Touching BasesSeptember 15, 2009
How Release Points Affect Platoon Splits
By Jeremy Greenhouse

Dave Allen I am not, but I will do my best at an F/X visualizations-style piece. Below is the expected run value of a pitch based on its release point, which is defined as the point where the ball is measured 50 feet away from home plate. The image is from the batter's perspective, so points on the left tend to be thrown by righties and vice-versa.


Kind of like a rainbow, kind of like a tie-dyed afro.

Looking at the image, my guess is that the graph says more about the context of the pitches than content. Managers can control when they deploy pitchers of a given arm slot, so in all likelihood, lower release points occur when the pitcher has a platoon advantage over the batter. For example, see that cluster of pitches about a foot off the ground and two feet on the third base side of the rubber? All 1,000 or so were thrown by righty one-out guy extraordinaire Chad Bradford, whose numbers exceed his talent thanks to his manager placing him in situations where he can be expected to succeed. So what I’m saying is that the above graph is more descriptive of batters than it is of pitchers.


The plots are more or less mirror images. Against same-handed batters, southpaws who stand far to the first-base side of the rubber and sling the ball from a low three-quarter arm slot are expected to shut down the opposition. Release points seem to have a much greater effect against left-handed batters than they do right-handed batters, as you can see in the range of the color map. This is likely why LOOGYs are all the rage, while you rarely hear about ROOGYs other than the aforementioned Bradford.

I decided to try an analysis of individual players with large gaps in their platoon splits. Billy Butler would be one of the American League's elite hitters were pitchers only allowed to throw left-handed.


He hits standard righties at a rate of one run below average per 100 pitches, while he hits standard lefties to the tune of a couple runs above average per 100 pitches. However, pitchers with untraditional arm slots are where it gets interesting with Butler. He can't touch righty sindwinders, and he's grounded out weakly three times while facing those several submarine pitches from Bradford. Conversely, he has apparently picked up the ball quite well against lefty sidearmers in the limited time he's had against them. You win a prize if you said small sample size.

For my lefty hitter, I chose Ryan Howard, who might be out of a job playing baseball if all of us could only throw lefty.


Oddly, Howard not only does poorly against lefties who sling the ball, but also righties who release the ball from the extreme third-base side of the rubber.

Paul Maholm exhibits an interesting split.


He's much better against lefties in general, but it seems to me that his worst pitches against both sets of batters when he releases the ball from straight on according to the batter's point of view. This is not what we saw the the league average split. It is abnormal that Maholm has performed better against righties when releasing the ball closer to the first-base line.

For such a great starting pitcher in the past, Brandon Webb sure shows a large platoon split. His go-to pitch, the sinker, does happen to be prone to the largest platoon split, on average, of any type of fastball. Keep in mind that the sets of graphs for Maholm and Webb vs. RHB and vs. LHB are set to different scales, so it appears as if they're dramatically altering their release points based on batter handedness, but it's actually just a fault of mine in setting the axes.


Webb is best when pitching from a higher release point that is closer to his body. Just a hunch, but I'd guess this has to do with the movement on his sinker he gains from a higher arm angle.


Why does the color green correspond to +0.015 runs on some graphs and up to +0.06 runs on others?

All of this scientific evidence is interesting......but how do you account for the extreme variations in umpires' strike zones?


You actually can do the same process of a local regression to account for umpires' different strike zones. Check out some work by Dave Allen, Josh Kalk, and John Walsh about strike zones using pitchf/x.