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Tuesday, October 18, 2011
Landing/crossing spot of uncaught pitches
Great stuff from Bojan, who models the wild pitch / passed ball scenario into two types: those that actually land in front of the catcher, and those that don’t. It adds a level of complexity, but it represents reality, so I’m very happy he went the extra mile here:
Now that we see how he models it, we get the payoff. If you can mentally “fold” it at the line, you can do so if that helps:
Then he has a ton more good stuff. And the payoff to see the impact by catcher, where you want to focus on the last column that shows that we’re talking about 4 runs of value:
We can compare to data I produced here for 1978-1990, and see that, other than Bruce Benedict, the best catchers saved 15 “passed pitches”, which converts to around 4 runs.
If we both end up in the same place, then why go to the lengths Bojan did? Well, two good reasons. Number one is we learn, and for that Bojan does a fantastic job. Number two is that his model can pinpoint things with a much smaller sample size than what I would need.
Remember the thread I had yesterday about fielding opps not created equal? The same applies here. Whereas after a few years, we’d expect all catchers to have the same kind of catching opps (after adjusting for the identity of the pitcher), a CATCHERf/x type of system would require a far smaller sample.
Here’s his list for worst catchers at blocking:
He also shows the correlation, and from there, we can actually figure out how much to regress the observed sample. The average sample size was over 6000 pitches for each catcher in each bucket. To figure out how much to regress, you do (1-r)/r * N. Since r=.68, you add about 3000 pitches of league average performance. It looks like there’s around 40 pitches per game in his sample, so we’re talking about adding around 75 games of league average performance to get from observed rate into a true talent rate. That is, r=.50 when G=75.
Anyway, this is in the running for my favorite research piece of the year.
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