Run Expectancy and Markov Chains
Sunday, August 14th, 2011Sorry 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 |