Sunday, 21 June 2009

Primer Cross Post: The Chaos Theory of Sabermetrics

I've decided to cross-post some of my contributions at Primer to this blog. That way I can keep better track of some of my important reflections.

Brian Joseph, who may have been involved with Baseball Prospectus Idol (which I didn't follow, I think the whole idea of 'Idol' is stupid), made a stab at attacking sabermetrics here. It's a pretty poor effort, to be brutally honest; but I think I see where he's going with it. He wants more granularity in sabermetrics. A Primer discussion broke out here.

The most misguided point of Joseph's argument is here:
The notion that sabermetrics is truly objective is silly when there are a number of ways to “objectively” look at a situation statistically depending on your subjectiveness toward the game.

This statement is, I believe, based on a misunderstanding of what it is to be objective. And all the rest of the article's problems arise from here. I suspect that if 'objective' was replaced with 'scientific', the author would not have misunderstood. 'Scientific' refers to a method, nothing more, so history can be scientific. Sabermetrics sometimes is not purely scientific. (Think of James's 'subjective factor' in the New Historical Abstract.) But that's rare.

Joseph then wanders into various specific examples, which unfortunately don't clarify the matter. One problem is that 'neo-sabermetrics', to borrow a term from Don Malcolm, is concerned with evaluating True Talent Level. Joseph is arguing that on a day-to-day level, True Talent Level doesn't actually explain very much. Well, anyone who thought about the matter probably knew that already. But True Talent Level isn't the only way to use use sabermetric studies.

It's always worth reminding ourselves that Bill James didn't start from wanting to know how good players would be, but rather how good they had been. Malcolm and some other members of the Big Bad Annual (BBBA) crowd, which included Primer's own Jim Furtado, were sort of feeling around the theoretical foundation that the game, not the season, is the cornerstone of performance analysis. Then BPro's great success and certain unprofessional characteristics of BBBA strangled that initiative, not quite at birth, but in late childhood. However, many of those basic concepts are still out there. James himself gave us the Game Score for pitchers, but I don't find that helpful. I don't want a number in that way, I prefer the categories of the Quality Matrix. The same with the idea of Leverage for bullpens. Leverage, and the related Win Expectancy, can tell us everything we need to know about what succeeded in a victory or what failed in a loss. Start totting that data up in columns and there's a handy explanation of a team's strengths and weaknesses.

Pecota, Zips, Chone and Marcel are great tools, but they are literally only half the picture.

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