Honorable Mention
Data analysis is central to sports training. Today, cutting-edge digital technologies are deployed to measure and improve athletes' performance. But too often researchers focus on the technology collecting performance data at the expense of understanding athletes' experiences with data. This is particularly the case in the understudied context of collegiate athletics, where competition is fierce, tools for data analysis abound, and the institution actively manages athletes' lives. By investigating how student-athletes analyze their performance data and are analyzed in turn, we can better understand the individual and institutional factors that make data literacy practices in athletics meaningful and productive—or not. Our pilot interview study of student-athletes at one Division I university reveals a set of opportunities for student-athletes to engage with and learn from data analytics practices. These opportunities come with a set of contextual tensions that should inform the design of new technologies for collegiate sports settings.
https://doi.org/10.1145/3313831.3376153
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)