Spin Rate: Is a C the new F?

Baseball has gone through many trends over the years in player development and strategy. You have the Moneyball era in the early 2000s, that is still being used today, the Home Run/Launch Angle philosophy, and now a newer analytical approach to the game that looks at stats that are more in depth than Batting Average and Runs Batted In. One of these new metrics that is being measured is Spin Rate. As obvious as it might sound, Spin Rate measures the revolutions (or rotations) per minute of a ball as it is traveling from the pitchers hand to home plate. Many fans see spin rate as a direct correlation with success, the more you can spin the ball, the harder you can throw and the more movement you can get on a pitch, thus making you more successful.

I was recently listening to Camden Kay give a presentation on spin rate and how pitchers should strive to not be "average". You can be above or below the average spin rate, but being at that average level makes you look like most pitchers. Intuitively, this makes perfect sense. The more unique that your pitches look, the less success that hitters will have against you. However, I wanted to see if the data backed this theory up and if it did, how we could use it to better train athletes.

Proving Average Spin Rate is Bad

In order to tackle this first problem, I had to accumulate lots of data, and I mean LOTS of data. I downloaded 40,000 pitches of data from Baseball Savant that included Pitch Name, Spin Rate, xwOBA, and more. From here I had to figure out exactly what I was looking for and how to test it. I came to the conclusion that the best way to measure the effectiveness of spin rate for each pitch would be to measure it to xwOBA. xwOBA is the expected weighted on base average of a batted ball. This is calculated using launch angle, exit velocity, and in certain situations, sprint speed. I think xwOBA is a more accurate measure than something like Batting Average because each outcome has a different weight assigned to it. With Batting Average, a single weighs just as much as a Home Run, even though a Home Run has a greater impact on the outcome of a game than a single does. xwOBA accounts for this difference, and thus gives us a more accurate representation of spin rate effectiveness.

The following charts display the spin rates of 40,000 pitches and their xwOBA results.

***Knuckle-Curveballs, Eephus, and 2-Seam Fastballs were eliminated from the data set as there was not enough data to support any logical conclusion***

As you can see, there is a funnel like structure in all of the graphs that support the theory that average spin rate leads to more hitter success. While there might not be a ton of points for the higher xwOBA values, there are 40,000 combined pitches across the 6 listed above, which is an ample amount of data for these purposes. Therefore, we can confirm that an average spin rate will more than likely lead to more hitter success.

How do we use this conclusion to develop athletes?

The lazy answer would be to mess around with the pitches until they don't look average anymore. However, Jacob DeGrom's fastball and Aroldis Chapman's fastball are considered "average" in spin rate, but are two of the most dominant pitches in baseball. We thus have to look at it from a different perspective. Jacob DeGrom shouldn't change anything about his fastball because it works. If a pitcher has a great pitch that has an average spin rate, who cares? We need to use this theory and this data to evaluate pitches that a pitcher might throw that aren't so great. If a guy is struggling with his slider and he feels his command is good and his movement is good, then you can look at his spin rate and say "how do we make this pitch look different?".

Pictured above is a random pitcher's Spin Rate Differentiation chart. The grey violin plot represents the MLB average of 40,000 pitches and the colored dots represent where this player was at the time of this bullpen. If the pitcher found that his slider just wasn't as productive of a pitch as it could be, then he might look to mess with the spin rate, as it falls in the dead center of the average. Plotting information like this could be really key to helping pitchers better understand who they are as a player and what kind of adjustments that they could make to become even better.

Conclusion

Throughout the entirety of this project, I couldn't but help think about the uprising of spin rate in bullpen pitchers. Minor league farm systems are filled with guys who can throw 95+ and have ridiculously high spin rates on their fastballs which can make them seem elite and ready to throw at the major league level. However, if this trend continues we may easily see the average move up and these flamethrowers look very similar to each other. This consequently could allow some of the slower spin rate players, like Kyle Hendricks and Hyun Jin Ryu to be even more dominant due to their unique look.