Sports broadcasting can reflect and influence fans and baseball culture. This study sought to analyze broadcaster language in baseball across three groups (country of birth, race/ethnicity, and players of color/white) and identify trends across the sample. Drawing on past research, this study employed a point-in-time content analysis of MLB broadcasts from 2023 to 2025 to determine whether announcers displayed bias. Using Artificial Intelligence-generated code, the content analysis was conducted on 45 games over three years across 16 different descriptor categories into which the announcer's language fell. Using Chi-square tests, the study found evidence that announcers talk about players from different countries, different races/ethnicities, and different skin colors differently. Some findings included Asian players being discussed in terms of experience more often than Black players, Latino players being discussed in terms of style of play more often than white players, and white players being discussed in terms of talent-based athletic skill more often than players of color. This paper also applied framing theory and poo pools lcultivation theory as media concepts that contextualize the statistically significant results.
Amrit Brown (Mon,) studied this question.