The Relationship Between Pitch Location Scatter Plots and Arm Slot

During the last offseason, I was looking at a scatter plot of Cleveland pitcher Tanner Bibee’s pitch locations, and it reminded me of a regression line with a negative slope.

I had a similar thought after watching Aaron Nola’s knuckle curve location.

However, the slope of Nola’s regression line is more gradual than Bibee’s. While this may be the result of various factors—such as the trajectory and usage of breaking balls, as well as command—I focused on something a little different: arm slot.


Nola’s average arm slot is 20 degrees, whereas Bibee’s is 51 degrees. And arm slot does have some correlation with pitch movement.


I looked into the relationship between pitch location scatter plots and arm slots. Here’s how:

• I plotted pitch location scatter plots for pitchers who threw more than 100 pitches of each pitch type.

• From these scatter plots, I drew regression lines and calculated the location correlation coefficients for each pitcher.

• Then, I compared the correlation coefficients with their average arm slots.


※ To evaluate left- and right-handed pitchers on the same scale, I multiplied the x-coordinates of left-handed pitchers’ pitch locations by -1.

※ Data from the 2024 season was used for the analysis.


Here was my hypothesis going into the analysis:

Pitch location scatter plots are influenced by a pitcher’s command and intent (such as inducing whiffs), whereas arm slot is more related to pitch movement—particularly vertical movement.

So, I expected that the correlation between the two would not be especially strong.


FF & FC & SI


All three fastball-type pitches showed virtually no correlation. Among the three, the sinker displayed a slightly higher correlation.


SL & ST & CU

Breaking balls that move toward the glove side showed a fairly strong correlation. The sweeper, in particular, recorded the highest correlation coefficient.


CH & FS


Offspeed pitches that primarily break to the arm side showed a higher correlation than fastball types, but a lower correlation than breaking balls.


Conclusion


The results did not deviate much from the initial hypothesis. However, it is noteworthy that glove-side breaking pitches such as sweepers, curves, and sliders showed the highest correlation. I plan to further explore this aspect in future research.






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