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How we can acknowledge uncertainty while minimizing it as much as possible.

Last week, we talked a bit about measuring the uncertainty in our estimates of offense. I hinted at having a few additional ideas on quantifying the uncertainty involved. Let’s examine two different routes we could take, both of which would offer less uncertainty than what we quantified last week.

When we did our estimates of uncertainty last week, we compared the linear weights value of an event to the actual change in run expectancy, given the base-out states before and after the event. What we can do instead is prepare linear weights values by base-out state and find the standard error of those instead. Looking at official events:

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