Pitching Clusters and Batters facing Them

Since the last post on clusters of pitchers I have done two things or at least started to do them.

A) Start to make more clusters which I will share with you in this post.

B) Look at some batters to see what their career stats are v.s. these clusters.  The reason I have began this portion of the project is that announcer line.  "Johnny is 0-4 against Chris lifetime"  I can not stand it, so to remove some of the bias against small sample sizes, I am clustering similar pitchers and seeing how certain hitters do when facing them.  I intend to preview one batter's lifetime facing the clusters at the end of this post.  Then hopefully make the third and final post containing a lot of batters lifetime facing the clusters.

Anyway, my new cluster must meet the following requirements.

-Left-handed  pitcher

-Groundball pitcher

-70+ innings or starter

-90-87 MPH on Fastball

- 14-18% Strikeout percentage

So far, I have found nine pitchers who meet these requirements and they are:

-Jeff Locke          -Paul Maholm
-John Lannan       -Joe Saunders
-Chris Capuano    -Jason Vargas
-John Danks         -Chris Rusin

Before I go into detail explaining this list, I have to mention Andy Pettitte, who actually qualified for this list.  Andy is the first pitcher, in my doing this cluster thing that actually fits into two clusters.  This is partially my fault for two reasons.

A) I made crappy clusters.

B) Andy is a pretty flexible pitcher.

The way I fixed this was just decided that the first cluster he got put into he would be in that one.  Just a small thing.

Upon researching and finding this cluster, I noticed that Jeff Locke and John Lannan are very similar pitchers.  Right off the bat you notice they have the same initials (J,L) but their similar traits also lead to the stats side too.  Here just a few things that are similar about them.

They both have pretty crummy strikeout and walk rates. 

They both were able to fool people into believing they were good pitchers in their rookie year.

I predict them both to be 6th or 7th starters in the future, because John Lannan already is one.


Another thing about these pitchers was that they were all spread out on their career maps.  For example Chris Capuano is old and trying to stick, John Danks and Joe Saunders are going through a mid career crisis.  Jeff Locke and Jason Vargas were both dramatically overvalued by their teams based on luck and stupidity.  I mean who gives Vargas a 4 year/32 million deal?  Somehow I think this cluster is predictable because well their careers all appear to be heading into the same direction fringy.

Deservedly so I called this group the 'Fringe Lefties'


 Now for the batter connection to these players.

I am going to use Chase Utley for a little preview.  Make sure when doing this, that you pick players who have played a few years in the major leagues or else you'll be back at square one with the small sample size problem.  Also before you read this preview I would read the first Pitching Clusters so you know what clusters were talking about.  You can find this post right here http://mlbrumblings.blogspot.com/2014/03/pitching-clusters.html

Young Righties	Fringe Lefties	Velocity Busters
Chase Utley 8/31 .258	Chase Utley 30/109  .275	Chase Utley 3/21 .143

You might notice the young righties relatively small sample size, this is for two reasons.

A) I trimmed the list by adding a strikeout rate requirement so the remaining pitchers on young righties are now Andrew Cashner, Matt Garza, Ivan Nova, Tyson Ross, Zach Wheeler, Chris Archer, Jordon Zimmerman, and Micheal Wacha. 

B) The righties are young.

Thanks for reading.  Look for Pitcher Clusters Part Three.

Pitching Clusters

What do all of these pitchers have in common?

Joe Kelly
Tyler Chatwood
Andrew Cashner
Edwin Jackson
Tyson Ross
Zach Wheeler
Jordon Zimmerman
Micheal Wacha
Ivan Nova
Nathan Eovaldi
Matt Garza
Chris Archer
Jared Cosart

They actually have several things in common and these are

A) They are all quite young with the exception of Garza and Jackson.

B) They all have a 3.50 ERA or lower besides Garza and Jackson.

C)This is a list of some good young pitchers.

You may be asking yourself, why are Matt Garza and Edwin Jackson even on this list.  They meet the requirements to be on it is my answer,  Here the things that got these pitchers on this particular list. 

- Right-handed pitcher
- High MPH 93+
-Starting pitcher
-Ground ball pitcher (More groundballs then fly balls)
-Throws their fastball 60% or more of the time

All of these pitchers and most likely a few more that I may have missed meet this.  This is just one example of a pitching cluster.  A pitching cluster is a group of pitchers who are very similar not by their stats, although in this cluster it worked out that way, but also similar traits.  The book that I got this data from, the Bill James Handbook 2014, had a few pages before showing most of this data which explained several things.  A) Fastball MPH is not correlated with pitcher success very much.  B) Groundball % does not make good pitchers.

I would have to say though on the topic of B. that this point is false.  According to these pitchers, GB % is a good factor in how they succeed.  However, we can take my argument with a grain of salt because this is a small sample of pitchers. 

Continuing on the topic of pitching clusters, I will show you the 2nd and final cluster I have figured so far. 
The following pitchers are in this cluster.

-Travis Wood
-Andy Pettitte
-Mark Burhele
-Cliff Lee

The Traits
-Lefty
-Low fastball velocity
-Starter
-Similar repertoire of pitches

I couldn't really make a Groundball, Fly ball requirement because these pitchers were all mixed.  Travis Wood is an extreme fly ball pitcher and somehow he survived and thrived in the extreme hitter's park that Wrigley Field was in 2013 with a lower 3 ERA.  However his FIP said he might have outperformed his skills a bit.  I don't believe that FIP is park-adjusted however so to me wouldn't Travis pitching in a hitter's park and his luck in FIP about balance themselves out?  It makes sense to me but I don't really know. 

Thanks for reading, I will have up a post on Luck and Where to Find it (Part 2) and I might do another pitching clusters one.

Luck and Where to Find it (Part 1)

What is the law of luck?  Luck is everywhere and everyone has more of it then you do. 

First, how do we determine a pitcher's luck?  The first theory came from Voros McCracken.  He noticed something that surprisingly nobody had ever realized before.  Pitchers can not control balls that are put into play.  It was quite a bold statement.  The only components that they could control were:

-Homeruns
-Strikeouts
-Walks
-Hit by Pitch and other rare events

He noticed that a pitcher's BABIP (Batting Average on Balls in Play) vary greatly from season to season.  So, he invented DIPS (Defense Independent Stats) This was another thing to look at along the lines of ERA.  Since this discovery over ten years ago the saber metric community developed FIP which I have mentioned on this blog before.  And for this discovery, Voros now lives broke and alone.  He said he's happy to have done it, and now spends his day cherishing over international soccer. 

Soon, we will take a look at pitcher's that have experienced lucky and not so lucky seasons.  Thanks for reading.