This is the 8th and final article in a series of articles about the beautiful creation known as Statcast. I encourage you to check out the previous articles on four-seam fastballs, cut fastballs, curveballs, sliders, knuckle curves, two-seam fastballs and sinkers. Statcast is a resource provided by MLB.com via the website baseballsavant.mlb.com. There seems to be an endless amount of data here to mine…and it’s all at our fingertips. The question is: how can we use this data to help us fantasy baseball addicts in our baseball player analysis for fantasy baseball purposes? In reality, this Statcast data is merely multiple pieces to a larger puzzle when analyzing each individual baseball player. It is new and exciting data that deserves analysis, but we shouldn’t lose sight of all the other resources on the Internet at our disposal.
Currently I want to focus on starting pitcher analysis with Statcast data. However, just not blanket analysis. Drilled down analysis looking at a single pitch. Today I want to look specifically at changeups.
Now we’re going to get into some statistics that are courtesy of baseballsavant.mlb.com. I want the readers to know for a starting pitcher to enter the data that I pulled for analysis they had to have a minimum of 100 total pitched thrown in that season (although I dropped that down to 10 in the current season).
The Top Goal For Changeups:
The top goal with this pitch is for the pitcher to throw it using the same arm motion as his fastball is thrown using. It is typically thrown 6 to 10 miles per hour slower in velocity than the pitcher’s fastball.
Does Average Spin Rate Matter With Changeups?:
Just like with two-seam fastballs and sinkers, there are proponents that believe that a lower spin rate is one aspect that makes these pitches effective. That may very well be the case, but from the pitchers in my 2015 and 2016 data sets I didn’t see an average spin rate range stand out as being better than any other range on starting pitchers performances that used the pitch 500 or more times in a season. There were good performances with very low average spin rates (Matt Shoemaker and a 1196 average spin rate with a .217 batting average against in 516 pitches during 2015) and bad performances (Jeff Locke and a 1373 average spin rate with a .281 batting average against in 627 pitches during 2015). There were good performances with a high average spin rate in the data sets (Kyle Hendricks and a 2117 average spin rate with a .167 batting average against in 556 pitches during 2015) and bad performances (Rubby De La Rosa and a 2063 average spin rate with a .297 batting average against in 604 pitches during 2015).
Average spin rate obviously isn’t the only thing that people look at as helping a pitch be an effective pitch. I don’t see enough evidence in the data sets to rely on average spin rate data as an easy sorting statistic for starting pitchers, so it is good that there are other things to draw from and look at in regards to a changeup’s success. There are many other factors that lead to whether a pitch thrown will be successful. Some of them are (in no particular order): velocity, pitch placement, talent level of defensive player ball is hit closest too, where the ball it hit, how hard it is hit, etc.
The Power Of The Whiffs%:
When looking at the 2015 (173 starting pitcher performances) and 2016 (168 starting pitcher performances) full season data sets, not surprisingly, I found that…generally speaking…of the starting pitchers that had the confidence in the pitch to throw it 500 or more times in that season the ones that had the top whiffs percentages (whiffs%) typically had lower batting average against numbers.
Jeremy Hellickson (2016): 766 changeups thrown, .169 batting average, .263 BABIP, 48% whiffs%
Cole Hamels (2015): 788 changeups thrown, .200 batting average, .290 BABIP, 46.9% whiffs%
Danny Salazar (2015): 585 changeups thrown, .155 batting average, .298 BABIP, 43.9% whiffs%
Francisco Liriano (2015): 608 changeups thrown, .280 batting average, .304 BABIP, 43.2% whiffs%
Cole Hamels (2016): 593 changeups thrown, .257 batting average, .304 BABIP, 41% whiffs%
Francisco Liriano (2016): 540 changeups thrown, .266 batting average, .321 BABIP, 40.9% whiffs%
Marco Estrada (2016): 841 changeups thrown, .162 batting average, .203 BABIP, 40.6% whiffs%
Kyle Hendricks (2015): 556 changeups thrown, .167 batting average, .242 BABIP, 40.6% whiffs%
Kyle Hendricks (2016): 777 changeups thrown, .136 batting average, .169 BABIP, 39.3% whiffs%
Erasmo Ramirez (2015): 555 changeups thrown, .147 batting average, .226 BABIP, 38.6% whiffs%
Johnny Cueto (2015): 540 changeups thrown, .221 batting average, .262 BABIP, 38.2% whiffs%
Sean Manaea (2016): 620 changeups thrown, .218 batting average, .274 BABIP, 38.2% whiffs%
Analysis: In the above list of 12 starting pitcher performances all of the performances had whiffs percentages (whiffs%) of 38.2% or higher with a minimum of 500 changeups thrown in that season at the major league level. Despite a high whiffs% Francisco Liriano had the 2 highest batting average against performances in the group (.280 and .266). Of the 12 listed above, 9 of the performances had a .221 or lower batting average against.
The Conclusion On Changeups:
The conclusion for me on changeups, as far as an easy to sort pattern, is this: Look for the starting pitchers with the changeups that have the confidence to throw it 500 or more times in a season. Then look for the ones that have a 38.2% or higher whiffs% on the pitch. Chances are good that the starting pitchers who meet those standards have a very good or better changeup in their arsenal.
When I do player analysis on starting pitchers obviously this easy sort work flow isn’t going to get me very far. If the pitch is utilized enough by the starting pitcher you can be sure that I will be tracking it by looking at how it has performed for that starting pitcher from one season to the next. I wish, like with some other pitchers, we had a stronger pattern that emerged from the data sets that identified more starting pitcher performances than the 12 it did in the 2015 and 2016 combined data sets. Unfortunately, that simply wasn’t the case when looking at the changeup.
While this type of individual pitch analysis is fun and yields some helpful information on starting pitchers, we must not get too caught up in it and we have to keep in mind that this is merely one piece to the overall puzzle when doing player analysis.