Quantified Play: Self-Tracking in Videogames

Abstract

This essay develops the concept of “quantified play” to describe and analyze the recent practice of self-tracking in the play of videogames. I argue that statistical, self-tracking utilities in videogames shape how gameplay “appears” and how it is experienced and valued by users. I proceed by situating contemporary self-tracking in games as part of a broader history of play as “quantified.” From there, drawing on interdisciplinary studies of self-tracking, as well as Bernard Stiegler’s postphenomenological analyses of technology, I characterize quantified play in three main ways. First, it is voluntary and occupied with self-knowledge. Second, it is used in mundane or everyday contexts. Third, it relates to the habitual faculty of users. The remainder of the essay illustrates the concept of quantified play through two examples of tracking hardware and software—showing how numerical or statistical apprehensions of player activity (and visualizations thereof) shape how we negotiate videogames.

Keywords

quantified play data analytics quantified self postphenomenology paratext

Recently, self-tracking technologies and techniques have found a foothold within gaming. Provided by both game developers and as third-party tools, these range from analytics software to track gameplay performance in competitive multiplayer games (such as DotaPlus or WarCraftLogs—which harvest, aggregate, visualize, and rank one’s performance in Dota 2 and World of Warcraft [WoW], respectively, according to various metrics), to hardware peripherals which monitor the more discreet bodily processes involved in play (such as Mionix’s Naos “Quantified Gaming” mouse, which tracks heart rate, or the SteelSeries Sentry gaze tracker). Despite having different functions and being used in different contexts, these technologies are united by the similar purpose of presenting in some sort of numerical or statistical format (or a visualization thereof) how players have affected or have been affected by a videogame. This is often presented by product developers as giving players the insights to reflect and improve upon their gameplay performance.

Due to the rapid and very recent growth of self-tracking in gaming, scholarly investigations of the subject are scarce. Important work has been done by Ash (2015) in his theorization of videogame interfaces. Ash considers the role of proprietary self-tracking platforms, arguing that the exteriorization of gameplay into the form of numbers, or imaged through heatmaps, shapes players’ experience (allowing players to contextualize their present play within a wider career of performance). Elsewhere, I have contended that platforms which harvest and aggregate player data in WoW have significant experiential implications for users—representing a changing up of the relations between somatic, cognitive, and technical aspects of gameplay (Egliston, 2017). In a different project, I have discussed the role of self-tracking within the context of e-sport—with competitors wearing devices to track perceptually inaccessible bodily processes, such as states of arousal, measured by monitoring electrodermal activity (Egliston, 2018). Others, like Medler (2011), look at statistic tracking as a site for accruing social capital. From the field of human–computer interaction (HCI), Kou and Gui (2018) survey various experiential outcomes of engaging with gameplay data (covering both self-surveillance and the surveillance of others).

Building upon this limited literature, this essay takes self-tracking in gaming as its focus. I present an analysis of software and hardware applications designed for the self-monitoring and self-quantification of videogame activity (tracking both “ingame” inputs, actions, and events, as well as monitoring biometric data). Examples are drawn from a 6-month exploratory study conducted from March to September 2018.

Consistent with existing accounts of self-tracking (see Lupton’s, 2017, recent “visceral data”), in developing an account of play as quantified, I am not simply interested in how these platforms quantify the qualitative phenomena of gameplay. Rather, I am interested in how quantification of gameplay produces particular qualities in play. Qualities here denote the phenomenal properties of subjective experience (Thompson, Lutz, & Cosmelli, 2005). Within the context of a videogame, qualities might be understood in terms of gameplay’s affect—understood as its capacity to “affect and be affected” (Massumi, 1987, p. xvii), to use Spinoza’s often-quoted definition. Crucially, to think of the “what” of phenomena, we must also think of the “how” of phenomenality (Thompson et al., 2005). Phenomenality has been approached variously by a range of scholars in phenomenology but might generally be understood as how things appear or are manifest to consciousness (see Thompson et al., 2005, p. 15). Across contemporary media theory (e.g., Hansen, 2015; Stiegler, 2011), phenomenality is widely taken as mediated and by that virtue yields access to particular experiences for humans (e.g., the use of technologies granting the ability to sense very small units of time; Ash, 2012b). To think about how a certain phenomenal quality is produced in play, I am interested in how self-tracking makes the embodied, temporal and procedural dimensions of play appear in a particular way, changing up gaming’s phenomenality through collecting, sorting, and presenting players’ gameplay information back to them in discrete numerical or visual form.

The central questions with which this essay is occupied, then, are how do commercially available self-tracking tools transform how gameplay “appears” and what are the subsequent effects on users’ experience of play? Moreover, mindful of the fact that people’s experiences of media differ greatly (self-tracking included, see Lupton, 2014a, 2016, p. 141), I ask “what kinds of practices does self-tracking produce and what does it inhibit?”

To attempt answering these guiding questions, I develop the concept of “quantified play”—offering what I believe to be the first sustained account of self-tracking in videogames. Through the concept of quantified play, I illustrate how self-tracking in gaming is generative of otherwise difficult to glean self-knowledge, forming a central and active component of the habitual and perceptual faculty of everyday videogame players.

Unlike previous work on games and self-tracking, my aim is to contextualize self-tracking within videogame play as part of a wider self-tracking movement, riffing off the popular and broader “quantified self.”¹ The quantified self-“movement,” as cofounder Gary Wolf (2009) puts it, is about “self-knowledge through numbers” (n.p.). In her sociological account, Lupton (2016) takes the quantified self, and self-tracking, to be about the voluntary “monitoring, measuring and recording elements of one’s body and life as a form of self-improvement or self-reflection” (p. 8). While scholars have acknowledged long-standing tendencies for people to pursue self-knowledge through external measurement (Crawford et al., 2015), recent self-tracking happens largely through (often wearable) digital sensors. Research to date has covered practices ranging from cycling (Lupton, Pink, LaBond, & Sumartojo, 2018), to workplace efficiency (Hand & Gorea, 2018), to sex (Lupton, 2014a), touching on experiential outcomes of tracking on the performance of these activities. Other work has provided broader commentary on the impacts of self-tracking technologies on the spatiotemporal texture of our everyday lives (see Pink & Fors, 2017). Indeed, the lives that self-tracking report on include our everyday mediated lives. On self-tracking mediated life, Lupton (2016) points out tools for measuring the popularity of one’s posts on Twitter or Facebook (pp. 22–23), as well as those for measuring one’s own productivity (by tracking website usage; p. 25).

By situating self-tracking in games as part of a broader range of mediated practices to do with data and the self, I hope to contribute to wider conversations happening around the role of data in negotiating our everyday lives. The role of data in transforming our everyday habits and routines is a key concern for scholars of media and technology. Through this investigation of quantified play, I contribute perspectives about videogames—an extremely popular mediated activity—to a much larger, ongoing conversation.

At the same time, I provide insight into the complexities of play and how it is produced within rapidly changing material contexts. Recognizing how self-tracking operates in gaming provides a new perspective into how gameplay “happens” around what Consalvo (2007) would call “paratexts” (supplementary, “out of game” materials). As Consalvo illustrates in her work, the technologies between and beyond videogames themselves significantly shape how we negotiate, understand, and value videogames and the practice of play. Extending and updating this perspective, I argue that quantified play and self-tracking are a rich and contemporary site for thinking about how assemblages of bodies and videogame technologies orient human experience and activity—a point that scholars like Crogan and Kennedy (2009), writing in this journal, draw attention to in interrogating the technologies ‘between' games and culture.

This essay proceeds by outlining my methodology. I then situate quantified play around other practices within games and play (digital and otherwise). From there, drawing on interdisciplinary studies of self-tracking, as well as Stiegler’s postphenomenological philosophy and analyses of technology, I develop the concept of quantified play. I do so by characterizing it as (i) voluntary and used to provide self-knowledge, (ii) mundane in nature, and (iii) connected to the habitual faculty of users. The rest of the essay illustrates the concept of quantified play through two examples—examining various implications for player experience.

Method

This essay draws on empirical material gathered from March to September 2018. Study material is comprised of reflections on my own experience as a self-tracker in the game Dota 2 (2013),² as well as online content pertaining to self-tracking tools, DotaPlus and the SteelSeries sentry. These included online discussions (from the popular forum Reddit) and YouTube and Twitch videos. Of course, not all self-trackers use Reddit, YouTube, and Twitch. In this way, the data will provide a perspective on how a sample of users on extremely popular gaming websites experience and use self-tracking. In total, I analyzed 18 discussion threads and 11 videos. I noted references to self-tracking made in discussion threads and videos. These references formed my data set. This was coded and analyzed using grounded theory methodology (following open and axial coding; see Glaser & Strauss, 2017). This was in line with the exploratory nature of this research. Despite using an inductive approach, sensitizing concepts (Bowen, 2006)—derived from the literature on self-tracking, as well as broader postphenomenological accounts of technology—guided my analysis, giving me a sense of direction in unpacking the data. The discussion of the data about each self-tracking platform is organized according to two principal axial codes—player experience as positively impacted or diminished and devalued through new data interfaces.

Antecedents: The Numbers Game of Play

While I showed earlier that there is a limited body of prior work about statistical or numerical tracking in games and the implications for the experience of play, it is possible to look further than studies of self-tracking platforms to situate play as “quantified.” Practices of number have long influenced the phenomenality and phenomenal quality of play and provide a good reference point for understanding contemporary self-tracking.

Locating videogames as part of a broader cultural history, Crogan (2016) writes that 19th-century war gaming—and games such as Kriegspiel—works as numerically and mathematically determined forms of play (from dice throws to the formulas that determine the movement of pieces across the game’s board; see Crogan, 2016, p. 666). In terms of videogames, code is a crucial (at least partially) numerical dimension of play. Writing on early, interactive 3-D displays, Crogan (2011) notes the “simulation of analog experiences such as touch and hearing” were enabled “via the mathematically based symbolic languages of computer programming” (p. 44). From a different perspective, in his “flat ontology” of computing, Bogost (2013) takes videogames as irreducible assemblages, in part “8 kilobytes of 6402 opcodes and operands” (p. 17).

More directly aligned with the present work, videogames also have a history of counting and presenting player performance in numerical form. Scoring systems are an obvious example, providing players a numerical readout of gameplay and giving a view of one’s progress or performance in a game. As Hunicke, LeBlanc, and Zubek (2004) put it, scoring systems can significantly shape players’ experience of gameplay—such as their enjoyment and motivation to keep playing. Differently, Ash (2015, pp. 96–98) examines how scoring systems can be used to create a sense of action and vitality in play, achieved by rapidly filling the screen with game information. The popular feature of achievement points can be seen as a kind of quantified play—although in the capacity of what Carter, Gibbs, and Harrop (2012) call a metagame, a scoring system situated “outside” the game, as additional content. Achievement points refer to a numerical score awarded to players for accomplishing a particular feat in a videogame. Previous work has looked at achievements as a site for accruing social capital (see Carter, Gibbs, & Harrop, 2012, on achievements to show off skill), as well as in terms of provoking affective qualities in play (Ash, 2012a).

The peripheral or paratextual materials and their associated practices, surrounding videogames, are also an important site for thinking about quantified play. There is a body of literature around the practice of TheoryCraft which is particularly relevant. TheoryCraft can be understood as the use of mathematical or quasi-statistical tools to optimize one’s playstyle (see Paul, 2011). In WoW, TheoryCrafters use third-party software to optimize playstyle, which provides various numerical rankings and weightings of ingame attributes which players can construct their characters and playstyles around. In a similar vein, user interface (UI) modifications in WoW break down, and often rank performance, according to a particular score or metric (allowing users to track their own performance and surveil the performance of others; see, e.g., Taylor, 2006). While add-ons are distinct from TheoryCraft, as Wenz (2013) notes, they are very much related—enabling an immediate form of reflection and optimization of playstyle (p. 188). While the connection between TheoryCraft, UI modifications, and quantified play is a clear one, I perform some work in the section on “mundanity” in distinguishing it from the account of quantified play developed in this essay.

It is evident that numbers-related practices and processes have factored heavily into games, in both their play and design, for some time. As these examples have shown, numbers produce various phenomenalities and qualities in play. As demonstrated over the remainder of this essay, these are qualities that are subject to further, continued change through a recent suite of gaming-specific self-tracking technologies.

Theorizing Quantified Play

In this section, I develop the concept of quantified play to think about videogame analytics platforms and the way they transform play experience. I am influenced by the previously surveyed accounts of numbers in play (and their relation to the quality of play), existing work on self-tracking, as well as broader postphenomenological theorizations of technohuman relations. The account of quantified play I develop has three main characteristics. First, it is a voluntary form of self-surveillance. Second, it is (increasingly) mundane in character—a part of “everyday” routines, enabled by commercially available technologies. Third, it is connected to the habitual and perceptual capacities of the user. This theoretical definition and associated concepts sensitized me to lines of inquiry in analyzing the study material.

Voluntary Enrollment and Self-Knowledge

Consistent with existing scholarly definitions of self-tracking (Lupton, 2014b, 2016), I define quantified play to be about the voluntary enrollment of the user in regimes of self-reflective play, enabled by the availability of data to do with their own ludic activities. Platforms—including those discussed in the following section—–require the user to opt in, participating in a mode of self-surveillance. While questions about other forms of surveillance (e.g., social or algorithmic surveillance) are indeed relevant to discussions of self-tracking (and have been taken up elsewhere³), the scope of the present work is limited to self-surveillance.

Also consistent with understandings of self-tracking, quantified play practices (as we see in the later examples) are usually done with outcomes of self-reflection or self-improvement in mind. As Lupton (2018) writes, in developing the concept of “data assemblages” to describe how data operate “with and through bodies” (p. 9) and its outcomes for user sense and knowledge, the use of self-tracking technologies is a process by which embodied activities and rhythms are turned into digitally produced numbers or visualizations (as to be made “readable;” see Lupton, 2016, p. 42). In turn, as Lupton (2016) puts it, data assemblages are “a way of mastering the uncertainties, inaccuracies and vagaries of human embodiment” (p. 41; for further accounts of self-knowledge and self-tracking, see Beer, 2016; Jethani, 2015).

In short, the practice of transposing one’s own vital processes into the form of numerical/statistical data or visualizations thereof—with the goal of generating further knowledge of the self and enhancing capacities for action—is one that characterizes existing accounts of self-tracking. As research on self-tracking points out, the phenomenality of everyday life is increasingly fixed through assemblages of bodies, tracking technologies, and data. I extend these claims to think about quantified play practices—considering the new phenomenalities that emerge from the production of gameplay data, and the phenomenal qualities that emerge from reflecting on “data-fied” readouts of our own gameplay activity. This is sketched out in further detail through examples in this essay’s final two sections.

Mundane

Quantified play and self-tracking in games are taken to be mundane or commonplace, facilitated by the widespread availability of requisite software and hardware. These consumer-grade products and services generally require minimal technical expertise. When sold, such software and hardware are relatively affordable consumer goods and services—allowing them to be adopted and integrated into users’ everyday routines. Biometric devices by SteelSeries and Mionix (previously limited to clinical settings) are marketed as tools which provide everyday players improved and richer gameplay performance. Self-tracking tools are often included as a basic feature of many games and game platforms. For example, games like Dota 2, Battlefield 1, or Overwatch allow users to access data to do with their entire play “careers” (such as win/loss or kill/death ratios). Online platforms like Steam quantify the number of games one owns, as well as how long they have been played for. The recent “screen time” feature for the Apple iPhone tracks and presents (visually and numerically) app use (including games)—couched in rhetoric of assisting with time management (Apple Newsroom, 2018). Put in summary terms, data about the self are key in the routines and rhythms of everyday gameplay.

Indeed, the relationship between enacting everyday routines and technology has been a key concern for scholars. For example, 20th-century European philosophy has attended to the question of technology and human experience—focusing on how technologies recede into our everyday lives, a taken-for-granted texturing of space and time (see, e.g., Heidegger, 1962; Merleau-Ponty, 2002; Stiegler, 1998). More contemporary research includes, inter alia, the study of digital data (Beer, 2016; Pink, Sumartojo, Lupton, & La Bond, 2017; Thrift, 2004). Here, scholars have explored how data embed itself into the rhythms of everyday life, with implications for ways of doing and being.

Conceiving of data as entangled with the everyday practices of users frames my understanding of quantified play and marks a point of distinction between quantified play and existing and well-documented, “quantified” gaming practices like TheoryCraft. TheoryCraft, explained earlier, is often a practice associated with “hardcore” communities. Of course, while still of scholarly and cultural importance, such hardcore communities only really represent a limited group of players with the economic and social mobility (cf. Paul, 2018, p. 54) to cultivate the requisite gaming and paratextual literacies to participate in these data-driven practices. While quantified play and TheoryCraft are similar in that numerical data intervene in play habits or routines, the distinction as I see it lies in the fact that quantified play is occupied with more mundane forms and uses of statistical or numerical techniques and technologies to track the practices and processes involved in gameplay, something relevant to a larger range of individuals. Given my focus on consumer technologies available to, and often used by everyday players, an examination of quantified play represents a look at transformations happening within the widespread and familiar material contexts in which games are experienced and perceived.

Habitual

The technologies and practices associated with self-tracking and personal informatics are, as discussed in the previous section, often routinized and taken for granted in nature—used in an everyday capacity by people to gain knowledge about (and potentially effect change in) their everyday lives. The pervasiveness of digital data, including data traces of our own activities, produces new qualities and ways of experiencing the world (see Beer, 2016; Pink et al., 2017; Thrift, 2004), doing so by rendering the space and time of various practices and processes discrete, altering the phenomenality of everyday life. As Lupton, Pink, LaBond, and Sumartojo (2018) write, the generation and interpretation of data by self-trackers, and its subsequent incorporation into one’s own mundane routines, can lead to the cultivation of certain habits. Focusing on this relationship between habit and data, this section theorizes the way quantified play—as a set of mundane technologies and techniques of the self—shapes user habit.

A useful definition of habit, for the purposes of this essay, comes from Grosz (2013)—understood as the state of an organism—its general or permanent way of being, and changes in that state of being. Another important characteristic of habit is its materiality (as underlined by Bennett, Dodsworth, Noble, Poovey, & Watkins, 2013, and also in a wider literature spanning phenomenology and postphenomenology; see Merleau-Ponty, 2002; Stiegler, 1998). There is a broad sentiment across these theorists’ work that habit can be understood as a dynamic state of doing and being that emerges from assemblages of humans and nonhumans (a point we see played out in existing writing on self-tracking, such as Lupton et al.’s, 2018, previously cited study of cycling, where habits emerge from assemblages of users, their tracking devices, and their data).

Drawing from postphenomenology, in thinking about the relationship between “data,” and gameplay habits, there is rich conceptual resource to be found in the work of philosopher of technology Bernard Stiegler. His concept of grammatization is particularly useful in framing questions about “data-fied” life. Grammatization is crucial to Stiegler’s (1998) broader theorization of human technicity—that is, the condition by which humans exist in composed complicity with nonhuman equipment, tools, or technics. Technics are “retentional devices” (or forms of what Stiegler calls “tertiary retention”) that exteriorize or contain traces of memory, materializing experience in a way that it can be passed down or adopted (e.g., the technical transmission of intergenerational experience, which enables the formation of culture, forms of somatic activity, and so on). In this way, Stiegler sees technics as fundamental in human beings’ capacity to recall the past but also to develop forward-leaning expectations, and potentialize action in future.

Grammatization has a central role in Stiegler’s technicity. The term derives from Derrida’s philosophical project of deconstruction and work on “gramme” (a Greek word for the written mark). As with Derrida, whose project of deconstruction sought to confront the historic opposition of thought and writing (Bradley, 2011, p. 18), for Stiegler (2010b), the gramme refers to “discrete marks, traces…that forms the hypomnesic milieu for anamnesis” (p. 66). What this means is that gramme condition consciousness—playing a central role in the always mediated ways of remembering the past and anticipating the future that constitute human technicity (see Stiegler, 2011, pp. 8–34). The process of “grammatization” can then be defined as a “technical tendency…consisting in the duplication and discretization of mental experiences (that is, temporal experiences) in the form of hypomnesic tertiary retentions” (Stiegler, 2015, p. 29), or technics. Stiegler’s work—as a history of technics—understands grammatization as a crucial dimension of human existence, whereby forms of knowledge are discretized, and conditions new ways of doing and being when adopted. This could range from the grammatization process of writing (i.e., the discretization of speech to symbols) to those characteristic of electronic and digital technologies (e.g., the grammatization of command prompts in graphical UI in modern computers).

In summary, for Stiegler, the basis for human activity and ways of doing and being is grammatization. With clear implications for thinking about the habitual faculty of humans, I adopt this concept to read the phenomena of quantified play, and the interface between self-tracking platforms, how gameplay appears, and how it is experienced by players.

Taken together, in this section, I have theorized quantified play as voluntary, and generative of epistemologies of self, mundane in nature, and connected with the habitual and perceptual faculty of the user. In the following two sections, examples are presented which illustrate the concept of quantified play. Each example presents a look at a different form of technology that tracks a specific aspect of gameplay, showing how they represent sites of transformation in the phenomenality and phenomenal quality of gameplay.

Software Platforms and Monitoring Gameplay: DotaPlus

DotaPlus is a paid subscription-based form of self-tracking software for the game Dota 2—provided and developed by the game’s designer, Valve Corporation. While Valve have not publicly released figures on the amount of DotaPlus subscriptions, in my playtime throughout the research period, I observed the use of DotaPlus to be a popular mode of experiencing the game, with at least one player in every match using a DotaPlus subscription (with subscription status being visible to other players in the match).⁴

Through presenting the complex embodied and technical fluxes that comprise gameplay in both visual and numerical formats, DotaPlus grammatizes gameplay. There are numerous features that achieve this, ranging from statistical overlays in each ongoing match, to postgame analytics—exteriorizations that allow for self-reflection of one’s performance over time (e.g., over the entire period spent playing Dota 2), and, purportedly (according to the game’s advertising), enabling players to reshape cognitive and somatic styles involved in play, developing empowering, capacity-building forms of habit.

One of the game’s features that best illustrates the connection between habit and visual and numerical grammatizations of gameplay is the ingame “head up display” (HUD). The HUD (see Figure 1) is a real time monitor of player activity. Various distinct aspects of gameplay are monitored, which are presented as scores in the top left corner of the game’s screen. These are last hits (LH; the number of computer-controlled, enemy characters killed), denies (DN; the number of computer-controlled, allied characters killed), and net worth (NW; the total accumulated resources of one’s character). In each game, DotaPlus users are scored on their performance and given a “goal” to work toward. This goal is determined by the player’s skill level (a lower skilled player would have a lower LH goal than a player in the game's top skill bracket) and the time of the match (a LH/DN/NW goal at 5 minutes will be much lower than a goal at 50 minutes).⁵

Figure 1.

DotaPlus real-time stats head up display.

The player’s score goal is accompanied with a small, but striking, visual indicator of performance. This takes the form of either an upward facing green arrow (which displays if the player is doing better than their goal) or a downward facing red arrow (if the player has not met their target). While simple, these visual indicators are affectively charged and can condition particular behaviors (consistent with HCI-based work on the use of colors in interface design; see Bartram, Patra, & Stone, 2017).

Seeing a red marker, when I have an LH score of 14 at 9 min, I might think to myself “I’m not doing so well this game” (which could encourage me to try harder). In some instances, however, it has had the inverse effect—amplifying negative affects if I’m having a particularly arduous game and making it difficult to perform well given the likely outcome of losing the game. Indeed, such negative, disempowering consequences are not unfamiliar in studies of self-tracking (see Lupton, 2014b). In short, these striking color-based indicators work alongside the numerical readout of gameplay, allowing players to retrieve a “performance summary” at a glance—which can produce either positive or negative qualities in play.

The ability to quickly retrieve information about one’s performance, and its associated positive outcomes on gameplay, was a principal theme in the user data. We see this, for instance, in discussion of other parts of the DotaPlus interface, for example, the DotaPlus “Relic” system. Relics are virtual items only available to DotaPlus users (purchasable with a DotaPlus-exclusive ingame currency), which provide a way of tracking how many times a player has performed a particular action ingame (for instance, accruing kills on opponents in a specific way). This is displayed in glowing text over the top of the players’ ingame character. This works as an exteriorization of gameplay performance that can be retrieved in real time. Interestingly, user responses to relics were often not about statistics as a means of skill development. Rather, they were about the positive experience of exteriorizing past gameplay experiences for recall later. Among the positive user responses to relics and tracking, one user notes “the records and statistics are reminders for all the amazing memories I have achieved in the past” (“Dota Plus,” 2018). As this user sees it, relics are less about using statistics to improve and more about playing around data interfaces to heighten their affective or emotional enjoyment of the game (cf. accounts of users’ emotional and affective connections with achievement points, Ash, 2012a, and self-tracking data, Lupton, 2016, p. 73).

Consistent with my own experiences covered prior, we can also note users discussing positive outcomes relating to self-improvement and skill development. This is seen in large part around reflections on the “death summary” part of the interface (see Figure 2). Here, users touched mostly on its ability to help them improve their play. The death summary provides a granular breakdown of how their ingame character died, accessible any time one’s character is killed. In one Twitch broadcast, streamer AdmiralBulldog dies. “What happened? I don’t know what happened,” he says. Opening up the death summary, “I guess physical damage. Yep, I got stunned here” (AdmiralBulldog, 2018). For YouTube user SUNSfan, the death summary,

breaks it down a little bit more, you can see how much physical and magical damage you took. Sometimes you’re not paying attention to every little thing in the game, and you’re like “I’m gonna buy a BKB” then you realise “Oh, I’m mostly dying to physical damage, maybe I don’t need BKB this game.” (DotaCinema, 2018)

Figure 2.

DotaPlus death summary.

Play’s grammatization through data interfaces also attracted negative responses from players. The general theme here is that play was negatively affected, devaluing gameplay by making calculable much of its contingency and a perceived diminishing of “skill” as accrued through continued performance. Such a critique of play’s “quantification” resonates, at least partially, with Stiegler’s critical writing on grammatization. While Stiegler (2010a) argues that grammatization is a necessary and formative part of human development, he is also deeply critical of the socially and culturally stifling impacts of modern technics. Grammatizing human activities through forms of industrial technics render them repeatable, synchronized, and reproducible. While not positing a deterministic relation, Stiegler paints a picture where the quality of human individuation or becoming (which is reliant on an interplay between individual and collective life and technics) is greatly diminished. Modern grammatization, as Stiegler (2010a) sees it, is injurious to, and devaluing of the kinds of social, somatic and cognitive, processses involved in an activity or experience that are cultivated over time, through techniques, technologies, and traditions of practice (pp. 37–44).

A key “negative” aspect of DotaPlus, appearing in many Reddit posts, was of the platform as a “pay to win” feature—meaning that an advantage was perceived to be given for an opt in fee, unevening the playing field of Dota 2. As one user suggests, DotaPlus offers an unfair advantage over those without a subscription: “if you can in any way spend money in the game to help you perform better (over others who don’t), it’s some level of pay to win” (Klagaren, 2018). Another user suggests that the HUD’s display of net worth (a number that was previously hidden and only accessible with a particular mathematical formula) in real time is advantageous and devalues the importance of developing a fuller range of cognitive skills (KatsukiDreams, 2018). To return to Stiegler, the grammatized abstraction of gameplay as a mode of short-circuiting skill might be seen as part of a broader tendency of commercial technics to short-circuit typically long circuits of individuation as to program economically desirable regimes of consumerist passivity⁶—aspects of modern technoculture of which he is deeply critical, and about which he has dire prognostications. DotaPlus offers an alluring promise of improvement, beyond the traditional means of practicing and cultivating somatic skills, participating in the game’s community, metagames, and so on, programming our continued involvement in Dota in a way where the autonomy of individual players, and the playerbase as a collective, is diminished.

Related, but distinct from this, was discussion of DotaPlus as “pay to lose”—with users suggesting that statistics are superfluous, unhelpful information. One user is critical of the ability of players to use the HUD’s statistical data in any meaningful way—suggesting that statistics can throttle one’s cognitive bandwidth. As they put it, “All the flashiness is honestly very distracting to me. There is already enough going on in-game for me to keep up with” (Drunk_soldier, 2018). As another user writes, the data overlay are likely to result in “the Dota equivalent of WoW players having DPS meters and dying in fire while trying to be on the top” (Nelsonbestcateu, 2018). In this way, the statistical interface has not (for these players) receded into the background of their everyday play; instead, it is something that is awkwardly negotiated alongside the game. Another player points out the ineffectual nature of statistics in accurately capturing a Dota 2 match. Writing on the HUD feature:

Having a high KDA, net worth and last hits are not what being a good Dota player is about…For example I just went 9/18/10 in a Winter Wyvern game which was considered “below average,” but I made so many good plays that I know I performed well above average overall. (Gr4b, 2018)

In a way that is consistent with previous writing on statistical data and human activity (Lupton, 2016; see also Paul, 2011, on games), what this player underlines is how statistics perpetuate normative ideas of “good performance.” Relatedly, I noted the “normative” effects of data played out in Reddit discussions where users point out the practice of “cheesing” the HUD system—trying to accrue the highest score possible, while not strictly trying to win the match. Consistent with what Gregg (2013) terms the “labor” of data, players here try to control how they appear through the data (a similar phenomenon has been observed in WoW, with players trying to “inflate” their damage dealt as measured by UI add-ons; see Kelly, Watts, & Payne, 2016). In this way, players have fully internalized the logic of DotaPlus—grammatizations rigidly ordering the various ways that users negotiate the game.

In short, these users point out some of the negative impacts of self-tracking and the ways they can diminish our capacities for action. As noted prior, this chimes with existing accounts of self-tracking that contest celebratory perspectives on data, emphasizing how “self-betterment” is not always an outcome (Lupton, 2014b). But, keeping with Stiegler’s critical philosophy of technology, these negative user accounts signal toward a broader issue of play being diminished—an activity where users would individuate based on the adoption of various techniques or practices made possible through the tertiary retentions of the game. More generally, this logic is consistent with a broader tendency of 21st-century capitalism—where technics are used to solicit individuals by short-circuiting and grammatizing experience.

Taken together, my examples from DotaPlus show the routine practice of playing Dota 2 becomes a process of negotiating the game where perception, somatic actions, and cognition are conditioned by and emerge through data interfaces—which grammatize and feed back to us our gameplay performance. In this way, aspects of ludic activity—such as learning and remembering how to play—are variously transformed.

Biometric Data: SteelSeries Sentry

Like the much broader suite of self-tracking technologies that feature prominently in many of our everyday lives, self-tracking technologies in videogames utilize a range of different sensors to monitor and present back to the user the (often quite discreet) somatic processes involved in gameplay. To note, while previous work has looked at the generation of biometric gaming data as a spectacle (within the context of e-sporting events, see Egliston, 2018), there is a scarcity of work investigating the experiential implications of everyday players monitoring their biometric data while gaming.

In exploring biometrics as part of quantified play, a fruitful example is gaze-tracking technology. While haptic routines are often privileged as a site of thinking about games and the body, optical movements are a very key part of how players negotiate videogames. Much has been made of eye-tracking technology and its ability to provide difficult-to-capture insights into gameplay within the context of HCI research on games (for a good overview of eye tracking and interaction in games, see Velloso & Carter, 2016). While gaze tracking has, up until recently, been used in largely clinical and experimental contexts, there are a range of consumer grade eye trackers (e.g., SteelSeries Sentry, Tobii EyeX), as well as laptops and monitors with inbuilt eye-tracking functionality (e.g., the MSI GT72S laptop or the Acer Predator XB241H “gaming monitor”) becoming widely available.

In investigating user experiences and perceptions of gaming through biometric technologies, I focused on one specific piece of hardware as example—the SteelSeries Sentry Eye Tracker (a gaming specific, consumer-level eye tracker) and its replay analysis feature (which can be used in competitive games like StarCraft II or Dota 2). Users of the SteelSeries Sentry, after finishing playing, can access the Sentry Replay analyzer, which provides a number of scores—based on numerous metrics for performance at the game, relating to gaze—which generally give a measure of the player’s spatial awareness or ability to react to events on the screen. The postgame gaze analysis interface also visualizes performance over the entire game (based on these metrics)—allowing players to reflect on their performance and identify precisely where and when they have performed well and where they have not. These various forms of grammatization have implications for how players negotiate the game. As I have argued elsewhere, looking at biometric imaging techniques for broadcast at e-sports events, platforms that can visualize discreet bodily processes involved in gaming can create the capacity for spatiotemporal sense and awareness (Egliston, 2018, pp. 170–172), enabling the viewing body to become affected in new ways (or what I describe as, after Latour, 2004, becoming more “articulate”). While focused on a different activity (watching e-sports), I suggest much the same is true here—biometrics are providing the means for articulation in everyday play through commercially available technologies.

Looking at biometrics as “positive,” as one video review notes, the SteelSeries Sentry allows for reflection on past play—interrogating it through various gaze-based metrics (Tobii Gaming, 2015). As studies of eye interaction in games would suggest, gaze and fixation is understood to be an indicator of user cognition and attention (Vellosso & Carter, 2016)—with the Sentry replay analyzer ostensibly able to help players recall “what you were thinking at the time” (Tobii Gaming, 2015). Other video content points out its capacity-building potential, speaking particularly to its use within the context of training for e-sport; the video shows how self-tracking is used by players in leading teams for training purposes.⁷

The idea that gaming biometrics can provide insights into otherwise perceptually inaccessible processes, allowing for self-reflection, is something we can see through broadcasts of the application in use. We see this in video material showing the application’s use over the course of an hour-long play and reflection session by Dutch Dota 2 commentator and livestreamer Jorien “Sheever” van der Heijden (see SheeverGaming, 2015).⁸ During the broadcast, she first plays a match—loading up the replay analyzer following the game’s completion. She looks first at the metric for “minimap look frequency.” The minimap is a small map of the game environment which shows one’s teammates, sometimes enemies, and other key aspects of gameplay. The frequency with which players look at the minimap can be taken as one marker for spatial awareness (high-level players, for instance, will have very high minimap look frequency). As an intermediate player, Sheever’s performance is characterized by high peaks and low troughs—meaning that there her performance is inconsistent across the game. Reflecting on this, she says “these spikes I shouldn’t have those, I should look at the minimap more often than this.” She then looks at her performance for “looking at enemy items”—which was done irregularly in this match. “I didn’t look at enemy items for like, 25 minutes [laughing]. This is really bad. So, I looked at enemy items twice this game.” Sheever’s reflection on “fixations per minute” also provides some useful insights into how the platform might work to develop more productive habits. As she notes during the video, “I’m thinking if I get this lower then that’s actually really good, because then I’m like faster in my movements with my eyes, faster in processing information, and faster in making decisions therefore.”

Throughout the video, we see the data not only give Sheever a sense of where she has performed (and underperformed), it also allows her to pose new questions about her performance and generate hypotheses based on logging this activity. As Sheever remarks, looking across the data visualizations provided: “I looked at my own items a lot which is interesting, because why would I do that?” Meditating further on the data, she surmises that “it’s kind of in line with when I look at my own items, because I look at my gold, buy something, and look at my items.”

In a more negative light, Sheever’s video also raises questions about the veracity of data. For example, during the video, she does not find utility in all of the different gaze metrics (“why would I need to look at [that]”). In a way that is consistent with discussions observed on Reddit around heatmapping gaze data (LordVolcanus, 2017), Sheever also points out where data do not properly take account of what was happening in the game (and signals how these visualizations might be misinterpreted). She shows, for instance, a “negative” visualization of her play—which was from a part of the game where the player is not given control of the game (and therefore cannot score highly). Sheever’s encounter with the gaze data shows, that while data provide a particular way of seeing, it is partial in its perspective and commercial in motivation (cf. Gregg, 2013, p. 43). In this case, the motivation might be to create a feeling of reliance on tracking technologies to play well and improve. In such a way, to return to Stiegler, who would perhaps be quite critical of the data analytics industry, data are a technique to enjoin particular ways of doing, seeing, and being through the selective capture of an “event” (here, gameplay)—in doing so, working to create subjects amenable to capitalism and the interests of the media industries (cf. Stiegler, 2011, 2015).

As this brief glimpse into the use of biometrics in gaming shows, the use of applications like the SteelSeries Sentry represents a mundane way of capturing—by spatializing and breaking into discrete, retrievable forms—some of the processes involved in gameplay which potentially fall outside the thresholds of player awareness. Through the grammatization of the bodily aspects of gameplay, the Sentry’s biometric data tracking materializes the spatiotemporal dynamics of gameplay, creating conditions for future ways of doing and being.

Conclusion

Writing a decade ago in this journal, Crogan and Kennedy (2009) suggest game scholars take seriously the technologies “between” games and culture. In doing this, they enjoin scholars to address the various experiential changes emerging from the evolution of games as contemporary technics. The proliferation of technologies to do with data and self-tracking shows that this claim is just as relevant today. Yet, despite the prevalence of commercially available self-tracking technologies in gaming, this area remains underdiscussed in game studies. Addressing this gap, this essay has offered numerous provocations for game studies going forward. Foremost, how does gameplay “appear” through new kinds of data interfaces? How might we think about play’s phenomenal quality in this moment? Moreover, what kinds of practices does self-tracking produce or inhibit?

This essay offers some modest responses. To do this, drawing from recent scholarship on self-tracking and writing on games, technology, and habit, I have developed the novel concept of quantified play to think about new assemblages of paratextual software and hardware that grammatize (through statistics, visualizations) various human–technology relations involved in the performance of gameplay.

Alongside the theoretical argument developed in this essay are a series of examples which demonstrate how gameplay is inscribed in the form of numbers, data, metrics, and visualizations thereof, advancing a view that ways of knowing and doing, and the embodied and sensory routines involved in gameplay, are conditioned through the numerical and visual format of data—a recent advancement in gaming. As shown through my exploration of DotaPlus and the SteelSeries Sentry, much like habit in a broader sense, the implications for our ways of doing and being in videogames around statistical platforms can both be positive (e.g., empowering or capacity building or enriching the affective pleasures of play) and negative (disempowering, and moreover, potentially injurious to the very activity of play).

To be sure, the provocations in this essay about the role of data and its imaging in videogames are by no means the first. That said, the scale and sophistication of contemporary, commercially available self-tracking technologies represent an intensification of prior practices of number in gaming—creating new orientations of human phenomenality and presenting new, critical questions about data and the qualitative experience of play. Given that data tracking is becoming ever more so central an aspect of everyday videogaming (and a commercial interest of the paratextual industries), the concept of quantified play is expected to be pertinent to scholars studying games in the immediate future.

Footnotes

Acknowledgments

Many thanks to Marcus Carter for comments on a draft of this article and for proposing the term ‘quantified play’. My thanks also to the two anonymous reviewers, whose generous feedback helped me strengthen the essay.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ben Egliston

Notes

Author Biography

Ben Egliston researches and teaches about new media and digital culture, with a specific focus on games. He currently works as a sessional lecturer at the University of Sydney in the Department of Media and Communications, where he recently completed his doctorate. His current research is focused on user experience in games, taking this as a postphenomenological concern. He has published on topics such as e-sports, data analytics, and livestreaming for a range of academic and general interest publications.

References

AdmiralBulldog. (2018). AdmiralBulldog tries Dota Plus for the first time [YouTube video]. Retrieved October 4, 2018, from https://www.youtube.com/watch?v=d22QWUlCOew

Apple Newsroom. (2018, June 5). iOS 12 introduces new features to reduce interruptions and manage screen time. Retrieved from https://www.apple.com/au/newsroom/2018/06/ios-12-introduces-new-features-to-reduce-interruptions-and-manage-screen-time/ Ash

(2012a). Attention, videogames and the retentional economies of affective amplification. Theory, Culture & Society, 29, 3–26.

Ash

(2012b). Technology, technicity, and emerging practices of temporal sensitivity in videogames. Environment and Planning A, 44, 187–203.

Ash

(2015). The interface envelope: Gaming, technology, power. New York, NY: Bloomsbury.

Bartram

Patra

Stone

(2017). Affective color in visualization. In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 1364–1374). New York, NY: ACM Press.

Beer

(2015). Productive measures: Culture and measurement in the context of everyday neoliberalism. Big Data & Society, 2, 1–12.

Beer

(2016). Metric power. London, England: Palgrave Macmillan.

Bennett

Dodsworth

Noble

Poovey

Watkins

(2013). Habit and habituation: Governance and the social. Body & Society, 19, 3–29.

Bogost

(2013). Alien phenomenology, or, what it’s like to be a thing. Minneapolis: University of Minnesota Press.

10.

Bowen

(2006). Grounded theory and sensitizing concepts. International Journal of Qualitative Methods, 5, 12–23.

11.

Bradley

(2011). Originary technicity: The theory of technology from Marx to Derrida. Basingstoke, England: Palgrave Macmillan.

12.

Carter

Gibbs

Harrop

(2012). Metagames, paragames and orthogames: A new vocabulary. In Proceedings of the 7th International Conference on the Foundations of Digital Games (pp. 11–17), New York, NY: ACM Press.

13.

Consalvo

(2007). Cheating: Gaining advantage in videogames. Cambridge: MIT Press.

14.

Crawford

Lingel

Karppi

(2015). Our metrics, ourselves: A hundred years of self-tracking from the weight scale to the wrist wearable device. European Journal of Cultural Studies, 18, 479–496.

15.

Crogan

(2011). Gameplay mode: War, simulation and technoculture. Minneapolis: University of Minnesota Press.

16.

Crogan

(2016). War, mathematics, and simulation: Drones and (losing) control of battlespace. In Harrigan

Kirschenbaum

(Eds.), Zones of control: Perspectives on wargaming (pp. 641–667). Cambridge: MIT Press.

17.

Crogan

Kennedy

(2009). Technologies between games and culture. Games and Culture, 4, 107–114.

18.

DotaCinema. (2018). Dota 2 SUNSfan’s impressions of DotaPlus [YouTube video]. Retrieved October 4, 2018, from https://www.youtube.com/watch?v=uozbtxX4xgk

19.

Dota Plus—Statistics or Something More? (2018). Reddit. Retrieved September 29, 2018, from https://www.reddit.com/r/DotA2/comments/84al89/dota_plus_statistics_or_something_more/

20.

Drunk_soldier. (2018). All the flashiness…[Forum post]. Retrieved October 4, 2018, from https://www.reddit.com/r/DotA2/comments/83zpvw/there_should_be_an_option_to_disable_ingame_dota/

21.

Egliston

(2017). Building skill in videogames: A play of bodies, controllers and game-guides. M/C Journal, 20.

22.

Egliston

(2018). E-sport, phenomenality and affect. Transformations: Journal of Media, Culture and Technology, 31, 156–176.

23.

Egliston

(2019). Videogame analytics, surveillance and memory. Surveillance & Society, 17, 161–168.

24.

Glaser

B. G.

Strauss

A. L.

(2017). Discovery of grounded theory: Strategies for qualitative research. London, England: Routledge.

25.

Gr4b. (2018). Having a high KDA…[Forum post]. Retrieved October 4, 2018, from https://www.reddit.com/r/DotA2/comments/850w03/the_goal_part_of_the_dota_plus_is_not_what_dota/

26.

Gregg

(2013). Inside the data spectacle. Television and New Media, 16, 37–51.

27.

Grosz

(2013). Habit today: Ravaisson, Bergson, Deleuze and us. Body & Society, 19, 219–239.

28.

Hand

Gorea

(2018). Digital traces and personal analytics: iTime, self-tracking and the temporalities of practice. International Journal of Communication, 12, 666–682.

29.

Hansen

M. B. N.

(2015). Feed-forward: On the future of twenty-first century media. Chicago, IL: University of Chicago Press.

30.

Heidegger

(1962). Being and time. Oxford, England: Blackwell.

31.

Hunicke

LeBlanc

Zubek

(2004). MDA: A formal approach to game design and game research. Retrieved from http://www.cs.northwestern.edu/∼hunicke/MDA.pdf

32.

Jethani

(2015). Mediating the body: Technology, politics and epistemologies of self. Communication, Politics & Culture, 47, 34–43.

33.

KatsukiDreams. (2018). Precisely…[Forum post]. Retrieved October 4, 2018, from https://www.reddit.com/r/DotA2/comments/83w219/new_dota_plus_is_actually_p2w/

34.

Kelly

Watts

Payne

(2016). Can visualization of contributions support fairness in collaboration? Findings from meters in an online game. In CCSW ‘16 Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (pp. 664–678). New York, NY: ACM Press.

35.

Klagaren. (2018). If you can’t…[Forum post]. Retrieved October 4, 2018, from https://www.reddit.com/r/DotA2/comments/83wi2f/get_rid_of_the_live_aspect_of_dotaplus/

36.

Kou

Gui

(2018). Entangled with numbers: Quantified self and others in a team-based online game. Proceedings of the ACM on Human-Computer Interaction, 2, 1–25.

37.

Latour

(2004). How to talk about the body? The normative dimension of science studies. Body & Society, 10, 205–209.

38.

LordVolcanus. (2017). It should be…[Forum post]. Retrieved October 4, 2018, from https://www.reddit.com/r/PUBATTLEGROUNDS/comments/6zssvw/eye_tracking_in_pubg/

39.

Lupton

(2014a). Quantified sex: A critical analysis of sexual and reproductive self-tracking using apps. Culture, Health & Sexuality, 17, 440–453.

40.

Lupton

(2014b). Self-tracking cultures: Towards a sociology of personal informatics. In OzCHI ‘14 Proceedings of the 26th Australian Computer-Human Interaction Conference on designing futures: The future of design (pp. 77–86). New York, NY: ACM Press.

41.

Lupton

(2016). The quantified self. Cambridge, England: Polity.

42.

Lupton

(2017). Feeling your data: Touch and making sense of personal digital data. New Media & Society, 19, 1599–1614.

43.

Lupton

(2018). How do data come to matter? Living and becoming with personal data. Big Data & Society, 5, 1–11.

44.

Lupton

Pink

LaBond

Sumartojo

(2018). Personal data contexts, data sense and self-tracking cycling. International Journal of Communication, 12, 647–665.

45.

Massumi

(1987). Notes on the translation and acknowledgements. In Deleuze

Guatarri

(Eds.), A thousand plateaus (pp. 9–20). Minneapolis: University of Minnesota Press.

46.

Medler

(2011). Player dossiers: Analyzing gameplay data as a reward. Game Studies, 11. Retrieved from http://gamestudies.org/1101/articles/medler

47.

Merleau-Ponty

(2002). Phenomenology of perception. New York, NY: Routledge.

48.

Nelsonbestcateu. (2018). The Dota equivalent of…[Forum post]. Retrieved October 4, 2018, from https://www.reddit.com/r/DotA2/comments/83w219/new_dota_plus_is_actually_p2w/

49.

Paul

(2011). Optimizing play: How theory craft changes gameplay and design. Game Studies, 11. Retrieved from http://gamestudies.org/1102/articles/paul

50.

Paul

(2018). The toxic meritocracy of video games: Why gaming culture is the worst. Minneapolis: University of Minnesota Press.

51.

Pink

Fors

(2017). Being in a mediated world: Self-tracking and the mind-body-environment. Cultural Geographies, 24, 375–388.

52.

Pink

Sumartojo

Lupton

La Bond

(2017). Mundane data: The routines, contingencies and accomplishments of digital living. Big Data & Society, 4, 1–12.

53.

SheeverGaming. (2015). Playing Dazzle with sentry eye tracker + analysis [YouTube video]. Retrieved October 4, 2018, from https://www.youtube.com/watch?v=m3PLDZ-8JZM

54.

Stiegler

(1998). Technics and time, 1: The fault of Epimetheus. Stanford, CA: Stanford University Press.

55.

Stiegler

(2010a). For a new critique of political economy. Cambridge, England: Polity.

56.

Stiegler

(2010b). Memory. In Mitchell

W. J. T.

Hansen

M. B. N.

(Eds.), Critical terms for media studies (pp. 64–87). Chicago, IL: University of Chicago Press.

57.

Stiegler

(2011). Technics and time, 3: Cinematic time and the question of malaise. Stanford, CA: Stanford University Press.

58.

Stiegler

(2015). Automatic society: The future of work. Cambridge, England: Polity.

59.

Taylor

T. L.

(2006). Does WoW change everything? How a PvP server, multinational player base, and surveillance mod scene caused me pause. Games and Culture, 1, 318–337.

60.

Thompson

Lutz

Cosmelli

(2005). Neurophenomenology: An introduction for neurophilosophers. In Brook

Akins

(Eds.), Cognition and the brain: The philosophy and neuroscience movement (pp. 40–97). New York, NY: Cambridge University Press.

61.

Thrift

(2004). Movement-space: The changing domain of thinking resulting from the development of new kinds of spatial awareness. Economy and Society, 33, 582–604.

62.

Tobii Gaming. (2015). SteelSeries Game Analyzer Updates [YouTube video]. Retrieved October 4, 2018, from https://www.youtube.com/watch?v=Xgt5A1py3Kc

63.

Valve Corporation. (2013). Dota 2 [Video game]. Bellevue, WA: Author.

64.

Vellosso

Carter

(2016). The emergence of EyePlay: A survey of eye interaction in games. In Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play (pp. 171–185). New York, NY: ACM Press.

65.

Wang

O’Kane

Newhouse

Sethu-Hones

de Barbaro

(2017). Quantified baby: Parenting and the use of a baby wearable in the wild. In Proceedings of the ACM on Human-Computer Interaction (pp. 1–19). New York, NY: ACM Press.

66.

Wenz

(2013). Theorycrafting: Knowledge production and surveillance. Information, Communication & Society, 16, 178–193.

67.

Wolf

(2009, June 22). Know thyself: Tracking every facet of life, from sleep to mood to pain, 24/7/365. Wired. Retrieved from https://www.wired.com/2009/06/lbnp-knowthyself/