# Quantifying and predicting success in show business

## TL;DR

This study analyzes the success patterns of actors in show business, revealing predictable productivity trends, a rich-get-richer mechanism, and gender biases, and develops a machine learning model to forecast peak career years.

## Contribution

It uncovers distinctive, predictable features of actor productivity, introduces a machine learning method to predict career peaks, and highlights gender disparities in show business.

## Key findings

- Two-thirds of actors are one-hit wonders.
- Productivity follows a Zipf law and peaks early in careers.
- The machine learning model predicts career peaks with 85% accuracy.

## Abstract

Recent studies in the science of success have shown that the highest-impact works of scientists or artists happen randomly and uniformly over the individual's career. Yet in certain artistic endeavours, such as acting in films and TV, having a job is perhaps the most important achievement: success is simply making a living. By analysing a large online database of information related to films and television we are able to study the success of those working in the entertainment industry. We first support our initial claim, finding that two in three actors are "one-hit wonders". In addition we find that, in agreement with previous works, activity is clustered in hot streaks, and the percentage of careers where individuals are active is unpredictable. However, we also discover that productivity in show business has a range of distinctive features, which are predictable. We unveil the presence of a rich-get-richer mechanism underlying the assignment of jobs, with a Zipf law emerging for total productivity. We find that productivity tends to be highest at the beginning of a career and that the location of the "annus mirabilis" -- the most productive year of an actor -- can indeed be predicted. Based on these stylized signatures we then develop a machine learning method which predicts, with up to 85% accuracy, whether the annus mirabilis of an actor has yet passed or if better days are still to come. Finally, our analysis is performed on both actors and actresses separately, and we reveal measurable and statistically significant differences between these two groups across different metrics, thereby providing compelling evidence of gender bias in show business.