This is the 7th 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 and two-seam fastballs. 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 is 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 should not 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 sinkers.
Now we are 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).
About The Sinker:
Of course, with any pitch, the pitchers love to miss the player’s bat with it. However, another goal of this pitch is to get ground balls…if contact is made.
In the articles prior to two-seam fastballs we were looking at the average spin rate of the pitches and whether the belief that a higher average spin rate on each pitch really mattered all that much. Well, just like with two-seam fastballs, there are proponents that believe that a lower spin rate is one aspect that makes these pitches effective. We will have to see what the data sets show in helping us determine if this is truly something worth tracking or not.
Things That Help A Pitch Be Effective:
Average spin rate obviously isn’t the only thing that helps a pitch be and effective pitch. 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.
Average Spin Rate: Does It Help Unearth Successful Sinker Performances?:
In trying to answer this question I looked over the data sets from 2015 (59 starting pitcher performances), 2016 (45 starting pitcher performances) and 2017 (39 starting pitcher performances) and I do not see in the numbers a noticeable pattern that specifically ties certain average spin rates (low, high, medium average spin rates) to batting average against or whiffs% (whiffs divided by swings) performance results. So, I do not count myself among the proponents that believe a low average spin rate is required to be successful with the sinker. Oh sure, a starting pitcher can be successful with a low average spin rate on a sinker. However, it is seemingly equally likely that a starting pitcher can be equally as successful with a high average spin rate on a sinker.
So, How Then Do We Use This Data To Find A Pattern?:
The next statistic I like to gravitate to is whiffs%, and specifically whiffs% of starting pitchers that have the confidence to throw the pitch a high number of times. When looking at the 2015 and 2016 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 1000 or more times in that season the ones that had the top whiffs percentages (whiffs%) typically had lower batting average against numbers.
CC Sabathia (2015): 1021 sinkers thrown, .316 batting average, .366 BABIP, 19.6% whiffs%
Hector Santiago (2015): 1916 sinkers thrown, .208 batting average, .249 BABIP, 18.4% whiffs%
Steven Matz (2016): 1319 sinkers thrown, .265 batting average, .335 BABIP, 21.5% whiffs%
Hector Santiago (2016): 2023 sinkers thrown, .231 batting average, .250 BABIP, 20% whiffs%
CC Sabathia (2016): 1017 sinkers thrown, .310 batting average, .327 BABIP, 19% whiffs%
Brandon Finnegan (2016): 1547 sinkers thrown, .258 batting average, .267 BABIP, 18.5% whiffs%
Jake Arrieta (2016): 1384 sinkers thrown, .201 batting average, .227 BABIP, 17.8% whiffs%
Analysis: In the above list of 7 starting pitcher performances all of the performances had whiffs percentages (whiffs%) of 17.8% or higher with a minimum of 1000 sinkers thrown in that season at the major league level. Despite a high whiffs% CC Sabathia had the 2 highest batting average against performances in the group (.316 and .310). The next highest was the .265 by Mr. Matz in his 2016 MLB performance.
The conclusion for me on sinkers, as far as an easy to sort pattern, is this: Look for the starting pitchers with the sinkers that have the confidence to throw it 1000 or more times in a season. Then look for the ones that have a 17.8% or higher whiffs% on the pitch. Chances are good that the starting pitchers who meet those standards have a very good or better sinker 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. Unfortunately, that simply wasn’t the case when looking at the sinker.
Tomorrow we will be finishing the starting pitcher pitch series by looking at the changeup. It will be the 8th pitch that we will analyze after looking at four-seam fastballs, cut fastballs (cutters), curveballs, sliders, knuckle curves (spike curves), two-seam fastballs and sinkers. 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.