Quantifying and predicting success in show business

Oliver E. Williams (), Lucas Lacasa () and Vito Latora ()
Additional contact information
Oliver E. Williams: Queen Mary University of London
Lucas Lacasa: Queen Mary University of London
Vito Latora: Queen Mary University of London

Nature Communications, 2019, vol. 10, issue 1, 1-8

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-10213-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10213-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-10213-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().