{"id":32158,"date":"2023-04-23T06:00:00","date_gmt":"2023-04-23T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=32158"},"modified":"2023-04-24T08:49:08","modified_gmt":"2023-04-24T15:49:08","slug":"16-year-old-data-scientist-creates-innovative-app-to-champion-gender-equality-in-sports-media-coverage-of-ncaa-womens-basketball","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/04\/23\/16-year-old-data-scientist-creates-innovative-app-to-champion-gender-equality-in-sports-media-coverage-of-ncaa-womens-basketball\/","title":{"rendered":"16-Year-Old Data Scientist Creates R Shiny App to Champion Gender Equality in Sports Media Coverage of NCAA Women\u2019s Basketball"},"content":{"rendered":"\n

Nathaniel Yellin, a 16-year-old student, has concluded a new study that reveals the significant gender bias in the sports media coverage of female athletes and, in particular, college basketball players. Yellin has pursued his passions for sports, data science and inspiring change through the creation of an organization and interactive R Shiny application SIDELINED<\/a> (best viewed on a laptop\/desktop). Using Natural Language Processing (NLP) techniques, Yellin scrutinized more than 1,700 ESPN articles from ESPN.com to expose the discrepancies in media coverage of male and female Division 1 college basketball athletes.<\/p>\n\n\n\n

SIDELINED is innovative and impressive. The interactive R Shiny application Yellin created invites users to interact with data visualizations and view word clouds to evaluate for themselves the extent to which gender bias exists at the league level in the sports media coverage of D1 basketball. Even more fascinating is the fact that users can also search in SIDELINED for specific teams and players to assess what gender bias exists in sports media reporting at these particular levels as well.<\/p>\n\n\n\n

The findings of SIDELINED are significant, demonstrating that a bias exists based on the quantity of articles published for male and female athletes, the sentiment of adjectives used to describe players of both genders, and how media coverage correlates differently with player performance based on gender. Female players are found to have received significantly less coverage overall. In fact, being mentioned in even one article is considered to be an outlier for most female athletes. Through advanced data science sentiment analysis, Yellin found that players who play center in men\u2019s basketball are described in articles with overwhelmingly positive sentiment while female athletes who play this position are described less positively. Yellin also discovered that male players who play and score more receive more media attention, but this relationship is much less clear for female athletes.<\/p>\n\n\n\n

\n

\u201cThis study highlights the ongoing issue of gender bias in sports media coverage,\u201d said Yellin. \u201cFemale basketball players are just as talented and accomplished as their male counterparts, but they are often overlooked or dismissed in media coverage. This not only perpetuates harmful gender stereotypes, but it also limits the visibility and recognition of female athletes. It also affects their performance and unfairly the income they receive as NIL athletes and ultimately, professional basketball players.\u201d<\/p>\n<\/blockquote>\n\n\n\n

Through SIDELINED, Yellin hopes his study will raise awareness of the gender bias that exists in sports media reporting and encourage the NCAA and media outlets to take steps toward more equitable and inclusive coverage of all athletes, regardless of gender.<\/p>\n\n\n\n

Yellin is a high school junior at the Leffell School in Hartsdale, New York who loves to share his passions with others and make a difference in the world around him. He founded and writes for his NFL sports analytics blog, Field General Analytics<\/a>; teaches math to children through the lens of sports and art in his Math4Kicks<\/a> program (IG:math4kicks<\/a>); and also hosts a podcast, It\u2019s Statistically Significant<\/a>, where he discusses statistics concepts to educate students. When Yellin isn\u2019t teaching, writing about or analyzing sports or statistics, he can be found playing baseball or pickleball with friends and his four siblings.<\/p>\n\n\n\n

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Nathaniel Yellin, a 16-year-old student, has concluded a new study that reveals the significant gender bias in the sports media coverage of female athletes and, in particular, college basketball players. Yellin has pursued his passions for sports, data science and inspiring change through the creation of an organization and interactive R Shiny application\u00a0SIDELINED.<\/p>\n","protected":false},"author":10513,"featured_media":22510,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[182,87,180,56,1],"tags":[743,133,635,909,1284,96],"acf":[],"yoast_head":"\n16-Year-Old Data Scientist Creates R Shiny App to Champion Gender Equality in Sports Media Coverage of NCAA Women\u2019s Basketball - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/04\/23\/16-year-old-data-scientist-creates-innovative-app-to-champion-gender-equality-in-sports-media-coverage-of-ncaa-womens-basketball\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"16-Year-Old Data Scientist Creates R Shiny App to Champion Gender Equality in Sports Media Coverage of NCAA Women\u2019s Basketball - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Nathaniel Yellin, a 16-year-old student, has concluded a new study that reveals the significant gender bias in the sports media coverage of female athletes and, in particular, college basketball players. 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