Analytics is Changing Pitcher Evaluations… You Better Get Ready – Part 1

By Robbie Aviles (RHP Cleveland Indians, Pitching Lab Coach)

As much as some people don’t want to admit it, baseball is turning even more into a numbers game run by machines such as Statcast, Rapsodo and Trackman, to name a few.  The ordinary fan watching a game on TV is getting a glimpse of this when they see Aaron Judge’s exit velo, launch angle, projected distance and exit velocity pop on the screen, or, when they’re watching Aroldis Chapman and instantly see his velo, extension, spin rate, and horizontal and vertical movement.  Whether we like or not, data is changing the landscape for baseball players.  And I can speak to this first hand, from my own experience with the Indians over the past 7 years.

What the fans are watching is just barely scratching the surface of what is going on in the clubhouses and front offices. I came into pro-ball in 2010 at the exact transition point from “old school” to “new school”. When I first started playing, I would never hear a single word about spin rate, z-break or perceived velocity.  Fast forward this past year at the Double A and Triple A levels, I was able to pull up every pitch I had thrown. I could go back and review every pitch’s, location, velocity, pitch type, z-break, perceived velo, movement, extension and spin rate and axes.  Yes, all of it!!  Even more fascinating, I could see a comparison to the MLB average data of similar pitches by other pitchers.

All this data is basically taking the guess work out of scouting. For example, when I throw a curveball in bullpens or in games the pitching coach, coordinator or front office guys don’t have to guess if that pitch will be effective in the big leagues. They’re now capable of breaking down my pitch in detailed numbers. For example, they can look into my curveball’s:

  • Velocity, let’s say 81, then
  • Vertical drop (let’s say 12 inches) then
  • Horizontal drop (let’s say 4 inches), and then
  • Spin rate (let’s say 2800 rpm), and
  • Useful (True) spin.

Since they have all the same data, from just about every pitcher from High A to the Big Leagues, they can just compare it to a similar pitcher, with very similar numbers, and predict my potential effectiveness in the big leagues. Scary, right!  They are predicting my future using analytics before I even get there.

This might seem crazy and unfair, but it makes a lot of sense.  Let me provide an example.  I’m a big sinker ball fastball guy. When my pitching coordinator came to me in Lynchburg Virginia in 2015, he said “your sinker plays in the big leagues and can be an effective pitch up there, so keep working on it”. As he walked away I asked my pitching coach “what the hell does that mean?”  Well, he basically said that my average sinker that year so far had been 93.4 mph, and with the movement (data) I was getting, it compared favorably to big leaguers who had that same movement and velocity.  The data had shown it to be an above average pitch in the big leagues. That also tells them (coaches and front office) to expect a lot of ground balls and very few homeruns.

What the data also showed, however, that I won’t be able to get a lot of swing and misses on it. This leads to more singles on the ground, which in turn leads to more double plays. So, more ground balls meant more singles, but fewer homeruns/flyballs.  Long story short, given my pitch profile, the data had shown that it is extremely important for me to not walk people “ever” because it will be hard for me to get the strikeouts. I will have to live low in the zone, and live arm side with my fastball, cause that’s where it is most effective.  My point with this personal story is that the Indians’ progressive approach with data analytics is helping them with long term development of their pitchers in ways unimaginable before.

Here is another quick example of the power of this newly available information.  I know of a pitcher with an ERA this past year north of 6.0 that has been rapidly moving up the minor league system purely based on his data analytics.  They have already concluded based on his data that he can be successful even though his numbers may not show it yet.  Based on what I know of him, I truly believe he will be successful as well.

As you can imagine this is a lot of info, but the next generation of pitchers will need to begin absorbing this information earlier and earlier.  Given the availability of the data, sooner or later college coaches will begin evaluating college-level pitchers using similar methods and there is no better time to begin evaluating this type of info on pitchers than yesterday.

During the past few years, I have absorbed an enormous amount of knowledge by learning about data analytics and I believe that it’s simply a matter of time before all this comes to down the younger ages.  Those that take advantage of it will have a huge leg up, not only in terms success on the mound but also their long-term development as an elite pitcher.

We’ll get more into this in Part 2 but just look at how much data is available on a typical fastball.  Along with everything else we offer pitchers here at RPP in terms of getting stronger, more elastic and more mobile, you can use the information from a Rapsodo Baseball System in a long-term development plan with pitchers in ways unheard of when I was growing up.

  • Velo: 89.0 mph
  • Total Spin: 1989 rpm
  • Spin Efficiency: 81.6%
  • True Spin: 1623 rpm
  • Spin Axes: 35 degrees
  • Horizontal Break: -3.1 in
  • Vertical Break: +16.7 in
  • 3D Ball Trajectory: Path
  • Pitch Type: Fastball

Not long ago, I heard someone say that “data talks to you”.  Well, it’s true.  It does, you just have to listen and know how to put it to work.  Stay tuned for Part 2, where we will begin reviewing how all this can be applicable to training younger pitchers.

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