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Abstract

Student-athletes are often treated differently and inequitably based on their race/ethnicity. For example, Black student-athletes often face unjust stereotyping and discrimination because they are seen for their athletic achievements over their academic achievements compared to White student-athletes. Using in-game broadcast data, Black athletes are also discussed differently than White athletes, with disparities in how broadcasters focus on players’ physical and cognitive abilities. The current paper draws on and extends this literature by using natural language processing techniques to identify how experts communicate about different National Football League (NFL) prospects. A decade of data (2014-2023), including nearly 4,000 draft profiles, suggested writers attending to Black draftees used fewer positive emotions in player narratives than writers attending to White draftees. Narratives of White players also mentioned the NFL more than narratives of Black players, revealing a possible disparity in perceived belonging to the league. Implications for theory and equity in sport are discussed.

Keywords

bias natural language processing sport American football National football league

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“All the Makings of an Eventual NFL Starter:” Racial/Ethnic Disparities in a Decade of National Football League Draft Profiles as Revealed by Natural Language Processing

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