Data Every Day: Athletes' Data Science Engagement in Collegiate Sports Contexts

• In Event: #TheMoreYouKnow: Critical Data Practices With New Digital Media and Technologies Toward Justice

Abstract

Introduction, Perspectives and Background
Data science plays a key role in guiding athlete training and competition. The science gets more advanced at collegiate and professional levels where athletics is a high-revenue industry and minor advances help teams gain a competitive edge. Data cleaning, collection, analysis, interpretation, and data-driven decision making - all critical data literacy skills (Maybee & Zilinski, 2015; Prado & Marzal, 2013) - fuel the intense science in sports. Although analytics are a critical aspect of sports, the data literacy skills athletes develop and use are often unrecognized and underdeveloped (Weight & Huml, 2016).

In this presentation, we are investigating the community-driven data science happening authentically in elite athletics as a means of engaging a community of learners - collegiate athletes, many of whom come from underrepresented groups - in STEM (Comeaux, 2018). We aim to recognize the data literacy practices inherent in sports play and to explore the potential of leveraging data science as a means to address systemic racial, equity, and justice issues inherent in sports institutions.

Methods and Context
We situate our work in NCAA Division 1 Power 5 elite athletics, where extensive budgets fund some of the most robust sports analytics. Complex socio-political dynamics present the need for athlete empowerment. Collegiate athletes face a dynamic struggle between their athletic and academic commitments (Weight, Cooper, & Popp, 2015). This athletic and academic divide has been labeled a Civil Rights issue because of its disproportionate effects on young Black men in particular (Comeaux, 2018) who make up 2.4% of full-time undergraduate students but 55% of the football and 56% of men’s basketball players within six major NCAA Division I sports conferences (Comeaux, 2018; Harper, 2018). Simultaneously, these issues present an opportunity for athletes to explore technical, social, and political aspects of data science in a context in which they are deeply intertwined. We leverage Nasir & Hand’s (2008) practice-linked identity theory as a lens to analyze interviews with 23 athletes and 27 athletics staff at two NCAA Division 1 Power 5 universities spanning 13 different sports. We specifically focus on tensions and alignments athletes face as they engage in data science and the ways they welcome, adapt, resist, and critique such engagements.

Findings and Significance
Our findings indicate ways in which athletes (1) readily accept data practices espoused by their coaches and sport and (2) ways they critique and intentionally dis-engage from such practices. Often athletes’ responses are mediated by their access to data practices within their sport and their own future aspirations. Additionally, we found that athletes’ personally initiated data practices served as a means to take steps towards disrupting power dynamics and dominant narratives that can disempower athletes in sports. However, such steps could be limited by the organizational structures inherent in athletics and athletes’ limited access to and understanding of their data. Our findings point to the need for intentional formal and informal education around data science in athletics to promote collegiate athletes’ well-being, empowerment, and academic pursuits in division 1 sports settings.

Authors

• Tamara L. Clegg, University of Maryland

• Keaunna Cleaveland, University of Maryland

• Niklas Elmqvist, University of Maryland

• Daniel Greene

• Erianne Weight