Ahead in the Count: Home Runs, Fly Balls, and Popups

I have always loved pitchers’ duels. One of my favorite childhood baseball memories is watching Curt Schilling throw a complete game shutout for the Phillies in a 2-0 win against the Blue Jays in Game Five of the 1993 World Series, with the Phillies facing elimination. I was only 12 years old at the time, and I did not know anything about sabermetrics, but Schilling appeared majestic as he pitched yet another brilliant start in what would become a magnificent playoff career. He only surrendered five singles that night and extended the series one more day.

When I finally did learn about Defense Independent Pitching Statistics (DIPS) and Voros McCracken’s discovery that pitchers have little control over batting average on balls in play, I found it mildly disappointing to look back at that box score and discover Schilling actually had rather mediocre DIPS numbers that night—just six strikeouts and three walks. However, I still felt confident in my memory that I had seen a great game as a youngster, and I convinced myself that the lack of home runs that Schilling allowed were evidence that he was unhittable that night. Unfortunately, I later discovered that pitchers have little control over the rate of home runs allowed per fly ball, and that Schilling actually generated just 10 ground balls that night on 24 balls in play. Though DIPS Theory finds Schilling’s entire career amazing, his numbers from that alleged gem appeared pedestrian even on that front.

Some fans probably dislike DIPS Theory because it takes the glamour away from memories like these, but I find myself appreciating individual pitching performances more now that I have a better understanding of the game. Seeing a pitcher like Tim Lincecum strike out 14 hitters in his playoff debut adds to the majesty of the 2010 postseason, rather than takes away from Roy Halladay’s playoff no-hitter the day before. The knowledge of what good pitchers do and do not control has given me an ability to watch a brilliant pitching performance critically and appreciate the masterful performances more.

Elementary DIPS theory is well established. Pitchers have a lot of control over strikeout, walk, and ground-ball rates, as evidenced by the year-to-year correlation of this statistic, but they have poor control over batting average on balls in play (BABIP). While we have known for some time that pitchers also have little control over HR/FB, statistics like FIP remove all of the variance from BABIP and reproduce an ERA with a league-average BABIP for all pitchers, but leave in all of the variance in HR/FB. This treats HR/FB as a skill and hides the fact that home it is a statistic that pitchers show even less ability to maintain than their BABIP. Looking at home runs per outfield fly ball is important, because some pitchers do have some tendency to allow more popups than others, even controlling for team.

Below I list the year-to-year correlation of different statistics for 549 pitchers with at least 300 balls in play in consecutive seasons from 2003-10. For BABIP and HR/FB, I give numbers net of team performance (because defense and parks affect these numbers similarly for pitchers on the same team).

Pitchers actually appear to have more control over the rate of hits allowed on balls in play than they have control over the rate that outfield fly balls go for home runs. Yet statistics like FIP remove the luck from BABIP, but not HR/FB, because it is known that the rate of HR/FB is a little persistent. However, since BABIP is even more persistent, this does not make much sense. Statistics like SIERA and xFIP that neutralize the number of HR/FB do a better job of explaining reality when you have only a limited amount of information about a pitcher. Tom Tango has often argued that several seasons of data will show patterns in HR/FB, and that FIP will outlast xFIP when predicting ERA for pitchers with several years of data available. The problem is that excluding BABIP for several years of data by using FIP will ignore the control that pitchers have over BABIP as it reveals HR/FB skill, and ERA itself may be better at showing a pitcher’s skill level with several years of data than FIP will.

Somewhat interesting was that pop-up rate has a high year-to-year correlation itself (.582). But what was surprising was that even after adjusting for team (and therefore park and foul ground area), the rate of popups per fly ball also had a pretty high year-to-year correlation (.328). This suggests that inducing popups is a skill beyond being a product of being a fly-ball pitcher. Therefore, SIERA’s inclusion of outfield fly balls and infield popups together may be something worth revisiting as we learn more about pitching.

All numbers in the below table are computed relative to total team numbers, and are for all pitchers with at least 100 balls in play in consecutive seasons between 2003 and 2010 (a sample size of 1459).

However, the rate of popups per ball in play does not have a significant correlation with the rate of home runs per outfield fly ball (-.043). The importance of making an adjustment to SIERA seems minimally important, as the correlation between next-year’s ERA and this year’s pop-up rate is low as well (-.028). On the other hand, regressing next year’s ERA on this year’s pop-up rate and this year’s outfield fly-ball rate shows an interesting effect.

Next Year’s ERA = 4.96 – 9.77*(net PU%) + 7.71*(net FB%)

The net pop-up rate coefficient is significant, with p=.022. This suggests that differentiating popups and fly balls may be more useful and could be used to enhance SIERA.

Although the rate of popups per ball in play does not correlate with the rate of HR/FB, this does not mean that we cannot learn something important about this statistic. In fact, part of the strength of SIERA comes from the fact that it implicitly models the rate of HR/FB similarly to how it implicitly models BABIP.

I checked the correlation of HR/OFB with DIPS statistics:

The reason that the strikeout coefficient in SIERA is so large is that not only do strikeouts lead to lower ERAs in and of themselves, but they also correlate with lower BABIPs and lower HR/OFB rates, which likewise correlate with lower ERAs.

Running a regression (on all pitchers with 100 balls in play or more) of HR/OFB rate on all three statistics does not show a statistically significant coefficient for anything but strikeout rate:

The equation of the regression with only strikeout rate is:

Net home runs per outfield fly ball = .016 – .100*(SO/PA)

In the above regression, the p-statistic on strikeout rate is also less than .001.

 Overall, this article and yesterday’s article both show the benefit of using regression analysis to study ERA. Pitchers clearly exhibit far more control over defense-independent pitching statistics, but they still do have some control over BABIP and HR/FB. While looking at statistics like FIP removes the noise inherent in these two metrics, they also remove the skill. Since pitchers’ BABIP and HR/FB skills are significantly correlated with their DIPS skills, running a regression actually controls for these effects and gives an accurate reading of pitchers’ true skill levels with a unique methodology that picks up on factors that other measures do not.