seen Tango mention adding 3.1 as recently as 2008. But more recently he’s gone with the 3.2 number.

A note on xFIP

In browsing stats on sites like FanGraphs. you might notice a stat called xFIP. This takes the idea of pitcher control a bit further, positing that in addition to having little control over outcomes on balls in play, pitchers have little control over the rate at which their fly balls go for home runs. So, to normalize for this variance, xFIP looks at the number of outfield flies hit off the pitcher, and takes 11 percent of that, which is the league average percentage of fly balls hit for home runs. The equation remains the same.

The reason I like this is because pitcher see more consistency in their year-to-year strikeouts and walks than home runs. There’s still some year-to-year correlation with home runs, but just not as strong. Is that enough to warrant a further normalization? That’s for you to decide. Chances are, however, that we’ll stick to just FIP here when talking about the things pitchers do.

It’s not all about luck

A common misconception is that FIP treats outcomes on balls in play as luck. This is not true. As explained above, outcomes on balls in play represent a gray area, where we don’t know how to what degree the pitcher and fielders are responsible. FIP just strips those plays out of the equation. See the section below for further elaboration.

A good way to think about this is how Tango put it. What we want is ERA to equal FIP plus fielding dependent pitching, plus fielding, plus luck — therefore luck is just one component stripped out of FIP. There are two other components stripped out as well, both of which are probably more important than luck.

Remember: it tells us one thing

The more important thing to note about FIP is exactly what it tells us. It does not make claims about luck, per se. What it tells us is how a pitcher fared on events that were close to 100 percent in his control. Since we know that factors like luck and defense play into ERA, it’s valuable to know how a pitcher does in terms of events for which he’s solely responsible.

Later in the series we’ll get to tRA, which considers batted ball type, and SIERA, Baseball Prospectus’s take on the matter, which will be revealed in the upcoming Baseball Prospectus 2010.

Resources

The original DIPS article
Defensive Responsibility Spectrum
Tango elaborates on FIP

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Hank speaketh and Derek benefiteth
RAB Live Chat