Highlights
- The rising importance of data and technology in sport over the last two decades reflects the arrival of a wider digital economy
- There are “twin revolutions” occurring in sport: a change in how we understand the world around us, and a change in the hardware and software available for training and learning
- The product of these twin revolutions has been the “hyperquantified athlete”
- In this SIRCuit article, Canadian researchers consider the wide-ranging implications of and questions raised by sport’s digital turn
The 2011 film Moneyball was a critically acclaimed, Academy Award-nominated, box office success. This is quite something, given that the movie centres on debates over baseball strategy and statistics.
Moneyball dramatizes the story of the Oakland Athletics Major League Baseball (MLB) team in the early 2000s, and specifically the team’s adoption of unconventional player evaluation methods to compete on a limited budget against baseball’s biggest spending franchises. In the movie, actor Brad Pitt plays the role of Athletics General Manager Billy Beane. Jonah Hill plays Beane’s newly-hired assistant, Peter Brand (a fictional character said to be based on Athletics executive Paul DePodesta).
In a key scene, Beane and Brand try to convince other Athletics staff that conventional methods of player evaluation are ineffective. The resource-strapped Athletics need to think creatively. In making their case, Beane and Brand return repeatedly to on-base percentage, a statistic that, to that point in time, earned far less attention than others in determining a player’s value. Brand has expertise in analytics and an Ivy League degree. Beane is decisive and brash enough to shake up the status quo. The message is clear: the old way is obsolete.
This article explores sport after Moneyball, meaning sport at a time when Moneyball’s disruptive ideas are widely accepted, and often celebrated. It’s true that the statistical revolution of the early 2000s was years in the making (for example, see Millington & Millington, 2015). But the Athletics’ then-unorthodox approach and Michael Lewis’s telling of their story in the book Moneyball: The Art of Winning an Unfair Game (the source material for the film) helped popularize the idea that advanced statistical analyses can improve sport performance “at the margins,” meaning in slight but still significant ways.
We contend that the rising importance of data and technology in sport over the past two decades reflects the arrival of a wider digital economy. In this context, important questions about the implications of data and technology in sport still need to be considered.
Twin revolutions in sport
The term “sports analytics” is commonly used now as shorthand for rigorous and objective data processes in sport. You’d be hard pressed to find a professional sports team in North America that isn’t informed by sports analytics at least in some way. Famously, Kyle Dubas was hired as General Manager of the Toronto Maple Leafs after bringing an analytical approach to the Ontario Hockey League’s (OHL) Sault Ste. Marie Greyhounds. A story in The Athletic charting Dubas’s rise makes a comparison to Moneyball:
Just as Oakland Athletics GM Billy Beane discovered how analytics could help his team win games in the film (and real life), Dubas believed numbers could be part of the way forward in hockey. Like the Athletics, the Greyhounds were a small-market squad. Dubas felt the organization had to exploit whatever margins they could to challenge behemoths like Windsor, London, and Kitchener for OHL titles.
It’s not just professional sport that is changing. In 2017, the Canadian Olympic Committee (COC) partnered with analytics company SAS Canada, and thus reportedly became “the first National Olympic Committee to form a long-term partnership with an analytics company and to leverage the power of data to give athletes and coaches an extra advantage in maximizing their performance outcomes.” The Canadian Tire Corporation’s “best-in-class data analytics division” has evidently informed Canada’s quest for Olympic and Paralympic success as well.
In education, there are sports analytics groups at Canadian universities. Syracuse University describes its Bachelor of Science degree in Sports Analytics as the first of its kind in America. Researchers are continuously adding to the wealth of technical sports analytics knowledge that might help performance.
In media, sports analytics knowledge is impacting how sports are broadcast and discussed. “Next generation” statistics are now shown during live-game programming. And options abound when it comes to websites, podcasts, social media accounts and news media stories that take an analytical approach in critiquing sport performance.
In the commercial sector, Canadian-based companies are providing novel insight to athletes and teams in an array of sports and at various level of competition. Per their website, Stathletes offers “Professional hockey’s deepest performance data & analytics.” Sportlogiq “helps teams gain an edge and media surface engaging stories with advanced AI [Artificial Intelligence] technology.”
What, exactly, is revolutionary here? With the mainstreaming of sports analytics, there are at least two revolutions at play. One is epistemological, meaning it has to do with how we understand the world around us. In this case, it’s the belief that more rigorous data processes can lead to a better understanding of issues of concern, and potentially to better outcomes. Such an approach might inform player evaluations, contract decisions, health and wellness assessments, on-field strategies, and many other aspects of sport business and operations.
The other revolution is technological. The task of evaluating player or team performance can be helped by an ever-growing suite of hardware and software: motion-tracking cameras that give a bird’s eye view of performance; wearable technology for tracking everything from heart rate to distance travelled and beyond; sensor-embedded smart objects that chart the trajectory of projectiles; apps for tracking lifestyle elements such as diet, sleep, and mood; platforms for visualizing and sharing data on performance; the list goes on. The growing sophistication of technology is helping a shift towards predictive insight on the playing field, to complement reflective insight on what happened in the past.
The upshot of these twin revolutions is what Deloitte, the multinational professional services firm, recently called the “hyperquantified athlete.” It’s clearly a phenomenon in elite-level sport. But it’s relevant to other levels of competition too. If a recreational golfer lands in the bunker, their smartwatch might deliver good news about their swing tempo – a silver lining, despite the poor result. Knowledge that was once confined to the most successful athletes and coaches or to researchers operating out of sport sciences laboratories is now much more widely accessible.
Sport and the digital economy
As researchers studying sport from a social sciences perspective, we have recently embarked on a project focused on sports analytics in Canada. The project is based on the idea that the guiding belief for sports analytics, that rigorous data processes can assist performance, is well-founded. Canadians celebrated gleefully when Canada won soccer gold at the 2020 Olympics in Japan. Some pointed to a progressive approach to data and wearable technology as a factor in the team’s success.
But we also think the above-described twin revolutions in sport have wide-ranging implications that merit further consideration.
There’s a view among many who study sport that sport reflects and contributes to the wider contexts in which it’s situated (for example, see Donnelly, 2008). In this case, the changes we’ve seen in sport are reflective of a broader shift towards a digital economy, meaning the widespread integration of information and communication technologies into organizational activity. It’s not just sports teams and athletes that use data and technology for insight and efficiency gains, it’s organizations of many kinds.
In pre-game preparation or post-game debriefing, a sports team might share game clips, prepared by a video analyst, through performance analysis software. In-game, athletes and coaches might assess individual or team performance on a computer tablet while seated on the sidelines. This is the integration of technology into the workplace. Examples of this kind abound in sport. Such uses of technology in sport are akin to the adoption, in other sectors, of messaging services to complement face-to-face meetings and in-person conversations around the office. In other words, sport reflects and contributes to the trend of integrating data and technology into a wide range of workplaces.
Sports analytics has risen in conjunction with the digital economy. Thinking about sport’s twin revolutions in this way is helpful in thinking about potential implications of sports analytics that go beyond the prospect of improving sport performance. In our own work, we’re interested in exploring several questions, including:
1) How are data and technology impacting job roles and communication in sport?
In one sense, this is a question of knowledge and skills. The film Moneyball seemingly depicts competing job roles: “old” scouts versus “new” analysts. The divide is perhaps exaggerated for dramatic effect. But job roles such as video analyst and performance analyst are now commonplace. A question that follows is, how knowledgeable do employees across a sport organization (for example, coaches, trainers, executives, and athletes) need to be about sports analytics? What accompanying skills do they need? Is there actually a perception of “older” and “newer” ways of understanding sport, and if so, how does this perception impact communication within sport organizations?
In another sense, this is a question of space and time. To be a scout once meant attending games in person. But the technological revolution means game data can be recorded and shared widely with ease. Perhaps sports analytics job roles needn’t be tied to geographic location? If so, this again reflects the wider digital economy, where the ease of outsourcing job tasks has made for an era of flexible work. What does this mean for jobseekers and sport organizations? What are the upsides and downsides of the era of flexible work for sports analytics?
2) How widely accessible is sports analytics knowledge?
Access can mean, in one sense, accessibility for the public. As said above, the sports media landscape is more crowded than ever. For example, hockey fans can turn to podcasts such as The Hockey PDOcast for analytical insight (PDO is a hockey statistic). But analytics knowledge quickly grows complex. Is there a perceived line between demystifying sport through analytics and (inadvertently) making sport harder to comprehend? How does this play out for “traditional” sports journalists compared to those making analytics content through new media channels? The description for The Hockey PDOcast seems to allude to this issue: “There’s an analytical bend to the discussion, but it’s not nearly as nerdy as the title may sound.”
The issue of access also brings questions of equity into play. For example, there is a persistent persistent of under-resourcing and underfunding women’s sports. How does sports analytics factor in? Perhaps women’s sports earn less attention in this area too? Might investment in sports analytics help address gender inequity? There are gender disparities in leadership opportunities in sport as well (for example, see Norman, Donnelly & Kidd, 2021; Evans & Pfister, 2021; Wicker & Kerwin, 2020). Does sports analytics contribute to or counter this trend?
3) Do stakeholders in sport see limits on the use of data and technology in sport, or is “more always better”?
In an oft-cited lecture, social scientist Herbert Simon (1971) upended the idea that more information is necessarily desirable from a management perspective. People consume information, yes, but information consumes attention; people can only handle so much. Constant data collection and analysis in sport might be productive, but can it be overwhelming too? If so, how do athletes, coaches, analysts, executives, and others manage this potential dilemma?
Furthermore, as noted in the aforementioned Deloitte report, the explosion of data in sport raises questions not just on how to use data, but how to do so ethically. Data and technology might help in creating safer and fairer sports environments, for instance, through technology-aided predictive insight identifying when athletes are at risk of injury. But concerns arise if athletes or others in sport see data collection as too far-reaching, unfair, or invasive. Again, research into the matter can help identify how stakeholders in sport are pursuing effective and safe practices in the use of data and technology, and what might be done to go further in this regard.
Sport after Moneyball
The book Moneyball captured a moment of disruption in sport. The film offered an entertaining and dramatized version of events. Decades later, the ideas at the core of Moneyball have found mainstream acceptance.
Our work isn’t alone in exploring implications of data, technology, and analytics in sport (e.g., see Baerg, 2017; Beer, 2015; Hutchins, 2016; Manley & Williams, 2022; Wanless & Naraine, 2021; Watanabe, Shapiro & Drayer, 2021). Yet there are still many contributions to be made, including about the emergence and impacts of sports analytics in Canada.
Sport reflects and contributes to (and yes, at times resists) its wider conditions. The growing use of data and technology in sport is an important case in the arrival of a wider digital economy.
Author’s note
This article draws on research supported by the Social Sciences and Humanities Research Council.