Understanding Pitch F/X: Release Point Chart

It’s a common problem in pitching to see pitchers experience difficulty due to improper arms slot.  Either they never had a very good one or something is happening earlier in the motion to throw their arm slot off.  Poor arm slot is generally indicitive of a poor mechanical process elsewhere.  Poor mechanics lead to rushing which not only throws a pitch off, it can hurt a pitcher as well.

For today’s little tutorial we are going to look at a single game from Justin Verlanders 2011 MVP/Cy Young/Better Than You Could Hope To Be season.  Our data set will come from his May 7th start against the Toronto Blue Jays.  (hint:  It was a really good game.)

 

So first off, lets decipher what we are looking at.  In the charts key we see indicated JV’s pitch types.  On the X and Y axis we see distances from the catchers perspective (in feet).  The box in the center of the graph denotes a generic strikezone.  Strike zones by definition can vary from batter to batter so the one shown is more or less just an average and should only be used to create a perspective, not a truth.

There is something about how this data is gathered that is very important to note.  It is not gathered at actual release.  It is gathered at the 50ft. from home plate.  This is important to note because looking at actual release points (aka video) can tell us so much more than any pitch f/x chart ever could.  However, given the absence of solid video sometimes the release point chart serves as an integral part of  my work.

What we want to see when we look at these charts is consistency.  In determining consistency, you must first determine someplace to work around.  There is generally going to be at least one good sized clump on the chart.  The center of that clump is going to be where you determine the consistency of the release from.  Deviation from that main point is the determing factor here.  The more pitches that deviate and how far they deviate from the main point, the worse the release is.

Looking at JV, I’d pick (-1.75, 6.25) as his general release point.  All his pitches are being picked up within approximately a foot of that point.  Given the nature of the data collection, magnus effect (pitch movement), and any other immeasurable factor I’d say that this a very good release.  It’s very deceptive to come around with so many different pitches, but have them all fly at you from the exact same spot.

For general purposes I’d have to say that falling within that 1ft deviation is very good.  The rating gets worse the larger that number gets and by the time you hit 2ft, you should probably be hitting up the minor leagues for some mechanics work in my opinion.

So when I see a bad release point, I know that there is a larger mechanical issue at stake here.  It’s at this point that I hope to find some video so I can play amateur pitching coach and tell the Big Leaguer what exactly he’s doing wrong.  All from the comfort of my couch of course.

Understanding Pitch F/X: Pitch Virtualization Charts

It is normal when evaluating a pitchers mechanics to take a video, slow it down, freeze it, and dissect the nuances of the motion.  Personally, I’ll look for positioning of the limbs, rotation of the hip, stride length, arm movement, etc.  It’s basically the most effective way to truly evaluate a pitcher.  Unfortunately, I don’t always have video to use.  I either don’t have access to it or I just have poor video to work with.  However, there is still a way for me to make strong guesses into how a pitcher is acting.

Pitch F/X are a very useful tool and are essentially the best tool for seeing just how well a pitcher is actually pitching.  They describe everything from how the ball locates and how fast it is thrown to how the ball spins and moves through the air.  These are things that are sometimes difficult to pick up in a video and are things that anybody can use to reinforce an opinion in attempt to establish new fact.

However, they can be tricky because the charts do not work how our brain wants them too.  There are things in the charts that must be understood before reading them and hopefully reinforcing ourselves.

So to help teach myself as well as my faithful readers, we’ll follow the best of the 2011 MLB season:  Justin Verlander.  To begin the tutorials we are going to look at the Pitch Virtualization charts at Texas Leaguers.

 

 

First to describe the basic information on the charts.

The box on the right side are the pitch types.  It shows JV featuring five different pitches.

• FF:  Four Seam Fastball.  This is a standard straight moving fastball that generally features greater velocity.
• FT:  Two Seam Fastball.  A very popular pitch because it mixes a fastball’s velocity with some movement.
• SL:  Slider.  A pitch that breaks laterally and down with a moderate velocity.
• CU:  Curveball.  A pitch that dives downward as it approaches the plate.
• CH:  Changeup.  Essentially a slow fastball.

I know many of my readers didn’t need me to tell you how these pitches act, but it’ll prove a useful resource when we actually start looking at the data.  In need of less explanation are the X and Y axis on the chart.  The X describes horizontal distance in feet (from release to glove) and the Y denotes height in feet (from release to glove).

So based off all that information everybody here has a bit of a grasp as to what those charts mean and what they describe.  In a nut shell they show how a pitch moves from release to the catchers glove and when the stages of movement happen over a distance.  One important piece of information to remember though are the charts titles.  Each contains a very key word:  Virtualization.  Virtualizations aren’t always representative of what actually happens.  In these charts they tend to represent what should happen.

So where does the virtualization come from?  The answer is data.  At the top of  Texas Leaguers Pitch F/X pages you can find the averages for their collected data.  Vertical and horizontal positioning, spin rates, spin angles, velocities, and pitch usage are all available averages for you to look at.  From this information a virtualization can be created showing us how the pitch should act.

Now maybe you’re asking a new question:  What good does it do us if it isn’t showing exactly what’s happening?  Simple answer is comparison.  What should happen is useful data when compared to what is actually happening.  But, I’m not gonna write a ten page paper at 11:30 at night (save that for my college professors).  You’ll just have to learn more in one of the upcoming segments where I will further describe some of the information that those data sets infer.