R Bloggers posts a Sabermetric article

For someone just starting out in either R or Sabermetrics Pitch F/X is a great way to get into the hobby. This post has some simple code that produces interesting images of relevant data. My first introduction to R and Sabermetrics was the Baseball Hacks book and it spurred my interest in both. I also think some of the more interesting data publicly available is the Pitch F/X data because it is performance based data as opposed to results based data typical in baseball. Although I wish they would make the Hit F/X data and the Trackman data available. 

I am working on putting together a Kaggle competition with a sabermetrics theme with Anthony Goldbloom and others. If there is anyone out there who has a suggestion on what would be good to be able to predict I would love to hear from you. I looked at using the retrosheet or MLBAM data for the dataset or anything that includes the Pitch F/X data. I would also appreciate any other recommendations on where to get the data for the contest.

Here is the article from R-bloggers by Millsy with Josh Weinstock

It is a Sabermetrics Weekend so todays post is Sabermetrics

This weekend I am going to the Sabermetrics Seminar in Boston. Some might think that it is strange that I am excited about this given that I never played baseball, and I do not watch many games. However, the analytics being done is baseball is developing and expanding at such a rapid pace there is no way you can enjoy analytics and not be interested. Recently a friend of mine ran into Prof. Bertsimas and asked if he could have a copy of the now famous paper that he wrote predicting the Red Sox would win 100 games this year. Prof Bertsimas asked "are you a fan of baseball?" to which he responded "No, I am a fan of statistics". 

The development of Sabermetrics in the last 30 years has been to look at existing data and try to build predictive models out of that data. It was a good first step and produced some good results. This work revealed that some of the historical statistics, like ERA, were not good predictors of anything so Sabermetricians created statistics that were better predictors. This is all great, and it has taken Sabermetrics to where it is today.

The problem with the data that has been used today in baseball is that it is all result based data. The pitcher threw a strike or a ball, the batter got on base, etc. That is all changing. Welcome to the world of physical data in Baseball. This post on Beyond the Boxscore is a good example. It has taken the improvement of a players performance back to the physical location of his pitch not just that more of his pitches resulted in ground balls, but an attempt to answer why based on data not opinion. The technology exists not only to track data of a baseball as it crosses the plate but within the entire ballpark. First this is going to create an unbelievable amount of data that needs to be in studied in ways not currently used in baseball because of shear volume. Second this data is collected in real time which means the models could be updated in real time. Billy Bean may have had his 3X5 note card in front him, but the manager of the future may be holding his iPad with feedback on up to the last pitch and the suggested options with predicted results of those options.

One of the companies doing this physical data collection in baseball is Trackman. They also recently posted for an R developer. I can not wait to see what is coming!

In Baseball too much data is never enough

A couple of weeks ago I ran across a post for a intern position at TrackMan which uses information of ball flight to improve performance. They have been very successful in golf. In fact I tried one of their units out over the winter. This job post was more interesting to me because it was looking for an analytic intern for baseball. My first reaction was just what baseball needs more data points in a hulking cloud of data.  Bill James and the Sabermatrics guys have already culled and studied the baseball stats to death even throwing out some stats as irrelavent and creating some others that are better predictors of results.

Then I realized the error of my ways. TrackMan is looking to enrich the result data with physical data. So not just if the ball was a strike or hit or even if it was a fast ball or a curve ball, but what was its speed, location and spin at points along its trajectory from mound to plate. This is very cool. In her talk at the NYC Rusers group Amanda Cox presents a heat map of Rivera's pitches crossing the plate versus other pitchers which was a simple piece of the total pitch but explains why Rivera was better in a very clear way (22:00). I believe this has the potential to change the way pitchers pitch and batters hit.