As many of you now know, last week we unveiled some tremendous new metrics. Available on individual player pages as well as the leaderboards, you now have access to plate discipline metrics for pitchers and pitch type statistics for hitters. The former includes information along the lines of how often a pitcher induced a swing out of the zone, in the zone, as well as his percentage of first-pitch strikes. The latter includes the percentages, and velocities, of pitches seen for hitters, as well as his percentage of first-pitch strikes seen.
I wrote a bit of an introduction to these new statistics last week, and David has written several glossary-type entries as well. This is the type of information that has piqued my interest for a long, long time, and it now adds another dimension to evaluations. For instance, did you know that Johan Santana posted an O-Swing % (percentage of pitches out of the zone that batters swung at) of 30.1 in 2005 and 2006, which decreased to 28.2% in 2007, and 26.8% this past season?
Using the new statistics, I decided to run some correlations to see if certain statistics held strong relationships to each other. First, here are the results for correlations run between the percentage of first-pitch strikes and six prominent evaluative statistics:
K/9: 0.194 BB/9: -0.719 WHIP: -0.515 BABIP: 0.096 ERA: -0.31 FIP: -0.406
The results here are not that shocking, or at least they should not be. Getting ahead of the hitter is generally considered key for the pitcher. Doing so, in theory, should correlate quite strongly to any metric involving walks. As we can see, there is a very strong relationship between the percentage of first-pitch strikes and the walks per nine innings issued by pitchers. The relationship loses a bit of its strength when hits allowed are added to the equation in the form of WHIP, but the -0.719 correlation between F-Strike% and BB/9 is actually the strongest of any that I ran. Here are the results for O-Swing % and the same six evaluative metrics:
K/9: 0.281 BB/9: -0.493 WHIP: -0.462 BABIP: 0.036 ERA: -0.362 FIP: -0.428
Here, the results are a bit different. Nothing is incredibly strong or on the same wavelength of strength as the FStrike-BB/9, but we have a few relationships of moderate strength. What exactly is O-Swing? It is the percentage of pitches that a pitcher threw out of the zone, that a hitter swung at. With this in mind, we might initially expect that pitchers with the highest percentages in this area would strike more batters out, walk less, and therefore be very effective in the ERA and FIP department. One thing to keep in mind, though, is the percentage of pitches that these pitchers throw in and out of the zone.
Jake Peavy and Barry Zito, for instance, were amongst the bottom in terms of percentage of pitches thrown in the zone, at around 47%. However, Peavy induced many more swings on these pitches than Zito, which is a big reason for the difference between the two, since their percentages of pitches in and out of the zone were virtually identical. When we have pitchers with different percentages in the mix, as is the case in the correlations using O-Swing, the results should not be as concrete. Overall, the strongest relationship here also involves BB/9, as the idea goes back to the Peavy/Zito example: Peavy gets swings and outs on pitches out of the zone, Zito does not. The higher the percentage is of swings out of the zone, the better the chance is that the BB/9 will be lower.
Lastly, Z-Swing%, which is still a bit curious. For instance, does a pitcher want a higher or lower percentage here? I would venture a guess that a lower percentage would be better, as the pitch is already in the zone and therefore very likely to be called a strike. A hitter failing to swing will take a called strike. It probably is not as important as FStrike or O-Swing, but here are the correlations:
K/9: -0.067 BB/9: -0.014 WHIP: -0.037 BABIP: -0.150 ERA: -0.027 FIP: 0.087
Well, I guess it really doesn’t matter for pitchers, as the percentage of swings induced on pitches in the strike zone does not share anything close to a strong relationship with any of the above six metrics. Interestingly enough, the highest correlation for Z-Swing involved BABIP, which was the lowest for F-Strike and O-Swing. The -0.150 isn’t significant by any means, though, so nothing should be taken away by that. At the very least, these results show what we would generally expect: the more first-pitch strikes, the lower the rate of walks or vice versa, and inducing swings out of the zone can result in better rate and run prevention stats.