Virtually all spectator sports are heavily saturated with statistics. Even before the advent of fantasy sports, which allowed fans to put statistics ahead of the outcomes of actual games, sports broadcasts were filled with numbers and data, as if all the solemn and ritualistic tabulation guaranteed the game’s facticity and the objectivity of its administrators. It’s as if the thoroughness of the accounting is its own form of athleticism on display. In team sports, the stats for each individual player are duly reported over and over again, apropos of nothing, foregrounding the tension between individual and team achievement, echoing a tension in viewers between vicarious identification with specific heroic players and assimilation into the mass of the team’s fans, losing oneself to the collective enthusiasm. The litany of statistics continually reassert individual accountability within a fundamentally collaborative context, prodding spectators to consider again and again how one might belong while also standing out and how to differentiate one’s personal performance from the overall performance of the organization. Star players are always at once “great teammates” and “clutch performers” who can “carry their team.” The way they are discussed often makes them symbols of a resolved contradiction; they stand as seeming proof that one can lift others by accruing glory for oneself. Everyone loves a winner.
Now that stadiums are equipped with technology to capture increasingly granular data about the games — for baseball, that includes the speed and location of every pitch and batted ball, the location of every player on every pitch and their speed to the ball in play, the bat speed of every swing, the “launch angle” of every ball put in play, and so on — broadcasts increasingly refer to this real-time information alongside the traditional counting stats and averages. “That left the bat at 107 miles per hour and traveled 417 feet.” These figures, often cited with a “how about that!” enthusiasm, are not only advertisements for the new surveillance capacity that is circumscribing the game, but they also evoke the fantasy of a completely datafied world where every act can be rendered “objectively” and be further analyzed. In that world, everyone’s individual contribution can be cleanly separated and perfectly attributed.
Usually one of the broadcasters — typically an ex-player — will have the role of performing skepticism of such statistics, preaching the importance of gut instincts and “feel for the game.” The thoroughness of player surveillance and statistical representation conjures an equal and opposite negative space for “intangibles,” the qualities that elude measurement but are nonetheless critically important to helping teams win. The players who exemplify “intangibles” take on an almost tragic, martyred air: They are always described as underappreciated “clubhouse presences” whose “impact on the field” transcends their individual numbers. On fantasy baseball sites, such players are sometimes described as “better baseball players than fantasy assets” and “not relevant for fantasy purposes.”
Unwilling to leave any intangibles unreified, however, baseball statisticians have devised a more holistic measure that attempts to capture a player’s full contribution to a team: WAR, for “wins above replacement.” It is supposed to measure how many more wins a team (not their actual team but a statistical construct) would have if they had this particular player instead of a league-average “replacement” placeholder player (not a real player but also a statistical construct). That is, WAR tries to construct a player’s value to a team with no concrete reference to any specific teammates. the possibility that there is special collaborative value created by the synthesis of a particular set of players — “team chemistry” — becomes the new intangible, seemingly impossible to optimize for. Meanwhile WAR insists on a kind of methodological individualism: Every win can be split apart and apportioned out to players acting alone, based on their discrete effort. It renders cooperation among teammates into a meaningless fiction, something of no measurable value to the team and thus impossible to take seriously in strategic planning or financial allocations. So perhaps it is more accurate to say that, much as neoliberalism insisted that there is no such thing as society, WAR insists there is no such thing as intangibles.
Yet statisticians have not settled on one way of calculating WAR; the figure is colored by the biases of each statistician and the degree to which they weight various baseball skills. So despite the apparent pretense of its making intangibles measurable, WAR remains fairly subjective. But it posits an ideal: that any positive contribution a player makes can be isolated and measured directly or inferred from other data sets. If your skills aren’t being captured in metrics, you need to demand better surveillance or better statisticians rather than insist on intangibles. In baseball, another set of statistics has emerged alongside the increased quantification of the fame: these use statistical regressions to calculate what a player’s “expected” performance outcomes are, what they should have been absent other anomalies presumably beyond their control. This approach purports to eliminate the element of chance, as well as the incompetence of teammates — one of these measures is “Fielding Independent Pitching,” how well someone pitched without considering balls put in play to other fielders. These sorts of statistics are becoming increasingly important to the salary arbitration and negotiation process.
WAR-type statistics track well with the increased emphasis on personal rather than organizational productivity that Melissa Gregg describes in Counterproductive, her study of time-management self-help trends. In that discourse, “productivity isolates and sanctifies the actions of individuals. It elevates an elite class of worker beyond the concerns of mundane others.” Productivity fetishism suits a society of free agents who must continually renegotiate the terms of their value, their viability, their irreplaceable contributions; it not only provides a rationale for employers to sort contract workers, but it provides those workers a way to frame their achievement in the absence of a stable association to a particular job or a particular set of co-workers. More and more of us are perpetually in the position of a baseball player facing an arbitration hearing. We cast everyone else as the replacement-level people that we are besting with our superior performances. To garner proof of that, we’re more likely to tolerate increasingly invasive forms of surveillance that can document our accomplishments and chart our personal growth on whatever metric is necessary, whichever seems to give us leverage over the competition, if not the employers.
WAR is a way to factor out the social conditions of achievement, to obviate the necessary framework of team goals and reframe life as a series of individualized opportunities. Everything becomes a referendum of me alone, focusing my sense of responsibility on me for me. As Gregg puts it, personal productivity protocols are elaborations of employer “initiatives aimed at erasing the practical and ideological means of experiencing labor as collective.” Individuating metrics and statistics are a means of concretizing that ideology as practice; the elaboration of ever granular metrics in spectator sports reinforces and glamorizes this technique.
I sometimes think of WAR as a potential metaphor for my life. I wonder if I am outperforming the replacement-level version of myself. This way of thinking is attractive to me precisely because the metaphor doesn’t quite track. I’m not sure what should count as wins, so I can find them everywhere, even or especially in petty acts of selfishness — a hallmark of time management, as it turns out, which in Gregg’s account is mostly about delegating the least respected work to other people: “Productivity operates on the premise that ‘taking time for oneself’ is a form of liberation.” Replacement-level me would have been on Twitter for a few hours today, so I’m like +2 for refusing to open TweetDeck. Replacement-level me is stuck behind that family of tourists waiting to get through the sidewalk bottleneck, but I’m +1 for slipping ahead of them and saving a few minutes.
It is easy to construe “replacement-level me” as simply the less productive version of myself and find the momentum to get me through the day in small triumphs of time saving and willpower. I can calculate the statistic for myself on an ongoing basis without reference not only to other people but to any actual accomplishments. Gregg argues that “the labor of time management is a recursive distraction that has postponed the need to identify a worthwhile basis for work as a source of spiritual fulfillment.” Instead, there is a sense that saving time is an end in itself. You don’t need any good ideas about what to spend it on. This unfolds the possibility of a fully gamified life, unfettered by actual games, rules, standings, actual victories — just statistical simulations of wins pegged to tautological efficiency measures that serve no perceptible purpose. As Gregg writes, “personal productivity is an epistemology without an ontology, a framework for knowing what to do in the absence of a guiding principle for doing it.” It’s a treadmill masquerading as a set of goals.
To make conditions of unrelenting antisocial competitiveness tolerable, people can convert the pursuit of productivity into a game (WAR against all) or, as Gregg emphasizes, into a form of self-care: “mindfulness.” Productivity becomes an optimal mode of taking care of one’s self in more ways than one. Focused asceticism, as is typified by the digital detoxes that allow us to get “real” things done, are good for the spirit as well as the accumulation of human capital. Finding ways to use data to differentiate myself from replacement-level me becomes a basic form of cruel optimism, a compensation that refreshes me for continued struggle toward ends that ultimately aren’t my own.
Since the pursuit of personal productivity requires the surrender of larger goals — the continual amassing of statistical data rather than actual wins — it depends on subjects being capable of emptying themselves of purpose, a kind of crypto-Buddhist pose of indifference to ends in favor of means. This strikes me like a production-side equivalent of the consumer-side subjectivity posited by algorithmic recommendation, where one’s desire for things is anticipated to the point of pre-emption, and the actual interior experience of desire becomes superfluous. You don’t have to actually want what is already being directed at you based on an analysis of your behavior. You can have it without the sin of wanting it.
Just as the consumer is subjected to ubiquitous surveillance to liberate them from decisions, so is the productivity-seeking worker seeking to leverage surveillance to learn what decisions can be delegated, with full delegation posited as an aspirational goal. “Delegating decision making to systems is increasingly portrayed as a rational response to the paralyzing experience of digitally mediated information overload,” Gregg notes, tracing this idea — now most familiar as a rationale for algorithmic filtering and ad targeting — to business self-help manuals for aspiring middle managers. Delegating decisions is represented as a form of executive privilege, a way of conserving decision-making resources (“executive function”) for situations perceived as more important. “Prioritization initiates a self-reflexive assessment of the worthiness of tasks relative to one’s sense of status,” Gregg writes.
Productivity protocols aim to highlight that aggrandizing moment of task sorting, but they also seek to automate it, as if delegating the decisions about delegation offer an exponential sense of mastery. Algorithmic decision-making draws from this same ideological complex, suggesting that you can seize an important sense of agency and prestige (the “convenience” of imposing on others) by delegating away as many consumer decisions (the “work” of shopping) as possible. The more decisions you can have made for you, the more powerful and in control you are supposed to feel: You have perfected your prioritization game. You are so important that no possible decision can be high-stakes enough to warrant your full attention. The logic of delegation is grounded in self-aggrandizement; automating it infuses it with passivity: together they construct a subject who is supposed to experience indifference as the ultimate luxury, a kind of elite pampering rather than an insidious form of control.
This echoes the rhetoric of mindfulness guru Jon Kabat-Zinn, which Gregg cites as influential in corporate circles. “Mindfulness is cultivated by assuming the stance of an impartial witness to your own experience,” Kabat-Zinn says. That is how I feel when I look at algorithmic feeds, or when I look at the health data my phone collects on me. Consuming one’s self through how algorithms understand it offers this seemingly impartial view, especially since you can’t know for sure how algorithms draw their conclusions. Since the algorithms are making the choices, I can feel like am “being” instead of “doing” — another mindfulness tenet. Algorithmic governance saves my whole mind for contemplating nothingness. The elimination of interiority pursued by surveillance-drive algorithmic systems is coupled with an aspirational ideal of purposelessness (i.e., productivity as an end in itself) configured as higher consciousness, mindless mindfulness.
“Productivity pivots on the belief that right actions will liberate an extraordinary class of worker from the concerns of this world,” Gregg writes. Algorithms promise the same thing. You are not replacement-level, even as your decisions are automatically replaced.