chances is…

Sorry for the long interval between entries – I hope to get back to posting on more regular basis. Continuing in the vein of my previous two posts, I’m still working my way towards baseball win expectancy, but I’m going to pause to examine run expectancy in a more detailed manner.

First, let’s look back at the run expectancy matrix from my last post. It was built by looking at each time a given base-out state occurred, and seeing how many runs were scored in the remainder of those innings (by utilizing the FATE_RUNS_CT field from Chadwick). I will refer to this as empirical run expectancy, as it is based on how many runs were actually scored following each base-out state.

Run Expectancy Matrix, Empirical
BASES	0 OUTS	1 OUT	2 OUTS
___	0.539	0.289	0.111
1__	0.929	0.555	0.24
_2_	1.172	0.714	0.342
__3	1.444	0.984	0.373
12_	1.542	0.948	0.464
1_3	1.844	1.204	0.512
_23	2.047	1.438	0.604
123	2.381	1.62	0.798

(more…)

I have created a Perl version of Tangotiger’s excellent Markov run modeler. Tango’s original HTML/Javascript version can be found here, with further discussion here.

This is just a basic adaptation – I have not added any new features, though I hope to in the future (at the very least I would like to make a Perl version of Bill Skelton’s modification of Tango’s original).

To use my version, first download the zip file (markov.zip), extract the Perl script (markov.pl) and the example input file (input.csv), and place them in the same directory. Change the values in the input.csv file to alter the batting line and the chances of taking an extra base (but make sure not to alter the formatting of the file). Then just run the Perl script, which will produce a file named output.txt that is tab-delimited. If you open that in Excel you should be able to view all the results in table form. For simplicity’s sake I didn’t include any command line arguments to specify the names of the input or output files, so if you want to run the script multiple times and save your results you’ll either have to rename/copy the output file or alter the Perl script (note that the output file does include the input values inside it).

For those unfamiliar with Markov models of baseball, there are a lot of great resources on the web. Outside of Tango’s site, I recommend work by Mark Pankin, Joel Sokol (includes Matlab code), Bruce Bukiet (scroll down for “A Markov Chain Approach to Baseball”), Carl Morris, John Beamer (includes Excel spreadsheet with purchase), Tom Ruane, and Berselius (includes Matlab code, though link appears to be down).