I spent most of Sunday working on the upcoming BP 2K10 annual, specifically writing player comments, and trying to keep in mind the marching orders given us by our editors, Steven and Christina: always try to answer the question “why.” For instance, anyone can say that Joe Shlabotnik is due for a collapse; at Baseball Prospectus, and at myriad other fine analytical sites, we tend to say that Joe Shlabotnik is due for a collapse because his .448 BABIP is unsustainable. It’s that second clause, the desire to understand and explain not just what happened but why it happened, that brought most of us to baseball analysis in the first place.
While I was writing yesterday, the Packers-Steelers game happened to be on in the background, and I was doing a good job of ignoring it until the following Joe Buck gem bled through: “We’re now going to show you a few stats which are good examples of why statistics can be so misleading, and why we don’t tend to put a lot of stock in them around here.” Or something very similar to that—my personal Tim McCarver filter generally extends to anyone who’s spent a lot of time sitting in close proximity to him, so Buck’s voice always sounds to me like it’s coming through ten feet of foam insulation, even when he’s covering football. Anyway, Buck went on to say something about how the Steelers are going to have a 4,000 yard quarterback, a 1,000 yard running back and two 1,000 yard receivers, yet were still under .500 going into yesterday’s game. That was his evidence as to how statistics are misleading and not of much value.
Wow. I mean, wow. Now, I haven’t followed the Steelers, or the NFL itself in fact, very closely this fall, but as soon as Buck had said his piece, I felt certain that the Steelers probably had either a bad defense (unlikely), had struggled in the red zone (untrue) or had a negative turnover ratio (bingo!). Turnover ratio is a statistic, isn’t it? So couldn’t statistics be successfully used to explain why the Steelers have struggled? “Statistics” themselves aren’t misleading—but certainly those who have no clue how to use them are. Calling statistics misleading and useless when you haven’t taken the time to actually use them to assemble a complete picture is like deriding a bow as a useless tool when you haven’t bothered to actually procure an arrow, knock it and fire it.
Sports fans deserve better than that, and that’s what we here at BP, and the fine analysts at places like Fangraphs and The Hardball Times, and columnists like Rob Neyer and Joe Posnanski and a host of others, try to provide. You might say we try to speak Tuyuca, a language of the eastern Amazon, recently described in The Economist as the most difficult language on earth:
Most fascinating is a feature that would make any journalist tremble. Tuyuca requires verb-endings on statements to show how the speaker knows something. Diga ape-wi means that “the boy played soccer (I know because I saw him)”, while diga ape-hiyi means “the boy played soccer (I assume)”. English can provide such information, but for Tuyuca that is an obligatory ending on the verb. Evidential languages force speakers to think hard about how they learned what they say they know.
We’re lucky here at BP, because each time we present a theory, or propose an explanation, our readers question us, challenge us, and often inform us. They force us to think hard about why we’re saying what we’re saying, to try to write our pieces in Tuyuca, and for that we’re grateful. If only all sportswriters produced their work in Tuyuca, or all sports broadcasters were fluent in it.