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Quantifying and predicting success in show business

Author

Listed:
  • Oliver E. Williams

    (Queen Mary University of London)

  • Lucas Lacasa

    (Queen Mary University of London)

  • Vito Latora

    (Queen Mary University of London
    The British Library
    Università di Catania and INFN
    Complexity Science Hub Vienna (CSHV))

Abstract

In certain artistic endeavours—such as acting in films and TV, where unemployment rates hover at around 90%—sustained productivity (simply making a living) is probably a better proxy for quantifying success than high impact. Drawing on a worldwide database, here we study the temporal profiles of activity of actors and actresses. We show that the dynamics of job assignment is well described by a “rich-get-richer” mechanism and we find that, while the percentage of a career spent active is unpredictable, such activity is clustered. Moreover, productivity tends to be higher towards the beginning of a career and there are signals preceding the most productive year. Accordingly, we propose a machine learning method which predicts with 85% accuracy whether this “annus mirabilis” has passed, or if better days are still to come. We analyse actors and actresses separately, also providing compelling evidence of gender bias in show business.

Suggested Citation

  • Oliver E. Williams & Lucas Lacasa & Vito Latora, 2019. "Quantifying and predicting success in show business," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10213-0
    DOI: 10.1038/s41467-019-10213-0
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    Cited by:

    1. Giovanni Colavizza, 2022. "Seller-buyer networks in NFT art are driven by preferential ties," Papers 2210.04339, arXiv.org, revised Nov 2022.
    2. Zappalà, Chiara & Biondo, Alessio Emanuele & Pluchino, Alessandro & Rapisarda, Andrea, 2023. "The paradox of talent: How chance affects success in tennis tournaments," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Xiaomei Bai & Fuli Zhang & Jinzhou Li & Zhong Xu & Zeeshan Patoli & Ivan Lee, 2021. "Quantifying scientific collaboration impact by exploiting collaboration-citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7993-8008, September.
    4. Sándor Juhász & Gergő Tóth & Balázs Lengyel, 2020. "Brokering the core and the periphery: Creative success and collaboration networks in the film industry," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-15, February.

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