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.
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Data availability
Our dataset, called Music-oriented Hot Streak Information Collection (MUHSIC), is publicly available in Zenodo (Oliveira et al., 2021a). This dataset has been presented previously in Oliveira et al. (2021b, 2022). Such works focus on the building process of the dataset and its characterization only (i.e., they are not related with Hot Streaks). In addition, Barbosa et al. (2021) and Seufitelli et al. (2022) use MUHSIC-BR, another dataset built with the same methodology but with data from the Brazilian music market, which is not addressed in this paper.
Notes
International Olympic Committee: https://www.olympic.org/athletes.
IFPI Global Music Report 2021: https://gmr.ifpi.org/.
Billboard: https://bit.ly/3hfzMJ0.
tslearn: https://github.com/tslearn-team/tslearn/.
Recording Academy Grammy Awards: https://www.grammy.com/.
Greatest of All Time: https://www.billboard.com/charts/greatest-of-all-time-artists.
References
Aggarwal, C. C. (2016). Recommender systems - the textbook. Springer.
Andrade, N., & de Figueiredo, F. V. D. (2016). Exploring the latent structure of collaborations in music recordings: A case study in jazz. International society for music information retrieval conference (pp. 633–639). ISMIR.
Avugos, S., Köppen, J., Czienskowski, U., Raab, M., & Bar-Eli, M. (2013). The “hot hand” reconsidered: A meta-analytic approach. Psychology of sport and exercise, 14(1), 21–27. https://doi.org/10.1016/j.psychsport.2012.07.005
Bar-Eli, M., Avugos, S., & Raab, M. (2006). Twenty years of “hot hand” research: Review and critique. Psychology of sport and lxercise, 7(6), 525–553. https://doi.org/10.1016/j.psychsport.2006.03.001
Barbosa, G. R. G., Melo, B. C., Oliveira, G. P., Silva, M. O., Seufitelli, D. B., & Moro, M. M. (2021). Hot streaks in the Brazilian music market: A comparison between physical and digital eras. SBC. https://doi.org/10.5753/sbcm.2021.19440
Garimella, K., & West, R. (2019). Hot streaks on social media. In International conference on web and social media, pp. 170–180.
Georges, P., & Nguyen, N. (2019). Visualizing music similarity: Clustering and mapping 500 classical music composers. Scientometrics, 120(3), 975–1003. https://doi.org/10.1007/s11192-019-03166-0
Georges, P., & Seckin, A. (2022). Music information visualization and classical composers discovery: An application of network graphs, multidimensional scaling, and support vector machines. Scientometrics, 127(5), 2277–2311. https://doi.org/10.1007/s11192-022-04331-8
Heiman, G. W. (2001). Understanding research methods and statistics: An integrated introduction for psychology. Houghton.
Hendricks, D., Patel, J., & Zeckhauser, R. (1993). Hot hands in mutual funds: Short-run persistence of relative performance, 1974–1988. The Journal of Finance, 48(1), 93–130.
Janosov, M., Battiston, F., & Sinatra, R. (2020). Success and luck in creative careers. EPJ Data Science, 9(1), 9. https://doi.org/10.1140/epjds/s13688-020-00227-w
Keogh, E. J., & Pazzani, M. J. (2000). Scaling up dynamic time warping for datamining applications. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/347090.347153
Liu, L., Wang, Y., Sinatra, R., Giles, C. L., Song, C., & Wang, D. (2018). Hot streaks in artistic, cultural, and scientific careers. Nature, 559(7714), 396–399. https://doi.org/10.1038/s41586-018-0315-8
Monroy, S. E., & Diaz, H. (2018). Time series-based bibliometric analysis of the dynamics of scientific production. Scientometrics, 115, 1139–1159. https://doi.org/10.1007/s11192-018-2728-4
Oliveira, G.P. (2021). Analyses of musical success based on time, genre and collaboration. Master’s thesis, Universidade Federal de Minas Gerais, Brazil.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., Lacerda, A., & Moro, M. M. (2021a). MUHSIC: Music-oriented Hot Streak Information Collection [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.4779003
Oliveira, G.P., Barbosa, G.R.G., Melo, B.C., Silva, M.O., Seufitelli, D.B., & Moro, M.M. (2021b). MUHSIC: An open dataset with temporal musical success information. In Proceedings of the 3rd Dataset Showcase Workshop, Porto Alegre, Brazil, pp. 65–76. https://doi.org/10.5753/dsw.2021.17415.
Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., & Moro, M. M. (2020). Detecting collaboration profiles in success-based music genre networks. International Society for Music Information Retrieval Conference (pp. 726–732). ISMIR.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Botelho, J. E., Silva, M. O., Seufitelli, D. B., & Moro, M. M. (2022). Musical success in the United States and Brazil: Novel datasets and temporal analyses. Journal of Information and Data Management, 13(1), 111–126. https://doi.org/10.5753/jidm.2022.2350
Rabin, M., & Vayanos, D. (2010). The gambler’s and hot-hand fallacies: Theory and applications. The Review of Economic Studies, 77(2), 730–778. https://doi.org/10.2139/ssrn.954636
Seufitelli, D. B., Oliveira, G. P., Silva, M. O., Barbosa, G. R. G., Melo, B. C., Botelho, J. E., Melo-Gomes, L., & Moro, M. M. (2022). From compact discs to streaming: A comparison of eras within the Brazilian market. Vortex Music Journal, 10(1), 1–28. https://doi.org/10.33871/23179937.2022.10.1.2
Silva, M.O., Rocha, L.M., & Moro, M.M. (2019). Collaboration Profiles and Their Impact on Musical Success. In Procs. of ACM/SIGAPP SAC, Limassol. pp. 2070–2077. https://doi.org/10.1145/3297280.3297483.
Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A. L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239. https://doi.org/10.1126/science.aaf5239
Tavenard, R., Faouzi, J., Vandewiele, G., Divo, F., Androz, G., Holtz, C., Payne, M., Yurchak, R., Rußwurm, M., Kolar, K., & Woods, E. (2020). Tslearn, a machine learning toolkit for time series data. Journal of Machine Learning Research, 21, 118:1-118:6.
Tukey, J. W. (1949). Comparing individual means in the analysis of variance. Biometrics, 5(2), 99–114. https://doi.org/10.2307/3001913
Yair, G., & Goldstein, K. (2020). The annus mirabilis paper: Years of peak productivity in scientific careers. Scientometrics, 124(2), 887–902. https://doi.org/10.1007/s11192-020-03544-z
Acknowledgements
This article results from an academic master’s thesis finished in 2021 in the Graduate Program in Computer Science of the Federal University of Minas Gerais (Oliveira, 2021).
Funding
This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil.
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All authors contributed to the study's conception and design. Material preparation and data collection were performed by Gabriel P. Oliveira. Analyses were performed by all authors. The first draft of the manuscript was mostly written by Gabriel P. Oliveira, Mariana O. Silva, and Danilo B. Seufitelli. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. The overall project was managed by Mirella M. Moro.
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Oliveira, G.P., Silva, M.O., Seufitelli, D.B. et al. Hot streaks in the music industry: identifying and characterizing above-average success periods in artists’ careers. Scientometrics 128, 6029–6046 (2023). https://doi.org/10.1007/s11192-023-04835-x
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DOI: https://doi.org/10.1007/s11192-023-04835-x