This is the second article in a series of articles about the beautiful creation known as Statcast. I encourage you to check out the previous article on four-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’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 cut fastballs, also referred to as cutters.
The Belief With Cut Fastballs:
The belief with cut fastballs is that they need a high spin rate to be an effective swing and miss pitch. We will be looking at that today and how spin rate affects batting average of the hitters faced.
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 pitches thrown in that season (although I dropped that down to 10 in the current season).
Average Spin Rate & Cut Fastballs:
The first statistic that I am looking at is the average spin rate of the cut fastballs in the data sets. In my 2016 data set there are 62 starting pitchers that qualified.
The first lesson that we learned in the previous article, in regards to four-seam fastballs, is that, even if you don’t have elite level spin rate on the four-seam fastball as a pitcher, that doesn’t mean you are doomed to it being an ineffective pitch. That lesson applies to cut fastballs as well.
The second lesson that we learned with four-seam fastballs is that, even if you do have elite level spin rate on it as a pitcher, that doesn’t mean you are guaranteed for it to be an effective pitch. Well, that lesson holds true here as well. One example from last season is Josh Tomlin. He had the second highest average spin rate (2610) in the 2016 data set for the cutter but had a .320 batting average on the pitch. He threw the pitch 996 times (the most in the data set). It was swung at 562 times and produced 101 whiffs. That led to a 18% whiffs%. Fast-forward to 2017 and Mr. Tomlin has thrown the pitch 537 times with a hitter swinging at it 291 times and it producing 64 whiffs. That is a 22% whiffs%. The higher whiffs percentage is part of the reason the cut fastball has produced a lower batting average (.299) this season compared to the .320 in 2016. This has happened while his average spin rate has dropped a bit to 2566.
What Else Could Have Led To Mr. Tomlin’s Lower 2017 Batting Average?:
The other reason, other than an increased whiffs%, for his batting average allowed being lower this season is likely not just one reason. As I mentioned in the previous article the answer is not simple. There are many 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 is hit, how hard it is hit, BABIP (Batting Average on Balls In Play) etc. One statistic I like to look at is BABIP (Batting Average on Balls In Play). Well, this time around BABIP doesn’t explain the lower batting average allowed in 2017 as his BABIP this season (.364) is higher than the .333 he had in 2016. The velocity (85.7) is almost identical to what it was (85.9) in 2016, so that isn’t the reason either. Maybe his ISO (down to .159 after the .218 he allowed last season) is the reason. Hitters, overall, aren’t hitting for as much power against the pitch this season. That could be a driving reason, among others mentioned above. Sometimes we don’t have all the answers.
Can Average Spin Rate Predict Batting Average Allowed?:
In the four-seam fastball article I did put forth some average spin rates that I like to see from a starting pitcher. The data showed enough of an overall pattern to allow me to put value in average spin rate as a tool in the player analysis toolbox when it comes to that pitch. Unfortunately, with cut fastballs, well…that isn’t the case…at least with batting average allowed.
Can Average Spin Rate Predict Whiffs%?:
What I can say is that so far this season, of the players in the data set with an average spin rate of 2500 or above, 9 of the 11 starting pitchers had a whiffs% of 22% or higher. Of the remaining 68 starting pitchers in the 2017 data set 25 had a whiffs% of 22% or higher.
In 2016 there were 5 starting pitchers in the data set with an average spin rate of 2500 or above. Three of the 5 had a whiffs% of 22% or higher. In that data set 57 starting pitchers remain. In that group 22 had a whiffs% of 22% or higher.
In 2015 there was only 2 starting pitchers in the 69 player data set that had average spin rates of 2500 or above on the cut fastball. Both of them had at least a 22% whiffs%. Of the 67 other guys, well, 34 of them had whiffs percentages of 22% or higher as well.
Is what we are seeing in 2017 a new trend when it comes to increased whiffs% and starting pitchers with a cut fastball at or above 2500 average spin rate? Or, rather, is it just an outlier and do the 2016 and 2015 full season data sets tell the real story…and that is simply that we shouldn’t bother to pay much attention to the average spin rate numbers when looking at cut fastballs?
My Conclusion On Cut Fastballs:
Right now, as a fantasy baseball analyst, I’m not going to pay close attention to average spin rate for the cut fastball. Even pitch velocity with the cut fastball, which I looked at, doesn’t give an easy pattern when it comes to the batting average allowed by the pitch.
The conclusion I am currently drawing with the cut fastball is simple. Like any other pitch, well, if a starting pitcher throws it enough to be worthy of analysis, I will see how effective, etc. he is with it from season to season. That will continue to be a piece of the player analysis puzzle. Unfortunately, though, there just isn’t an easy overall pattern to sort the data sets with that I can provide you with.