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Hot streaks in the music industry: identifying and characterizing above-average success periods in artists’ careers

Author

Listed:
  • Gabriel P. Oliveira

    (Universidade Federal de Minas Gerais)

  • Mariana O. Silva

    (Universidade Federal de Minas Gerais)

  • Danilo B. Seufitelli

    (Universidade Federal de Minas Gerais)

  • Gabriel R. G. Barbosa

    (Universidade Federal de Minas Gerais)

  • Bruna C. Melo

    (Universidade Federal de Minas Gerais)

  • Mirella M. Moro

    (Universidade Federal de Minas Gerais)

Abstract

In this work, we reveal fundamental patterns that appear in individual musical careers. Such careers may go through ups and downs depending on the current market moment and release of new songs. In particular, they face hot streak periods in which high-impact bursts occur in sequence. Identifying such periods and even predicting them may help in other practical issues, which include foreseeing success and recommending artists. After modeling artists’ careers as time series, we find a general trend of clustering within the most successful weeks, which justifies the applicability of the concept of hot streaks. Hence, we use a specific methodology for identifying hot streaks, whose evaluation results reveal meaningful patterns for artists of different genres. We also confirm the career peaks of artists appear and disappear progressively over time. Overall, our findings shed light on the science of musical success as we observe the temporal evolution of artists’ careers and their hot streaks.

Suggested Citation

  • Gabriel P. Oliveira & Mariana O. Silva & Danilo B. Seufitelli & Gabriel R. G. Barbosa & Bruna C. Melo & Mirella M. Moro, 2023. "Hot streaks in the music industry: identifying and characterizing above-average success periods in artists’ careers," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(11), pages 6029-6046, November.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:11:d:10.1007_s11192-023-04835-x
    DOI: 10.1007/s11192-023-04835-x
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    References listed on IDEAS

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    1. Patrick Georges & Ngoc Nguyen, 2019. "Visualizing music similarity: clustering and mapping 500 classical music composers," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 975-1003, September.
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