As any college student who also has an interest in sports and especially baseball, I always found a chance to squeeze that into my studies. History? Alright, I’ll write about hockey during the Cold War. Data science? Perfect, I’ll write about lineups and Markov chains. My final essay, actually, was for a class called Computing Cultures, which aimed to “examine how computing technology and culture shape each other” and how “values [are] embodied in the cultures of computing and consider alternative ways to imagine, build, and work with information technologies.”
More often than not, norms of technology are dominated hegemonically, thus those institutions heavily mediate their purpose. It’s why I was able to blend two of my passions--sabermertrics and technology—to explain why this hegemonic control of data and analysis warps its original, intended purpose.
This final paper was called, “The ‘Moneyball for X’ Revolution: When Algorithms Are Viewed As Panaceas,” and I proceeded to blend our class readings with a variety of nefarious applications of Moneyball, one of them being the data triangulation of voters to their detriment, and another being “Moneyball for HeadStart,” which tried to circumvent the obvious problem of structural inequality with bootstrapping education via data.
One area in which the class and Moneyball overlapped heavily, where it became even more operative this past offseason, was a reading we did on “The Contexts of Control: Information, Power, and Truck-Driving Work,” by Karen E.C. Levy. It sounds unrelated, but in fact talked about how data analysis over trucking patterns led to more granular control over their workflow. I wrote:
“Operational control can sound awfully like Moneyball for the workplace, especially when it sounds like this: ‘The case… demonstrates that rationalization can be an incomplete explanation of the mechanisms through which information systems reconfigure organizational information flows… The systems provide ‘hard’ evidence… which forms a basis for challenging drivers’ accounts of local biophysical conditions’ (Levy 171). The use of data in baseball is at least defensible because baseball is almost exclusively measured in wins... [but] [w]hat are the goals in trucking? While safety is obviously a concern here, there is a certain lack of empathy towards a driver’s local concerns, and especially their rate of pay. Not only that, but truckers have an identity, one of independence, and this flies in the face of that mindset.”
We saw that in baseball just recently, but it’s important to remember that the little hobby we talk about here has real-world implications on the lives of people. In baseball, data and governance has allowed teams to circumvent labor laws for minor league players, and it has pushed teams into the position of under-cutting existing market rates because, well, that’s what the data indicates for older players.
I don’t expect people to have sympathy for these figures, but that’s not the point. Data analysis in baseball was originally intended to provide some sense of an “objective truth” for hobbyists who love the game, but instead it has been co-opted by teams and the league, and we’ve not only seen a brain drain that has hurt the public space and privatized that same data, but more importantly saw the world use those sabermetric skills to sharpen their knives in the “real world” of using the gig economy to micro-segment us, and using us as mini Amazon Mechanical Turks that fit like variables into an equation.
Luckily for us, the hobbyists are still here. We are not hegemonic. I came here from Pinstripe Alley not just as an upgraded position, but because I truly believe in the hobbyist and layperson’s ability to redefine how we think about baseball. This is not in service of anyone else’s needs but merely our own enjoyment and understanding, to make us more enriched and full from it. I’m excited for the next era of this already-storied site, and I hope you’ll join us.