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How Release Points Affect Platoon Splits
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.
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.
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.
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.
Paul Maholm exhibits an interesting split.
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.
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Comments
Why does the color green correspond to +0.015 runs on some graphs and up to +0.06 runs on others?
Posted by: Anonymous at September 15, 2009 9:09 AM
All of this scientific evidence is interesting......but how do you account for the extreme variations in umpires' strike zones?
Posted by: Tom at September 22, 2009 8:01 AM
Tom,
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.
Posted by: Jeremy Greenhouse at September 22, 2009 10:22 AM