I am ambivalent, if you hadn’t guessed already, about the popular use of statistics. Dan Kahneman, among others, showed that people are inherently bad at statistical thinking. My contention on this point has been that it is not simply that people don’t understand the basic statistical concepts, but that the subtle implications run counterintuitive to how people normally think. The powers of statistics is that it summarizes a complex data into a neat, simple package, PLUS it provides a summary statistic of how wrong that package is, conditional on the sample. Everyone loves the simple package, not the fine prints that comes with it.
This article on Fivethirtyeight strikes me as emblematic of exactly this sort of seemingly profound misuse of statistics. I don’t doubt that, in general, pitchouts, sac bunts, and other self-sacrificial baseball strategery has been fairly ineffective. But I also find it hard to believe that they are universally ineffective, period. The more likely scenario is that the use of such strategery has generally included a small proportion of useful applications mixed in with many ineffective ones, with the situations difficult to disentangle through simple statistics. Thus, the average sac bunt in the sample of the past was indeed ineffective, but with a small but meaningful if only it could be identified subset that was effective.
This is not an argument for a blanket condemnation of sacrifice bunts, then, but a call for better, more nuanced research. What are the effective use of sacrifice bunts? How do you sacrifice bunt more in situations when they are actually valuable and avoid using them when they are counterproductive? One might suspect, in fact, that, if the baseball people are not as stupid as naive sabermetricians assume them to be, and are actually combining their traditionally minded skills with sabermetric insights productively, ineffective sacrifice bunts have been eliminated from the sample much faster than the effective ones. So has the value of sacrifice bunts changing over time, then? One might think it should be so, and we’d be able to tell if we have developed a useful methodology for distinguishing effective sacrifice bunts from ineffective ones, rather than trying to place a value on the average sacrifice bunt the way so much misuse of statistics winds up becoming.