Note: I found some errors in the data. Data below has been corrected, as well as some conclusions — BP
Yesterday, Jeff Zimmerman examined how Tim Lincecum’s performance has depended to some extent on his ability to pitch to the edges of the plate. Last year, Lincecum was one of the worst starters in the game in terms of the percentage of his pitches thrown to the black. Coincidently (or not so coincidently), Lincecum suffered through his worst season as a professional.
As with many things, Jeff and I happened to be investigating this issue of the edge simultaneously. Of course, we were not the first to dabble in this area. Back in 2009, Dave Allen noted that differences in pitch location–specifically horizontal location–led to differences in BABIP.
Like Dave, I was curious about the overall impact that throwing to the edges–or the black–has on overall performance. My thinking about pitchers throwing to the edges naturally led to some hypotheses:
- Throwing a higher percentage of pitches on the edges leads to lower FIP.
- Throwing a higher percentage of pitches on the edges leads to lower ERA.
- Throwing a higher percentage of pitches on the edges leads to lower BABIP.
- Throwing a higher percentage of pitches on the edges is associated with lower four-seam fastball velocity.
I think the first three hypotheses are intuitive, but the last one stems from the idea that as a pitcher ages and loses zip on their fastball they cannot remain successful unless they increase their avoidance of the heart of the strike zone.
Jeff and I are still tinkering with the methodology. For this article, I decided to classify the edge as the area three inches (essentially, about the width of a baseball) to the left or the right of each side of the strike zone. I also adjusted the zone for right and left-handed batters per Mike Fast’s work (see here):
Edge for right-handed batters:
px <=-.765 AND px >=-1.265 & px >=.75 AND px <=1.25
Edge for left-handed batters:
px <=-.85 AND px >=-1.35 & px >=.845 AND px <=1.345
For frame of reference, I calculated the Edge% (i.e. the percent of all pitches thrown that qualify as being on either edge) for all qualified pitchers since 2007 and broke them out into percentiles:
When we look at the average performance of pitchers in each percentile and compare them to each other, a few interesting patterns emerge*:
And here are some basic correlations:
First, our initial hypothesis that the more a pitchers throws to the edges the higher their strikeouts and lower their walks is confirmed. The correlation between Edge% and K% is only .08, and Edge% and BB% is -.11. Interestingly, the difference in average K% between the top decile and bottom quarter of edge throwers is almost 0%. However, the difference jumps to about 1% between the top and bottom quartiles; same for BB%
This pattern holds pretty well across a number of metrics, including BABIP (>=75th Percentile minus <25th Percentile -.005, -.11 correlation with Edge%), ERA (-9, -.18), and FIP (-.17, -.21).
Pitchers that work the edges more also see higher swings outside the zone, along with less contact.
There is also a slight difference in the rate at which pitchers throw to the edges and their velocity. Pitchers that worked the edges the most threw their four-seam fastball about 1 mph slower than those with the worst Edge%, and relied on that pitch less (39.7% vs. 54.6%). Pitchers that threw more cutters, sinkers, and two-seamers lived on the edges at a higher rate.
So that’s the high-level, how does this actually look for specific pitchers?
Fister came up with Seattle in 2009 and posted decent, but not outstanding, numbers (96 ERA-, 120 FIP-). That year, Fister flirted with top quartile performance in terms of Edge% (19%). The following year, however, Fister’s Edge% dropped below the 50th percentile (18.2%), and with it his ERA- jumped to 106 and his K% and SwgStr% tumbled (12.9% and 4.4%, respectively). Fister did manage to post an above-average FIP, but this was mostly due to cutting his HR/FB by over half in 2010.
In 2011, Fister broke out. His Edge% jumped into elite range (21%) and he posted ERA- and FIP- that were also in elite company (73 and 78, respectively). Additionally, Fister’s K% jumped to 16.7%, and would further jump to 20.4% the following year:
We see a similar story for David Price. After a brief call-up with the Rays in 2008, Price entered the rotation in 2009 and posted a slightly below average ERA- and FIP-. In 2010, Price elevated his already good Edge% to elite status (21.2%) and saw his ERA- and FIP- plummet. Price has since maintained that elite Edge%–even improving it to a league-leading 22.5% in 2012–and along with it he had arguably his best season in 2012 (66 ERA-, 77 FIP-, 24.5% K%).
None of this is to say that Edge% is the explanation for pitcher performance. The correlations exist, but they are relatively small. However, the preliminary results show a pretty good relationship between pitchers adjusting their approach and learning to control the edges of the zone better and a corresponding change in their overall performance.
Jeff and I are continuing to work on this topic. For example, we think it makes sense to take Edge% and look at it in the context of counts. Are pitchers more successful throwing fastballs in fastball counts because they largely throw those fastballs on the edges? Does throwing to the edges account for why some pitchers have a higher percentage of strikeouts due to called strike threes?
Needless to say, there are a number of places one can go with this, and we’ll be exploring a number of them in the coming weeks.
*Because of the changes made to how PITCHf/x classifies pitches, the data for pitch-type classifications only uses 2010-2012 data.
Bill leads Predictive Modeling and Data Science consulting at Gallup. In his free time, he writes for The Hardball Times, speaks about baseball research and analytics, has consulted for a Major League Baseball team, and has appeared on MLB Network's Clubhouse Confidential as well as several MLB-produced documentaries. He is also the creator of the baseballr package for the R programming language. Along with Jeff Zimmerman, he won the 2013 SABR Analytics Research Award for Contemporary Analysis. Follow him on Twitter @BillPetti.