[Paper] Data Everyday: Data Literacy Practices in a Division I Sports Context

1278 words papers,

CHI 2020 was supposed to be happening right now, but it was regrettably cancelled due to coronavirus. All of the papers were put into the ACM Digital Library as scheduled, though, so I started looking at them. This paper studies data literacy among student-athletes at the University of Maryland.

Authors: Tamara Clegg, Daniel M. Greene, Nate Beard, Jasmine Brunson

Link: ACM Digital Library, from CHI 2020


Sports make heavy use of data analytics; the degree to which this happens differs by level and sport, but fields like sabermetrics are increasingly well-known. At the Division 1 (highest collegiate) level, sports like football and basketball typically have outsized attention when compared to sports like track or golf. The authors of this work discuss a gap in the field of sports analytics where the experiences of the people being analyzed—the athletes themselves—are often ignored.

Too often, sports analytics researchers focus on the technology and its impact on performance at the expense of understanding athletes’ experiences with data. This is particularly the case in the largely understudied context of intercollegiate athletics, where competition is fierce, tools for data analysis are ubiquitous, and the institution actively manages athletes’ personal and academic lives. By investigating how student-athletes analyze their data and are analyzed in turn, we can better understand the individual and institutional factors that make data literacy in athletics meaningful and productive—or not.

The authors draw upon information science research to define data literacy thusly:

(1) having an awareness of what data are and their role in society; (2) understanding how to find and obtain data (e.g., being aware of types and selecting the most relevant); (3) reading, interpreting, and evaluating data; (4) managing data (e.g., being aware of the need to save or archive data and understanding how to use tools to do so); (5) preparing data for analysis, synthesizing, and analyzing for specific questions; (6) ethically using data and acknowledging data sources.

This by itself is interesting, as I read a lot about data literacy but rarely see such an explicit and thorough definition. The authors’ goal is “to see how student-athletes are exposed to and engaged with data.”

Tensions uncovered

The authors uncover several tensions with respect to data literacy in college athletics:

  1. Coaching staff gathers both individual and team-level data, but student-athletes are often most concerned with the former. The authors hypothesize that this may be because “athletes were more drawn to data they perceived to be directly relevant to their individual efforts.” Players would sometimes filter for information relevant to them e.g., by only watching film that impacts their position.

  2. The agency that student-athletes had was inversely proportional to the resources their team had. Football or basketball players had less freedom “to choose what to measure, how, and why.” This isn’t surprising: the more money there is in a sport, the more the institution will take control of it.

  3. While student-athletes were most interested in personal data, they often needed help analyzing it. They also mentioned how they would use personal data to compare themselves to teammates or pro players, but that data often stripped away context (like years of experience or age differences).

  4. Student-athletes discussed needing to step back from their data when they felt they were getting too bogged down in it, e.g., in the days approaching a competition or when they felt it was negatively impacting their health (through calorie counting in particular).

  5. A “family atmosphere” that many student-athletes describe can be disrupted by data sharing and, notably, through data comparisons with teammates. Because student-athletes on the same team are competing with each other (in individual sports, in the same event; in team sports, for a starting spot), they were hesitant to discuss individual data with each other.

Supporting data literacy

The authors’ findings show that student-athletes are quite engaged with some parts of data literacy, like being aware of data collection and understanding how it can be useful. Other parts, like how workout plans are created or how the data is stored, are shielded from them.

While concealing of data literacy practices is likely practical for very busy individuals and teams, there is room to consider how to create opportunities for life-relevant learning for athletes …, particularly given the call for athletics programs to consider learning and development opportunities for players through their sports play.

One idea they propose is lift cards to include more information on how practice plans correspond to overall performance. Another is to explore visualizations that make more clear the link between individual performance and team performance.

Finally, the authors discuss how the organizational structure of college athletics departments influence these data literacy practices. “Effective data systems will change the information flow within the organization, thus adjusting inter-role relations,” they write, noting data asymmetries between what coaches knew and what students knew, or that athletes were sometimes more willing to share data with the public than with teammates. Additionally, these asymmetries persist “in part because of power differentials built into campus athletics. Coaches are adult authority figures employed by the university for the long term. Players are younger, uncompensated, and there for a short period.” Designers must be aware of this.

On differences across sports

I appreciate that the authors studied and drew conclusions about multiple sports. Too often are collegiate sports focused on football and men’s basketball, which are largely the ones that draw money to universities (with some notable exceptions, like UConn women’s basketball). This study interviewed athletes from several different sports.

With that said, it’s important to recognize that there is a culture difference between so-called “revenue sports” and the others, and the authors indeed do so. They call out the differences in technology (hopping on a scale vs. using a full-body scanner), coaching (filling out weight measurements individually vs. having strength coaches do it), and equipment.

Competing perspectives

A human-centric perspective really is out of the ordinary here. There are many competing goals in college athletics, including allowing student-athletes to compete in different sports, preparing them for professional play, promoting friendly competition, fostering a sense of community, and (yes) making money for the university.

One might claim that the goal of sports analytics is to help the teams perform better, and I would be inclined to agree. But such a perspective ignores the student-athletes at the center of it all. Indeed, this is one of the major criticisms of schools not allowing players to be paid—that it ignores the fact that there are college students generating money and exposure for their universities.

Adopting a human-centric perspective also mandates that researchers be more intentional about the design changes and improvements they propose, and it requires working with the athletes themselves. This raises important questions about the goal(s) of analytics, and when analytics can be overbearing:

While players saw the value of studying their performance data, across sports, student-athletes talked about times when they specifically needed to step away from their data so that they would not, in Seth’s words, “get in their head” about it or overthink it. The flood of available data could be a distraction from their sports play. In fact, even though golf coaches expected them to be intimately familiar with their numbers at practice, when it came to game time, David said, “They just want you to play. Just play. Don’t get bogged down in all the numbers and details. … That’s why we practice so we don’t have to think anymore.”

Only by working closely with the student-athletes and coaches can one propose effective improvements to data literacy and analytic practices. The authors do a great job at this, and if anything this research shows how infrequently student-athletes’ voices are heard.