Tag: PITCH f/x

If you’ve made it to Part III in the search to identify potential breakout pitchers, congratulations.  If you missed them, you can find Part I here and Part II here.

Enough Talk, Where Is This List Of Potential Breakout Pitchers?

I’ve uploaded an Excel file to Microsoft Sky Drive.  You can edit, view, or download the file for your own uses.  It’s mostly the same data from the YouTube video, but I added a lot of bells and whistles.  A red cell indicates a pitch that has declined in use from 2012 to 2013.  A green cell indicates a pitch with more usage.  The color intensity indicates the magnitude of the change.  The links to the right take you directly to that specific player’s page on BrooksBaseball.net.

Disclaimer #1

Keep in mind, I started this analysis on June 24th, 2013.  So if you’re finding this information after that time, you may want to double-check the usage graphs for any pitcher you’re researching.  But I’ve tried to document the approach to doing this research in the video and other parts of this series.  You can perform this research at any time (it would be great if we could get monthly usage reports from Fangraphs, then we could do this in the offseason to identify pitchers who started to change their mix late in the season).

Disclaimer #2

You saw from Part II of this series that these changes in mix have to be taken with a grain of salt.  And even after you’ve verified that there is indeed a change in pitch mix, you still need to go review the effectiveness of the pitches being used more frequently.  I wish I could go through each of these pitchers and break them down for you.  But it’s just not practical (my two-year old and four-year old don’t find PITCH f/x research very entertaining).  Hopefully I’ve equipped you with the tools you need to go analyze these pitchers more closely.

For pitchers on your team, check them out.  If you’re thinking of picking up a free agent, check him out.  If your pitching staff is terrible and you need to find the next big ace, check them all out.

Conclusion

Granted, it’s a small sample size.  But I’ve done a deep look in this fashion for Edward Mujica, Max Scherzer, and Alexi Ogando.  And all show promising results.  There will certainly be pitchers that change their mix and it has little effect on their end results.  But this seems like a promising exercise.

PLEASE LET ME KNOW WHAT YOU THINK Or If You Have Questions

I realize this is quite involved.  It’s certainly more difficult than reading the weekly pickups columns that are out there.  But anyone can read those and snag players just as easily as you can.  This process will put you ahead of the curve, give you players to monitor, and give you first crack at picking them up.

Thanks and be smart.

 

Picking up where we left off in the post “Use PITCH f/x Data to Identify Potential Breakout Pitchers“, now that we’ve identified the potential pitchers (link to pitchers with differences) who have added a new pitch or that have significantly adjusted their pitch usage mix in 2013, we need to determine if the new or more heavily used pitch is successful.

Before We Go Any Further

I think it’s important you read the article The Internet Cried A Little When You Wrote That On It, by Mike Fast (follow Mike on Twitter).  The whole article will be helpful if you’re trying to improve you understanding of PITCH f/x, but at least read bullet #1.

My takeaway from that piece is that significant changes in pitch mix, especially within the fastball classifications (FA, FT, FC, FS, SI), are most likely to be changes in the algorithm used to classify the pitch.

Take for instance, Jake Peavy.  The PITCH f/x data I downloaded from Fangraphs and manipulated to identify “potentially” new pitches, shows the following for Peavy:

Interpreting that chart, from 2012 to 2013, the Fangraphs data shows a decrease in the fastball (FA) of 19.9% and increase in the two-seam fastball (FT) of 26.7%.  That sounds interesting on the surface, no?  Decrease one pitch 20% and increase another?

From Mike Fast’s article we know that we can’t necessarily trust the pitch classifications.  So let’s look at the 2012 velocity and spin on Peavy’s pitches:

And the same for 2013:

From these two charts you can see Peavy’s throwing the same pitches in 2013 that he was throwing in 2012.  The clusters are in the same general vicinity on the chart.  But more importantly, you can see there is very little difference between the fourseam (FA) and the sinker (BrooksBaseball calls the two-seam fastball a sinker (FT)).     So a 20% transfer from one classification to another is likely a change in the algorithm, as we were warned.

Give Me Someone Else to Look At

Alright, Alexi Ogando, although injured recently, has been intriguing.  The raw data shows a sharp decline in fastball usage and an increase in the changeup.  This probably isn’t just a case of an algorithm change (fastballs wouldn’t likely be misclassified as changeups).

Let’s look at his 2012: Continue reading “Use PITCH f/x Data To Identify Potential Breakout Pitchers (Part II)”