Player Cards

After a weekend of banging my head against the wall trying to figure out how to properly normalize the acceleration I needed a break. So back to just looking at home pitches for pitchers. I decided to skip ahead to the next thing I wanted to do which is upload some player cards. Basically, the plan was to use the PITCHf/x data to create a plot of the type of pitch each pitcher throws and then start to expand from there. What I needed was a clustering algorithm that could look at all the pitches thrown by a pitcher and classify them. I am not going to go into details about the algorithm as it still needs some fine tuning (as you will see below) but basically it examines every pitch and correlates speed and movement into clusters. Once it has those clusters for each pitcher it finds the pitcher's fastball and then calculates what his other pitches do in comparison to the fastball. It then compares his other offerings to all other pitchers and tries to guess what the other pitches are. Sometimes this algorithm preforms well.
First, these plots show the movement of the pitch not the location. For a great description of what exactly this means read this excellent article by John Walsh. This is exactly what you would expect from the hard throwing Broxton. He has a great four seam fastball and what can be a devastating slider. His change though, is a work in progress. It doesn't have nearly the same movement as his fastball which helps tip the pitch to opposing batters. Because of this, you can see he doesn't throw it very often.

Sometimes though the algorithm can get messed up. This mostly happens in two ways. First, the clustering gets over active and combines two pitches that really are different.
Saito appears to be throwing two varieties of fastballs (two seamer? cutter?) but the clustering algorithm combines them into one type. This mostly occurs when the speed of the two pitches is very close. You can see that Saito's splitter and his curve are about as far apart as the two fastballs but the algorithm correctly separated them. The other failure is sometimes the algorithm will misidentify a pitch.
It is my understanding that Oswalt throws a slider not a split finger fastball but the pitch seems to move more like Saito's split flinger fastball than Broxton's slider to the algorithm. Also, one pitch that Oswalt threw didn't seem to match up to anything and just got left out. Looking from the movement on the pitch it probably is a fastball but it could be a change. Missing one pitch from Oswalt really isn't a problem but a few pitchers have clusters of pitches that aren't combined. Rich Hill is an example of this.
Again, Rich Hill throws a slider not a splitter but the horizontal movement gets the pitch classified as a splitter. If the group of unidentified pitches were added in maybe the pitch would be correctly identified. Sometimes all hell breaks loose and the algorithm falls apart.
The great Greg Maddux who throws nothing but fastballs. So what is going on here? Well the clustering algorithm really needs some space between the types of pitches and Maddux doesn't really provide any. What I mean by that is Maddux will throw his fastball at a wide range of speeds. The low end of that range is very close to the high end of the velocity on his change. This provides a bridge for the clustering algorithm to lump them all together. The unknown points in the bottom right are some type of off speed pitch but it is unclear what. Lastly, we can look at the worst case scenario, the knuckleball.
Here the algorithm really doesn't have a chance. It does a good job of separating the knuckleball from the fastballs and most of the knuckleballs are grouped together with a few wrongly grouped at the edges. The problem comes in comparing Wakefield to other pitchers. Without any other knuckleballers in the league to compare him to the algorithm is lost and just throws out a guess and calls the pitch a slider.

Anyway, here is where you come in. I have uploaded a plot for every pitcher who has thrown more than 100 pitches in their home park while PITCHf/x was on. If your favorite pitcher is missing don't worry, hopefully I will soon have a good league correction and can add in the away stats. What I need is you to look over plots and tell me where the algorithm has messed up. If the algorithm has combined two lumps of pitches that you think should be separated let me know in the comments below. If the algorithm has incorrectly identified a group let me know. If there is something ascetically unpleasing about the graphs or if there is something you would like to see me add to them let me know. If you would rather email me than add a comment my email can be found under my profile to the right.

The whole process of going from downloading the data to producing the plots takes nearly half a day. The clustering algorithm itself takes over three hours on my super fast desktop. The moral of the story is I am going to stick with this data set for at least a few more days as I try to hammer out the kinks to the algorithm. Maybe this weekend I will do a full update.