Since their rise from the depths of the American League in the mid-90s, the Yankees have always held a reputation as a patient team that can wear down opposing starting pitchers. The team consistently ranks near the top of the league in walks, finishing first in 2009. Patience has many effects, among them putting men on base for future hitters and getting into the opposing bullpen quickly. These effects have helped the Yankees win quite a few games over the past decade and a half.
Laying off pitches in and of itself, however, isn’t necessarily a good trait. Some players simply take pitches, regardless of where they cross the plate. That can be a good thing, but it doesn’t signify discipline. What we want is the number of pitches a player swings at outside the strike zone. Furthermore, we want to see how this compares to his swing percentage inside the zone, to see if he’s simply laying off pitches, or just laying off the ones outside the zone.
Here’s the list, taken from FanGraphs. O-Swing% is the percentage of pitches swung at outside the zone, Z-Swing% is the percentage inside the zone, Swing% is the overall percentage of pitches swung at, and Zone% is the percentage of pitches the player saw inside the strike zone. The Ratio number, which is how I sorted the list, is the out-of-zone percentage divided by the in-zone percentage. The lower the better, since we want the least out of zone swinging to the most in-zone swinging.
While Brett Gardner swung at the lowest percentage of pitches outside the zone, he also swung at the fewest pitches inside the zone. Since I wanted to correct for players who simply don’t swing — his 34.1 overall swing percentage was the lowest on the team — Gardner falls a bit, though he’s still in the middle of the pack. Jorge Posada, it appears, has the best combination of swinging at pitches inside the zone and laying off pitches outside the zone.
Just for fun, and because it might be more telling, here’s the same table, except the ratio is out-of-zone swings to overall swing percentage. It comes out much the same, though with a few changes.
Now, for the fun part. Anyone have any suggestions on how to better manipulate this data? This is a pretty rudimentary study, and it pales in comparison to what Jeff Zimmerman is studying. Consider this a jumping off point. Comments? Suggestions? Let’s talk about this.