We’re doing Baseball Week again here at Undrafted! If you don’t remember from last time, Baseball Week is what it sounds like: A week where we only talk about baseball. It’s October, and the Division Series are going on, so now seems like a good time to honor our nation’s pastime with a series of posts inspired by this baseball season. Enjoy!
Last week, in Game 1 of the Wild Card round between the Yankees and Red Sox, New York’s manager, Aaron Boone, made the questionable decision to take two lefties, Jazz Chisholm Jr. and Ben Rice, out of the lineup against Boston’s Game 1 starter, Garret Crochet. It didn’t work: Crochet had a dominant performance, striking out 11 over 7 ⅔ innings and giving up only one run. The Yankees did not have a single runner on base between the first and eighth innings — the only hit in that stretch was a solo home run from Anthony Volpe.
The other big move Boone made — pulling starter Max Frid in the 7th — also backfired when Luke Weaver gave up the lead later in the inning. This all left many Yankee fans frustrated, and they knew exactly what was to blame: Analytics.
Beyond the specifics of this debate, though, this shows how the term “analytics” has evolved. Originally, the term was a catch-all expression for the advanced statistics of the Moneyball era, and served as a kind of crude delineation of “modern” analysis and “old-school” analysis. But in this case, there was no such divide. Sitting lefties against a left-handed pitchers is not some modern, sabermetric strategy — managers have been doing it since the days of Montgomery Burns.
This isn’t just a one-off, though. I see this use of “analytics” any time fans or people in the media disagree with something a team or a manager does. The word has evolved to stand in for any baseball decision that someone doesn’t understand. Obviously, this has sowed some confusion. Every time people bash “analytics” because of some dumb move by a manager, there are people who rush to explain what the numbers ACTUALLY say.
But I think this sort of misses the point. This dispute is not about any one particular decision — it’s about the way that advanced stats are used to mystify the whole decision-making process. Fans have been arguing about dumb managerial decisions since baseball was invented, but what’s different now is that the reasoning behind those decisions is opaque and proprietary. Indeed, it often feels as if the managers themselves are not even responsible for the decisions they make. It often seems like these decisions are handed down from on high.
In this way, the new definition of “analytics” is actually better at capturing what the analytic revolution was all about. At first it may seem dumb and imprecise to blame “analytics” every time a manager does something you disagree with. But the changes to baseball highlighted in Moneyball, which eventually made their way throughout the game, were never about one specific statistic or number. It was about a shift in power away from the people on the field — the players and managers — and towards the people in front offices, like GMs and team presidents.
In other words, it was about good, old-fashioned alienation of labor. What happens on the field often doesn’t seem dictated by the players, but by the front offices hoping to drive down labor costs. The culture war between jocks and nerds, between instinct and data-driven decisions, between guts and math has been a distraction from the basic class tensions at the heart of the sabermetric revolution. So while it seems silly to blame “analytics” for every stupid thing that happens on a baseball field, it is no different from people blaming “the algorithm” for the nefarious effects of social media, or blaming “big business” for the ills of capitalism. Perhaps it is misplaced anger, but when people are disempowered by a system, then in the absence of anyone taking accountability for that system, they are going to give it a name. But socialists know that, regardless of the specific name, the issue at the heart of the matter is alienation.