Recapping the BIP
July 5, 2008 3 Comments
Before even getting into the meat of this article, no, the title does not refer to Bip Roberts… so I’ll understand if hardcore fans of his are now turned off. What the title does refer to, however, is balls in play and how they pertain to the statistics BABIP, FIP, and ERA. I have written a lot here and on my other stomping grounds of late about how some of these statistics are affected and, seeing as it is a holiday weekend with not much interweb usage, it seemed like the logical time to recap everything into one neat package. For starters, what are these three statistics?
BABIP: Batting Average on Balls In Play is a statistical spawn of the DIPS theory discovered by Voros McCracken at the turn of the century. Essentially Voros found that pitchers have next to no control over balls put in play against them, which is why certain pitchers would surrender a ton of hits one year and much less the next. From a control standpoint, the goal of the pitcher would be to get an out. Once a ball is put in play, unless it is hit right back to the pitcher many defensive aspects have to coincide for an out to result. Take a groundball for instance, one between shortstop and third base: both fielders have to understand whose territory the ball occupies and that fielder has to have the proper range in order to field it, all in a very short amount of time.
There are plenty of other variables as well but what should be clear is that the pitcher has no control over them. He may have control over sustaining a certain percentage of balls in play each year but the hits that result are almost entirely out of his hand. In fact, the only aspects of pitching over which he has any type of control are walks, strikeouts, and home runs allowed. Everything else is dependant on the fielding and luck.
BABIP is calculated by dividing the Hits minus Home Runs by the Plate Appearances excluding Home Runs, Walks, Strikeouts, and Sacrifice Flies. If Player A has 30 hits out of 90 at-bats he will post a .333 batting average. But if 8 of those 30 hits are home runs and 8 of the outs are strikeouts, in BABIP terms he would be 22 for 74, or .297. This explains that, of all balls put in play–any hit or batted out other than a home run–29.7% fell in for hits.
FIP: a creation of Tom Tango’s, Fielding Independent Pitching takes the three controllable skills of walks, strikeouts, and home runs allowed, properly weights them, and then scales the result similar to the familiar ERA. The end result explains what a pitcher’s skillset suggests his ERA should be around. Someone with an ERA much lower than their FIP is usually considered to be lucky while the inverse is also true. The statistic is kept at Fangraphs and ERA-FIP was recently added as well in order to allow readers a glimpse at those under- or overperforming their controllable skills.
ERA: arguably the most popular pitching barometer, ERA can be calculated by multiplying the earned runs of a pitcher by nine and dividing that product by the total number of innings pitched. While not a terrible stat it suffers from some pretty drastic noise. For starters, what are earned runs? The surname ‘earned’ implies there are other runs that can be given up and that these must satisfy a specific criteria. For instance, if a fielder botches a routine play with two outs, and the pitcher then gives up seven runs, none will be earned because the inning was extended by the poor play of the fielder. This gets into all sorts of questions regarding exactly what an error is and how that factors into a pitcher’s performance.
Earned runs are also a direct result of hits, which have been proven to be largely accrued through chance via the DIPS theory. So, if pitchers cannot control the percentage of hits they give up on balls in play, then fluctuations in hits can either inflate or deflate an ERA regardless of the pitcher’s skill level. Therefore the FIP is more indicative of performance level because it only measures the three aspects of pitching he has control over which should not suffer from much fluctuation at all, as Pizza Cutter showed not too long ago that these skills were some of the quickest to stabilize.
At Fangraphs we occasionally call upon a statistic we titled xBABIP, which refers to what the BABIP of a pitcher can be expected to be given his percentage of line drives. Dave Studeman found a few years back that the general range of BABIP could be predicted with very good accuracy by adding .12 to the LD%; if a pitcher surrendered 22.1% line drives his xBABIP would be ~.341. Using this for predictive purposes would not be correct due to the fact that the general baseline for pitchers is .300. What we can do is evaluate performance at a given time and attribute line drives to a rather high or low BABIP. For instance, saying that Player B’s BABIP of .275 as of today primarily due to his ultra-low 14-15% LD rate would be correct; saying that it will continue like this would not. The line drive percentage may change as the season goes on. In summation, we can use something like this when evaluating the past for pitchers but not the future.
David Appelman showed not too long ago that, in 2007, 15% of flyballs fell in for hits, 24% of grounders turned into hits, and a whopping 73% of line drives also followed suit. Due to this, the ideal xBABIP calculation would be .15(FB) + .24(GB) + .73(LD).
I have done studies here recently, and Jonathan Hale at Baseball Digest Daily has done others in the past as well, that show how aspects like velocity, movement, and location can all affect the BABIP of a given pitcher. It also been shown, again by Studeman, that elite relievers have the ability to consistently post lower BABIPs than others. More studies have shown that pitchers, if any, have very weak control over their BABIP but instead of deeming it control I would be more inclined to say that these pitchers are merely taking advantage of “cold spots.”
If just 15% of flyballs result in hits and such a large number of line drives do, then we could intuitively expect someone with consistently low LD rates and higher FB rates to post lower BABIPs. From a movement perspective, I found that those with above average vertical movement in different horizontal movement subgroupings post lower BABIPs as well. Higher vertical movement usually correlates to flyballs, and voila, flyballs have the lowest percentage of hits.
This was just a recap of the three statistics and explanations pertaining to their usage. Based on this, if we see someone like Carlos Zambrano, whose ERA consistently beats his FIP, based on consistently posting lower BABIPs, we could somewhat safely assume that he might not be controlling anything persay but rather taking advantage of all the aspects proven to result in lower BABIPs. His controllable skills may not be as good as his ERA would suggest but movement, velocity, and location may have combined to greatly aid his efforts.