Last year, an interviewer asked me why baseball statistics matter.
First thought: They don’t. I just love ’em.
Second thought: That’s not a very good answer, Ben.
Third thought, this one aloud: Mumble mumble. Words?
Now, having had a few months to indulge in l’espirit de l’escalier, I’ve got a tentative answer. It goes like this:
The field of data analytics is conquering the world. Our emotions, our behaviors, our precious bodily fluids – all are becoming subject to statistical analysis (and thence to algorithmic manipulation). It’s cool. It’s scary. And it raises questions.
Questions like: “What might our numbers leave out?”
Questions like: “Do the data confirm old wisdom, or upend it?”
Questions like: “Will experts become obsolete in an era of all-powerful, all-purpose machine learning?”
Questions like: “Do all these numbers dull the poetry of human life? Do they turn the fertile jungle of experience into something cold and gray, like lunar soil?”
And here’s why baseball matters: Because it entered the data analytics era two decades ahead of everybody else. The sport has spent 20 years negotiating, compromising, learning. It models for us the pitfalls and possibilities of statistics.
Baseball shows us how early adopters will grab low-hanging fruit.
It shows us how jealous rivals will blunder along, uncomprehending, in their wake.
It shows us how rich institutions will learn to leverage their resources, perhaps exacerbating inequality.
It shows us how old-school experts will prove wrong about a lot, and right about a lot.
It shows us how the best organizations achieve a synthesis of organic wisdom and statistical analysis, an alloy stronger than either approach alone.
In short, why does baseball matter? Because it shows us what the data analytics era will become.