Battle of the Bands: Trying to Identify Contributions to a Collaboration

In this article, we create and explore a dataset containing musical and lyrical features for tracks from three rock bands – Manchester Orchestra, The Front Bottoms, and All Get Out – all of whom contributed to a collaborative track, “Allentown.” We use the goal of disentangling the different contrib...

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Bibliographic Details
Published inJournal of statistics and data science education pp. 1 - 27
Main Authors Cipolli, William, Dalzell, Nicole M., Bower, Roy, Evans, Ciaran
Format Journal Article
LanguageEnglish
Published Taylor & Francis Group 14.08.2025
Subjects
data collection
data visualization
logistic regression
multinomial regression
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Summary:In this article, we create and explore a dataset containing musical and lyrical features for tracks from three rock bands – Manchester Orchestra, The Front Bottoms, and All Get Out – all of whom contributed to a collaborative track, “Allentown.” We use the goal of disentangling the different contributions to “Allentown” to motivate learning logistic and multinomial regression. Furthermore, collecting data about what each band sounds like (using Essentia) and what the lyrics of each band read like (using the Bing Lexicon and the Linguistic Inquiry and Word Count software) provide opportunities for students to work with various technologies to collect and clean data, as well as create features from data. These data are approachable to students because of the context, and the overarching results match public comments about the collaboration, providing a real-world connection. We have successfully integrated these data into undergraduate and master’s level courses through classroom activities, assignments, and assessments, which are shared and discussed throughout the manuscript.
ISSN:2693-9169
2693-9169
DOI:10.1080/26939169.2025.2547611