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Reworking WARP 

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Why and how replacement level has shifted over time.

Last week, we talked about what replacement level is, and why we need it. Now let’s talk about who replacements are, and how to find them.

What we uncovered last time is that, rather than being some wholly arbitrary baseline concocted by evil sabermetric geniuses, replacement level (and the consequences of replacement level-based analysis) in fact grow out of something fundamental to the game: the distribution of talent in baseball, and the limited number of roster spots available. So the question becomes, how do we measure the distribution of talent? What are some things we need to make sure we’re capturing?

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Or why none of the alternatives to replacement level works as well.

Last week, we talked about different ways to measure offensive performance. Let’s talk some about baselines. The past few weeks have had a lot of math; this time I want to step back and talk some theory (although we’ll have a fair amount of math as well.)

What’s funny is that sabermetrics is regarded as being about math first, when really the heart of the thing is theory. I and my fellow travelers have been accused of “ruining” the game with numbers. But from its earliest days, the spread of baseball was as much about newsprint filled with columns of numbers in agate type as it was about the stories written about the game. Numbers have always had an incredible power to tell us about the game of baseball, and that was as true in 1913 as it is in 2013. Scratch any columnist who talks about how stats are ruining the game and you can find a voluminous knowledge of the history of the game as told in numbers, of the records people hold and the records people haven’t managed to break.

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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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How accurate can we be when comparing hitters' performance?

Previous Installments of Reworking WARP
The Series Ahead [8/21]

When I started working on a series about revising WARP, I didn’t expect to have much to say on the subject of offense. Measuring offense is probably the least controversial part of modern sabermetrics. So why start here? I have a few reasons:

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What we'll be doing with WARP in the coming weeks, and why.

The hardest part of explaining sabermetrics to someone who’s versed in traditional baseball stats is explaining that they’re different not just in degree, but also in kind. The definition of an RBI, for instance, hasn’t changed since it was made an official statistic in 1920. The stats created by sabermetricians are much more prone to revision. Some look at this as a bug, because they view sabermetrics only as potentially better versions of traditional stats.

But sabermetrics isn’t ever a finished product. (This is not, in fact, a bad thing.) So instead of expecting our stats to calcify, we should be expecting them to grow and change as we develop the ideas beneath them. So it is with WARP, which has undergone any number of changes over the years. And now we’re going to be changing WARP again. But we’re going to be throwing open the doors and letting you watch us while we work. So we’re kicking off a series of articles, running each Wednesday, where we’ll take you inside what we’re doing. There’ll be a lot of math, but also a lot of discussion about what WARP is trying to measure and the philosophy behind various choices.

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