Abstract
This article utilizes empirical insight to critically reflect on the employment and life experiences of data workers in a high-performance environment. The context under study is that of elite sport and the role of performance analyst – a specialist field comprising the use of technology and data in the process of improving sport performance outcomes. Using in-depth semi-structured interviews, the social and organizational environment encompassing data work is explored to examine how it may enable or constrain certain labour practices. The findings reveal implications concerning the nature of data work, and in particular how the pursuit of data at scale escalates issues regarding work-life balance. By acquiring insight into the everyday experiences of analysts and the nature of datafied knowledge production, the study demonstrates how participants find meaning in their labour through establishing credibility and a connection to the affective dimensions of work. We conclude by offering practical recommendations for those entering into this field of work that centre on the importance of enculturation and the collaborative nature of the role, reinforcing the imperative that a human-centred approach to examining data work helps us to better understand how data representations come into being.
Introduction
The proliferation of big data and advanced analytics within the sports sector has generated unprecedented informational capacities, shaping the modalities through which sport is (re)produced and consumed, operationalised by professional organizations, and experienced by athletes in relation to their performances (see Arth & Billings, 2022; Hutchins & Stauff, 2024; Silk et al., 2016; Watanabe et al., 2021). This accelerated demand for data analytics in the sports industry has led to the development of new technologies, software, the trading of data, and the outsourcing of labour to conduct analyses (Hutchins, 2016). As such, data analytics and performance analysis have become a prevalent feature of organizational life within professional sport to such an extent that it is now permeating youth sport with a greater intensity (see Sanderson & Baerg, 2020). However, in the social sciences literature, the nature of work in sport in the so-called digital economy is lacking for attention. And while performance analysis has received scholarly attention to date, existing research often adopts applied or technical perspectives – meaning a focus on how analysis can improve sport outcomes for individuals or teams (see Szymanski, 2020). What remains to be studied is what this means, in empirical terms, for the workforce in sport.
In considering the above, our research was designed as an initial step towards addressing this occupational enquiry, specifically through a case study of performance analysts – employees central to the ‘datafying’ of sport. Drawing upon interviews with 20 analysts in elite sport, this study sought to address the following research questions: What are the work and life experiences of analysts in elite sport organizations? And what role does technology play in shaping these experiences? The focus on performance analysts, and the sporting context within which they operate, is pertinent in that it allows us to better understand the work-life conditions in which data representations come to be and the wider implications this has for shaping our broader understanding of work in the digital age (Beaulieu & Leonelli, 2022; Beer, 2015). Additionally, the research discussed herein presents a further contribution by examining data work for how it is experienced, as opposed to what it can offer, providing insight into the expression of meaningful labour within this occupational domain. This is particularly relevant to understanding the human and organizational complexities that influence the enactment of data processing and the way meaning is ascribed to analysis; an area of research enquiry that remains underdeveloped (see Watanabe et al., 2021).
In attending to the above concerns, the article is organized as follows. First, we review relevant literature on labour in the digital economy, followed by an examination of the social conditions of data work and how this intersects with the context of professional sport. Second, we review the study protocol we followed in this research. Third, we discuss three key themes emerging from interviews: (1) the pursuit of data production and analysis at scale as key components of analysts’ responsibilities; (2) the elusiveness of work-life balance and job security; and (3) factors that render work experience meaningful – most notably, the affective qualities attached to the role and the capacity to demonstrate value within the organization. Fourth, we discuss study findings and consider their significance in understanding the pursuit of meaningful work in the digital economy. Fifth and finally, we conclude with recommendations and suggestions for further research.
Labour in the Digital Economy
Broadly, the digital economy involves economic practices reliant on the Internet and related information and communication technologies (ICTs). Barefoot et al. (2018: 7) add three other elements to their definition: “(1) the digital-enabling infrastructure needed for a computer network to exist and operate, (2) the digital transactions that take place using that system (‘e-commerce’), and (3) the content that digital economy users create and access (‘digital media’)” (also see Jordan, 2020).
In one sense, we can consider historical and contemporary drivers of the present-day digital economy. Huws’s (2014) account identifies economic, political, and technological factors that have engendered a “sea change” in the character of work over time. Huws (2014) charts a transition (at least in the global North) from the post-war Keynesian state, to an era of deindustrialization beginning in the early 1970s, to a deregulatory neoliberal era where digitization helped the process of outsourcing labour (e.g., in the form of call centres) – and finally to the current moment whereby ICTs have become a taken-for-granted aspect of all work. Huws’s (2014) point is that digital work, in some form, is now seemingly inescapable. As another case in point, Moore (2017) outlines the use of surveillance technology to quantify the labour of manual workers, service workers, and knowledge workers alike. Work on online platforms such as websites and apps – part of the so-called ‘platform economy’ – presents another example of digital labour. Nemkova et al. (2019: 226) describe some of the different forms this can take: on the one hand, “small digital human-computing tasks such as tagging images and classifying text into categories,” and, on the other, “more creative and complete work experiences, such as designing a company’s brand guidelines or logo, and programming a website.” Platform work also bleeds into ‘playbour’, meaning playful ‘labour’ whereby consumer attention is captured, kept, and sold to advertisers (e.g., via social media), and thus creates commercial value (see Fuchs, 2014).
In another sense still, the digital economy has discernable implications and has stimulated interest in the possibilities and threats that new technologies may generate surrounding specific matters such as work-life balance (e.g., see Agger, 2011). For example, technology enables flexibility (e.g., working from home or on one’s own schedule), but also potentially foments job insecurity. Graham et al. (2017) describe empirical cases of workers in the global South accessing work opportunities on digital platforms ‘from afar’. However, employers are also geographically unrestrained in their ability to access competing labour markets (also see Anwar & Graham, 2020). Dyer-Witheford & de Peuter’s (2006) account of the political economy of video game production tells a story of game developers in Canada enjoying the flexible and creative components of their work. Yet problems arise in (for example) the expectation that workers will put in excessive hours during ‘crunch time’, as deadlines grow near (also see Dyer-Witheford, 2015). Flexible work is also often precarious, meaning it can be insecure and uncertain (e.g., due to irregular hours), provide limited economic and social benefits (e.g., in terms of pay), and is restricted in terms of statutory entitlements (e.g., as provided through regulation – Kalleberg, 2018). With an intensified emphasis on the automation of workflows, the human contribution associated with these tasks – and data work in general – becomes increasingly erased, further signifying the precariousness of these roles and their lack of visible contribution (Graham & Shaw, 2017).
Despite the accelerated influence of analytics within the workplace, empirical insight into the datafication of sport and the work-life experiences of those inherently tied to this progression within the industry are lacking. Thus, and in light of the concerns outlined above, this paper aims to contribute towards a better understanding of the circumstances under which data and their representations come into being. In doing so, further insight can be acquired that adds to existing knowledge surrounding data work, the individuals involved, their processes and the capacity to find some sense of meaning or value within the realms of digital labour itself.
Big Data, Sport, and Meaningful Labour
Big data is the notion that data can be collected, analyzed, and shared with greater ease and speed than ever before. A series of key ‘v’ terms often feature into definitions of big data: data are now accessible in great volume and variety; they move with velocity in the interest of producing value (e.g., Mayer-Schönberger & Cukier, 2013). For boyd and Crawford (2012), big data comprises technological, analytical, and mythological components. On the technology front, big data is about “maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets” (boyd & Crawford, 2012: 663). The relative ease of data collection is crucial in this regard. For example, through technological innovation, data capture is increasingly automated and thus ‘passivized’: we constantly and often unknowingly generate data through (for instance) the GPS tracking capacity of our smartphones (Andrejevic & Burdon, 2015; Millington, 2018). On the analysis front, big data is about pattern recognition in the interest of making claims. The mythological component is that these first two elements promise a higher form of intelligence based on truth, accuracy, and objectivity.
Data analysis and statistics have long been important to sport. Accordingly, sport has been a site for the popularization of big data and its application towards unearthing hidden patterns to improve organizational efficiencies (see Hintz, 2022; Phillips, 2019; Stauff, 2018). Per Stauff (2018), sport has played “an important role in the emergence and shaping of contemporary big-data culture” (p. 46). In recent years, this has been brought to mainstream prominence through publications such as Michael Lewis’s best-selling book Lewis (2003) and the subsequent 2011 film adaption starring Brad Pitt. As such, the desire to draw upon big data analytics as a tool to distil and reproduce certain knowledge in sport has manifested in many different forms, both reflecting and producing key features of the ‘age of big data’ (Beer, 2015). In one sense, technological innovation presents opportunities for quantifying performance in new ways, and to a previously impossible extent: performance in both games and training can be captured and translated into data points via motion-tracking cameras; the body is quantified through wearable technologies that register GPS coordinates and/or physiological data (e.g., on heart rate); lifestyle is known through tracking devices worn away from the playing field and through online platforms where athletes log daily information on things such as diet, sleep, and mood (e.g., see Manley & Williams, 2022; Silk et al., 2016; Baerg, 2017; Hutchins, 2016). This is big data’s volume and variety. In another sense, the seeming aim of such initiatives is to achieve ostensibly better knowledge.
Big data in sport also gives cause for optimism as stakeholders welcome technological innovation in pursuit of success in an exceedingly competitive industry (Millington et al., 2025). Yet big data in sport has also been looked upon with a critical eye. For example, Hutchins (2016) contends that the supposed ‘democratizing’ effects of digital media and networked communications are not readily apparent in elite sport. The concern in this regard lies with a divide between ‘data-rich’ and ‘data-poor’ entities: “Already strong elite men’s sports continue to become stronger in a multiplatform media environment where the pressure to produce and distribute content is constant, making it difficult for many women’s and semi-professional sports to gain greater attention” (Hutchins, 2016, p. 505, also see Hutchins & Rowe, 2012). Additionally, on- and off-field performance tracking allow for the constant scrutiny of athletes, raising ethical concerns over matters such as surveillance, ‘data overload’, and data rights (Manley & Williams, 2022; Millington & Millington, 2015).
The embrace of big data within the context of sport has also led to an increased requirement for analysts. These roles have become an integral part of improving organizational efficiencies supported by data analytics and an evidence-based rationale. To this end, we seek to better understand how those engaged with such labour establish a voice and impart value concerning their contribution towards organizational operations. Here consideration is given to the pursuit of data production and analysis at scale as a key responsibility, the broader impact of digital labour on work-life balance, and the importance that socialization plays in establishing visibility, credibility, and meaning for analysts that operate in a high-performance environment.
Within the context of sport, few studies seek to explore the concept of meaningful work (see Ronkainen & McDougall, 2025). Research that has broached this topic highlights that meaningful work can often been found in labour that seeks to service others or contribute towards specific organizational outcomes that extend beyond the immediacy of individual tasks (Baer, 2023; Mansouri et al., 2024). Additionally, many employees within the context of sport perceive their occupation to be a ‘calling’, investing time in their role as a reflection of the passion that they hold for it (Huml et al., 2024). Although a growing literature base is evident, a call for further research in this area is warranted, acknowledging that meaningful work in sport should be considered both fluid and fluctuating over time (see Ronkainen & McDougall, 2025). This is particularly pertinent where technology is a core component of occupational life. New systems of analysis, software, and technological hardware are evolving at a rapid rate for analysts in sport. Thus, the quest for meaningfulness in the day-to-day of work will no doubt be reflective of these changes, specifically as new technological systems come to alter both workplace practices and relations.
Context and Methods
Performance analysis can be defined as a discipline, or field, within the wider sports industry whereby sport is systematically observed in the interest of improving individual or team performance. This definition allows for different manifestations of data work across a range of sport-related contexts. For example, analysts can work ‘in house’ for teams, offering insight alongside coaches and other specialists, such as sport psychologists and strength and conditioning staff. Freelancing is also common in the field. This might mean, for example, contracting one’s services to a team such as a professional club or high-level national or regional team. There are also analogous terms, such as data analyst and video analyst. A broad understanding of performance analysis also allows room for commercial companies that offer sport-focused data and analytics services (e.g., servicing teams as well as sectors such as sport broadcasting, sport media, and betting and gaming). The divide between analytics companies of this kind and ‘in house’ analysts is potentially blurry – in that the latter might make use of data supplied by the former. That said, our concern herein lies with analysts working in team contexts, and not commercial data/analytics companies.
The study in question involved interviews with a sample of 20 participants with experience in performance analysis at the elite level. ‘Elite’ in this research was taken to comprise professional sport or international-level amateur competition. 1 The rationale for the chosen sample size of 20 is two-fold. First, given the dearth of research on the work and life experiences of analysts in sport, semi-structured interviews with a sample of this size allowed for depth of investigation through open-ended questions on key topics (see below) and ‘probing’ questions seeking further elaboration. This is in keeping with our exploratory approach. Second, we regard our participants as ‘key informants’ with specialist knowledge of data work in sport.
Participant recruitment involved a purposive approach to sampling. We developed and continually updated a contact list – for example, via searches for performance analysts with an online presence (e.g., a public web profile) – and then employed a snowball recruitment method thereafter. We included participants from a range of sports – both men’s and women’s 2 – though interviews often focused on association football, rugby, and cricket. Participants had varying levels of experience in the field of performance analysis in general and with various analytics tools and responsibilities in particular. 3 The only inclusion criterion was that participants in fact had experience in performance analysis at the elite level. Our sample included: ‘in-house’ analysts permanently based in sport teams (n = 11), including three participants with experience exclusively as interns; participants with past experience as analysts who now worked primarily for teams in other roles (n = 3); freelancers working on a full- or part-time basis (n = 5); and one participant developing online performance analysis resources who had experience consulting with professional teams (n = 1). That said, and as another reflection of the variegated nature of performance analysis, these different participant ‘types’ are not neatly circumscribed. For example, ‘in house’ analysts sometimes crossed over into other roles, such as coach, while freelancers had experience working with teams on a permanent basis. Furthermore, many participants worked their way up from internships over time, meaning interviewees could reflect on internships and speak to their place in the field. 4
Where possible, interviews took place in person; otherwise, they were held via video conferencing software. The average interview time was 51 minutes. The interview guide featured questions that typically covered the following areas: current and/or past roles in elite sport; reasons for entering the field; technologies involved in the performance analysis role; typical workload on a weekly and yearly basis, and whether and how work responsibilities have changed over time; the prospect of work-life balance; any impacts of workload on leisure or family life; participants’ perceptions of remuneration and job security in the field; and future trends in performance analysis. As noted, questions were generally open-ended and focused on the European context.
A thematic analysis of pseudonymized interview data was subsequently undertaken. Through an iterative process of scanning and rescanning the data, and by adopting a pattern coding approach, material related to the initial summarising segments of data could be grouped into a smaller number of themes to create more meaningful units of analysis (Saldaña, 2019). For example, when reviewing data related to the analysts’ experiences of work, issues associated with data production (i.e., volume), data waste, data effectiveness, and marginal gains reoccurred with high frequency. Therefore, ‘the pursuit of data production and analysis at scale’ emerged as an overarching theme to explore the technological arrangements that shape the working lives of the analysts and speak to broader issues of datafication that underpin their everyday practices. By cross-checking data sets and identifying patterns throughout the data collection phase, two of the research team were able to verify the most common reoccurring threads in the participants’ accounts and establish key overarching themes. The three themes discussed in the findings section are as follows: Data at scale (example codes: ‘Data – effectiveness’, ‘Data – volume’, ‘Tech arrangement’); Work-life balance (example codes: ‘Organizational communication’, ‘Workload – control over work’, ‘Hyperemployment’); and Meaningful work (example codes: ‘credibility’, ‘establishing value’, ‘labour – emotive and affective’).
Two key ethical issues requiring attention were the sensitivity of study topics and pseudonymization. The former matter was addressed through a process of obtaining informed consent and by communicating that participants could discuss their view on industry trends in general if they were uncomfortable divulging personal information. The latter issue has been addressed herein by removing identifying details and reporting only in broad terms on the study sample. University Research Ethics Board approval was granted before the research began.
Findings
6000 Clicks: the Pursuit of Data Production and Analysis at Scale
The first main finding from this research pertains to performance analysts’ common responsibilities and the technological arrangements that underpin their work. Participants’ accounts suggest a spectrum of work-related tasks, from more rudimentary responsibilities at one end (especially data entry) to more analytical duties at the other. In either case, technology plays a prominent role.
Participant Y described a technological assemblage, comprising both hardware and software, that is common to performance analysis. Video cameras (either built-in to the playing field or set-up ahead of time) allow player movement to be captured. Software programs such as Sportscode are used for data entry and analysis. Laptops and tablets are used by analysts and coaches, who might be strategically located (in a physical sense) around the playing arena. Staff might also be connected via audio communication technologies to share insight as gameplay unfolds from their different vantage points. Other technologies include wearable GPS devices, Go-Pro wearable cameras, and drones, to give a bird’s-eye view of the playing field. Software for presenting and communicating results, such as iMovie, PowerPoint, and WhatsApp, were discussed as well. There was also mention of the prospect of automated data entry through artificial intelligence, suggesting that this will alter the role of data work by requiring a greater knowledge of analytics and the capacity to better embed oneself into the workplace culture, as reflected by Participant E: It’s a matter of time before some of that technology [AI] you know, drips, feeds down into something like sport…I think somebody who can write sequel (SQL) code or whatever it is, but also walk into a dressing room and have a real conversation with a player or a coach, and being able to bridge those two worlds I think will be a big thing.
Despite this, work tasks at the more rudimentary end of the spectrum generally involved using software to translate performance, as captured on film, into standardized measures for analysis. This came up as Participant R reflected on work responsibilities as an intern in elite-level cricket: If you’re talking a four-day game, if you’re doing it on your own, the match analysis during the game is unbelievably difficult, because there’s 96 overs in a day. So, let’s round it up to 600 balls. And you’re recording every ball, where it lands, where the batsman hits it, what shot he is playing and how many runs, where you speculate the ball’s going to go beyond the stumps, if they’re out, who fields it – so you’re clicking ten times on different performance indicators, so that’s 6000 clicks. But then you’ve got to be accurate at each of those, every ball. So that’s a really hard job four days straight. And that’s mentally taxing.
To be sure, data entry is something of an analytical process in its own right. Subjectivity rears its head in an ostensibly objective process when Participant R speculates on where the bowled cricket ball would have gone, if not struck by the batter. Nonetheless, the point of 6000 clicks in this case is to render data more conducive to subsequent ‘higher-level’ analysis; a video file is itself a data file, but it is too ‘raw’ in its own right to deliver insight.
Other participants spoke of similar circumstances. Participant M describes the thoroughness of data collection incumbent to his role in elite-level association football: “One of my responsibilities is filming training every single day, so every single second of training for the past two seasons, we have logged.” Participant G says this is something that travels down the organizational hierarchy: “For analysts, there’s this big meeting at the start of any work where you talk to the coach about, ‘what do you want analyzed?’ … And I know these meetings, every time the coach says he wants every single thing in the whole world that can possibly be measured.”
Data production sets up data analysis. Towards the other end of the spectrum is work that involves pursuing and delivering insight that will subsequently improve performance. Participants were optimistic on this matter – unsurprisingly so, as this is the sine qua non of performance analysis. Participant Y connects the pursuit of data at scale to the pursuit of success at the margins: I think the fact that we are working in an elite environment and you’re looking for that 1%, you’re looking for that thing that really is going to make the difference … So, for example, you can start looking at just our KPIs [Key Performance Indicators] for a game and then you can start looking at how our KPIs change when we are losing by one goal, by two goals, by three goals, when we have certain combinations of players on. And before you know it, you can be looking for a question but it’s going to take you a long time to find the answer.
Other participants pointed to initially-hidden insights that can emerge from large and systematically produced datasets. Participant S explained the added value that comes from analyzing and communicating individual and team performance: “The main thing I see PA [performance analysis] as being able to say, [is] for a coach to be able to say, ‘We’re doing this because I know this is what happened’, as opposed to, ‘I’m doing this because I think this is what happened.’”
This is not to say the process is perfect. Big data evidently engenders concerns about data waste. Participant Z levies a critique at spendthrift clubs in major sports: They absolutely waste what they’ve got. They pump all this money in, they’ve got a team of analysts, and they produce all this data. Reams of data. I’ve seen reports from certain clubs, even nations, that are 40 to 50 pages long. As a coach, how are you going to deal with 40 to 50 pages of data? It’s impossible.
Participant C, who has experience as both an analyst and coach, articulates a similar view: “I’d ask our analyst what percentage of your work do you think is effective and has impact? And, what percentage could you immediately throw away? And it’s scary, the answer, it’s about 10%–20% effective, 80%–90% [ineffective].” When asked why data are collected in such vast quantities if they so easily go to waste, Participant C added that the point is to account for potential future uses of data. If archived, data might deliver valuable insight down the road, and so there is an imperative to collect it in the present.
It’s Pretty Full on: Work-Life Balance and Job Security
The second main finding from this research involves participants’ concerns over work-life balance and job security while working in the field. Evidently, data work for analysts working ‘in house’ with teams can be characterized by long days and weeks. Returning to Participant R, data entry was only part of their game-day routine: If we’re playing a four-day game, you’d sort of arrive at the ground half eight, nine o’clock. You get into your routine of setting up the camera, setting up the technology, making sure that’s all working fine before you think about the players … Early morning, data does take priority, so making sure that all your technology and all the players’ well-being scores and workloads are up to scratch, up to date, then you start the game at eleven. Eleven until six you’ll be recording, and then you pack your stuff away and maybe start some of the review things that the coach might want for the next morning, and you could easily be going home about eight, nine o’clock. It’s a long day, 12-hour day, sometimes more for a 4-day game. Yeah, and then, back round again for the next 3 days.
With respect to weekly routines, phrasing like ‘full on’ and putting in the ‘hard yards’ was common. As said by Participant E: “I think it’s pretty full on to be honest. It’s a role that has become about quantity over quality in many ways.” Participant A described how, during especially-hectic periods, “it’s just like, from the moment you wake up to the moment you go to sleep, everyone is doing something.”
While workload was heavy, participants commonly described a yearly work pattern characterized by peaks and valleys. For Participant M, a performance analyst in elite association football, it is a case of “polar opposites,” with “all hands on deck” 6 days a week in-season, but then a suitable amount of holiday time once the season ends. Other participants recounted how out-of-season periods could be devoted to bigger-picture projects or to tasks like auditing and replacing equipment. Participant K also questioned whether analysts would be empowered enough to reduce their hours periodically, or whether the high-performance culture would demand a similar commitment during ‘off’ periods, albeit towards different tasks.
An outcome of extended work hours is diminished leisure time, which potentially strains social and family life. Participant B connects performance analysis to the wider employment landscape in this regard: It is the way of life, and luckily, I’ve got an understanding wife … I think what you will find with analysis, and probably most jobs these days, is that, I see it all the time now … you can take your work home with you. Whereas traditionally, you know 15, 20 years ago, you finish at this time, you go home, and you actually have mental space where your mind’s clear of work.
Participant B adds that communication platforms such as text and WhatsApp can exacerbate this trend: “It’s really easy for the coach to text you to say, ‘can you get me this?’ Or, ‘can you have a look at this?’ Or, ‘I’ve been thinking about this’. So yeah, that’s a challenge.” Participant F similarly described a sense of being consistently “on call.”
Elsewhere, by their own calculation, Participant W works 80-hour weeks in cricket, though is generally sanguine about this: It’s not an issue for me at the moment and it’s not something that I feel awkward doing because I’d get home and after a long day, I can easily put the TV on and keep working in front of the TV. But it is, the first thing, I’m back with family or if my girlfriend is down and is staying with me, it is the first thing they’ll say is, “You’ve been at work all day, why are you still working?”
Freelance work was described as potentially labour-intensive as well. For example, having worked as both an ‘in-house’ analyst and freelancer, Participant X described how, either way, balance has been elusive: It’s tough to manage, I’ll be honest in saying. It is an incredibly painstaking, time-taking domain to work in. I think one of the biggest challenges I face is that you can’t switch off from it. Even if I’m not sitting in front of film or looking through stats, I’m probably thinking about it. I’ve never found a middle ground in it. I’ve never found balance in 10 years of coaching and analysis. And I would probably say, the more popular it becomes the more life-overtaking it’s gotten. So yeah, currently it’s 7 days a week. Most of the kind of detailed analysis I do is late at nights. So, kind of 2 or 3 o’clock bedtime is not abnormal.
The challenge of freelancing, per Participant X’s account, arises partly from the fear that time off will create an opening for competitors. This is “psychologically draining” and something that is “a strain on family.”
Related concerns also involved the issue of job security. For Participant Z, performance analysts would probably be the first staff members to go in the face of financial strife at the organizational level: “You could be out the door tomorrow if you’’re not needed or wanted … I’’d be going back to square one and fighting for other chances.” Other participants were more direct, indicating that the high supply of analysts – specifically, a young workforce – and the limited availability of positions exacerbated feelings of insecurity. As Participant D noted, “there’s always somebody younger who will do it for cheaper”. Redundancy can also cascade downwards, from the head coach to other staff. A countervailing force in this regard is that analysts can move with coaches from one opportunity to another. Still, insecurity in this sense looms large with many indicating that both technical and social skills were key requirements to securing employment, particularly the need to ‘fit in’ by presenting likeable characteristics and embedding oneself into the organizational culture.
You’ve got to Love what You do: Data Work as Meaningful Work
A lingering question involves why performance analysts pursue work opportunities that potentially feature long hours and limited job security. A third key finding from this research involves factors that render data work meaningful, including the processes of socialization required to establish a sense of value in the role.
For many of the analysts interviewed, a large part of their role involved forming relationships with senior personnel to ensure that the knowledge they generated from statistical analysis could be conveyed to those in authoritative positions and thus demonstrate their value to the organization. Close ties with managerial staff provided the opportunity for analysts to offer opinions that might otherwise go unheard: I think there are still a lot of analysts out there who just get caught in that process and get caught with just a lot of data collection type stuff. And I suppose it goes two ways, they never voice their opinion but they never get asked to voice their opinion. But actually, being able to just have a normal relationship, a good relationship with someone [coach or manager] where I can go out with them for a beer…then that might come into the working environment…it just builds into the relationship like into your operating relationship [Participant A].
Analysts referred to the ‘soft skills’ and enculturation associated with their work. Participant B highlighted the added importance of establishing relationships with those in positions of authority to convey relevant information and perform the role successfully: I mean you could be the best analyst in the world but actually if you don’t have those relationships…lots of feedback I hear from the young colleagues, people doing their Masters, they don’t teach that side of things, it’s all kind of the hard skills when actually you need the soft skills to present the hard skills.
The time invested in establishing oneself as a legitimate member of the organization through a process of social integration conferred value to the role of the analyst. In turn, this provided a sense of inclusion and relevance that made everyday work more meaningful: You might have a [team] that doesn’t value their analyst at all and they’re just based in a shed somewhere on their own and don’t ever get involved with the professional staff. Whereas, someone like myself who’s got good relationships with all the backroom staff and with all the players, and based in the dressing room on match day, you know, you’re a real part of the squad and you’re a large part of it [Participant W].
Comments of this kind, specifically on the nature of inclusion, dovetail with the notion that analysts make valuable contributions to success at the margins. Work is meaningful in that it makes a difference. To be included is to inform decision-making and, in turn, help influence performance outcomes. Indeed, meaningful work for the analysts further manifested in the shared experience of a successful outcome, as noted by Participant Z and Participant Y: It’s great to be able to support people who have the same dream. I couldn’t have done it myself, but at least I can help someone on that journey. And that’s the rewarding bit [Participant Z]. You won’t ever be a millionaire as an analyst but the, there’s pros and cons for every job isn’t there…There will be no amount of money that would be able to pay what that experience [of success] was and so, there’s swings and roundabouts, I guess it depends what drives you really, and what kind of what makes you sleep easy at night [Participant Y].
Additionally, the love of sport as a career determinant and the emotive qualities associated with the role were dominant themes that arose in nearly every interview. Here Participant S articulates this idea: There’s just something about sport that people love. And being in that environment is such a draw. And yes, I’ll go through stressors and I’ll go, “I’m really tired and I haven’t had a day off in ages,” but it gets to game day and I absolutely love it. And the highs and lows you experience with the team are something that outweighs everything else.
Other analysts likewise spoke to the experiential qualities of the role and how they coincide with the affective dimensions of work in sport. For Participant Z, “You’ve got to love what you do to do it.” In Participant Y’s words, “You absolutely live and breathe how the athletes and everyone else feel when you win and lose.” For Participant E, performance analysis is “the next best thing” after falling short of a professional career as an athlete. Such reflections echo insights from prior research, whereby sport employees have perceived their work to be a calling, accessing meaning through the passion of their labour and reinvesting this into an increased commitment of time (see Huml et al., 2024).
These findings highlight the potential role of collaborative experience in shaping everyday conditions of data work. Although technical skills were a requirement (e.g., basic production work, coding, and analysis), a significant proportion of the analysts’ role involved interaction with non-data workers. The human labour invested in developing interpersonal relations was integral to acquiring a voice within the organization, establishing credibility, and imparting meaning to the role – aspects of data work that remain largely unseen.
Discussion
We argue that the implications of the above findings are three-fold. First, the pursuit of data at scale suggests a data imperative, or what has been called dataism, is at play in elite sport: steadfast belief in the merits of tracking human activity, thoroughly and in myriad ways (Harari, 2017; van Dijck, 2014). Volume and variety of data are deemed valuable factors in success. To be sure, there are other ways of observing the importance of ‘datafying’ performance; this is evident, for example, in the use of statistics in sports reporting and television broadcasts. In one sense, however, this research confirms at least part of the rationale for datafying sport. From a team perspective, the point is to provide value at the margins – something between the extra 0.01% and 1% – in an incredibly competitive industry. Per some participants’ accounts, even seemingly wasteful practices make sense in their capacity to deliver value in the future. In another sense, this research shows the extent to which this desire to find marginal gains is operationalized. As Couldry and Yu (2018) say of datafication in general, the question of whether data should be collected has seemingly given way to questions of what data can do.
Second, data work in sport is labour intensive. Evidently, work hours in performance analysis can be exceptionally long, particularly at peak periods of the year. And work invades leisure and life in many ways. This goes along with the reported view that work can be insecure due to factors such as the abundance of workers seeking jobs in performance analysis.
Important to these purposes is the fact that this second point dovetails with the first. It is partly data and technology that render performance analysis so labour intensive. This is true in ways that transcend performance analysis. As Moore (2017) writes, “[p]eople can now never really switch off, even while asleep or on holiday. These pressures are compounded in a new world of work where employees are ‘always on’, or even ‘hyper-employed’” (p. 149). At the same time, however, it is the nature of data labour specifically that seems to diminish the prospect of work-life balance. The desire to measure ‘every single thing in the whole world’, as one analyst put it, has consequences: someone has to do the measuring, analyze the data, and communicate their findings.
A key implication of this research involves the relationship between labour and time in the digital economy. ‘Flexibility’ is now a common work characteristic – replete with both potential benefits and pitfalls (Huws, 2014) – and researchers have documented the time-crunch that comes with increasingly short product cycles (see Kline et al., 2003). In elite sport, the pursuit of success at the margins is time sensitive. A quick turnaround can yield an advantage. Thus, the integration of technology helps shorten the cycle for delivering insight, but it also expands the workload within the shortened period. This speaks to the large amount of concealed human labour that often comes with an increased utilization of technology in the workforce – an aspect of work that seemingly places employees in a vulnerable position through the illusion of automation, yet simultaneously demands an increased workload from human input at a localized level (Selwyn, 2021).
Third and finally, the above findings demonstrate perceived benefits of data work. In examining digital freelancing platforms, Nemkova et al. (2019) note the distinction between ‘manifest’ and ‘latent’ dimensions of meaningful work: the former involves instrumental rewards; the latter includes, “intrinsic rewards of autonomy, creativity, authenticity and external recognition among others” (p. 228). The manifest rewards of pay and benefits are no doubt important to performance analysts, but the above findings point to the significance of latent meanings as well. Sport has an allure, and providing value, even at the margins, can be important to organizational success and personal satisfaction.
To be sure, that performance analysis is at times construed as a ‘labour of love’ is something that might help rationalize job features like exceptionally long work hours and thus perpetuate an unhealthy work-life balance and potentially exacerbate the risk of burnout among employees (Huml et al., 2024). As evidenced by analysts’ accounts in this research, the role is characterized by expectations to work flexibly, requiring individuals to operate seemingly everywhere, at any time, and at the discretion of those in positions of authority. To refute such expectations might render the role of the data worker surplus to requirements, intensifying job precarity and encouraging analysts to continue working long ‘after hours’. Similar to other professions where the traditional distinction between work and leisure is perceived as unclear (see Dyer-Witheford & Peuter, 2006), the concern here is that the challenging and laborious dimensions of data work in sport can be obscured by the notion that such work is inherently rewarding. This point was particularly evident in this study in one analyst’s reflections on how their partner questioned the necessity of working at home after an already-full day of labour. As such, there is a need for caution in celebrating the ‘labour of love’ discourse in data work. Equally, though, we should not overlook that analysts find meaningful experience in data work and the outcomes that follow from it.
Additionally, findings point towards broader issues that span across industries and throughout a range of sectors incorporating data work. Namely, for data workers, establishing relations of trust with non-data workers to acquire credibility and a sense of value can be important to experiencing meaningful labour. The analysts within this study were required to interact with a range of stakeholders (e.g., managers, coaches, athletes, sport science support staff) to form positive relationships and translate their work effectively. Whilst technical skills were a necessity for this particular role, the ability to negotiate trust and establish a sense of credibility was equally integral to the technical operations of their everyday work. Such findings further reinforce the collaborative and heterogeneous nature of data work itself, requiring interaction with a varied range of influential personnel – both technical and non-technical – to ensure that data can be effectively put to work and influence organizational decision-making in a meaningful way.
Conclusion
In a bid to explore opportunities and tensions associated with data work, and in an occupational domain where digital data constitutes a key component in the operations of the organization, we posed the following research questions: What are the work and life experiences of analysts in elite sport organizations? What role does technology play in shaping these experiences? The first question is answered through the three key study themes highlighted above: (1) the pursuit of data production and analysis at scale are generally key components of performance analysts’ responsibilities; (2) performance analysis can be labour-intensive, whereby the acquisition of data at scale can evidently escalate work responsibilities in the form of extensive human labour; and (3) work experience in the field remains meaningful through analysts’ affective attachment to sport and their perceived contribution to organizational operations. To the second question, the ongoing integration of technology into elite sport allows for success at the margins (or at least the pursuit thereof) and thus helps render data work more rewarding. This is significant in that prior research examining the role of technology in shaping data work highlights the lack of visibility of workers’ contributions beyond their peers and immediate superiors, thus rendering their role less meaningful within the broader context of the organization (Ringel & Ribak, 2020). While meaning in data work could be found through a rewarding contribution towards successful outcomes, this was dependent on operating collaboratively with others to negotiate a sense of trust and credibility. Our findings reinforce the case for better understanding the human and organizational work that has come to shape datafied knowledge production, highlighting the social practices and process of enculturation that dictate the ‘everyday’ of data work – aspects of digital labour that remain understated and lacking investigation.
Two key areas stand out as requiring further attention. First, the above findings have implications for organizational culture. If, as some participants suggested, feeling ‘part of the squad’ is important to delivering impact, and communication at and even outside the workplace (e.g., social events) can be part of this, it raises important questions of inclusivity: Who benefits from this arrangement? Who is able to form connections outside the workplace that have an impact on trust and communication at work? Our inquiry herein was meant as an initial foray into analysts’ perceptions and experiences. And while participants spoke to ways of succeeding in the workplace, findings also point to a need for organizations to create positive and inclusive workplace environments for analysts and others – and for further research in this area.
Second, the question of automation looms over the above findings. As noted, artificial intelligence (AI) was mentioned in some interviews in this research. In one sense, technologies such as AI cameras that track and code sport performance will no doubt be a boon to performance analysis. Laborious tasks such inputting 6000 clicks, as described above, can be offloaded to technology, freeing human labour for more creative and potentially meaningful analytical tasks. In another sense, however, human labour might be made altogether redundant, at least for data entry jobs that are often a first step in entering a field, and perhaps eventually for more senior, analytical jobs as well. The prospect of AI rendering human labour redundant is of course not unique to sport. Here again, the context of sport reflects and contributes to wider trends, making it an important site for continued research on data work and the digital economy.
Footnotes
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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.