# Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts

arXiv: https://arxiv.org/abs/1909.03343 · HTML: https://mlsanity.com/paper/1909.03343/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2019.

## Built on by (most cited first, 8 of 8)

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- [Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration](https://mlsanity.com/paper/2109.06304/) (2021)
- [SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs](https://mlsanity.com/paper/2402.10979/) (2024)
- [SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain](https://mlsanity.com/paper/2106.01972/) (2021)
- [MCAD: Multimodal Context-Aware Audio Description Generation For Soccer](https://mlsanity.com/paper/2511.09448/) (2025)
- [Racial Bias Trends in the Text of US Legal Opinions](https://mlsanity.com/paper/2307.01693/) (2023)

## Abstract (arXiv)

Sports broadcasters inject drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes. Prior studies based on small datasets and manual coding show that such theatrics evince commentator bias in sports broadcasts. To examine this phenomenon, we assemble FOOTBALL, which contains 1,455 broadcast transcripts from American football games across six decades that are automatically annotated with 250K player mentions and linked with racial metadata. We identify major confounding factors for researchers examining racial bias in FOOTBALL, and perform a computational analysis that supports conclusions from prior social science studies.