London (IANS) Researchers have developed an algorithm that predicts whether an actor’s career has peaked and also predicts their most successful days in the future, with an accuracy of 85 per cent.
The research team discovered that the most productive years for actors, defined as the year with the largest number of credited jobs, are towards the beginning of their careers.
The study, published in the journal Nature Communications shows how around 70 per cent actors and actresses have careers that only last for one year.
“Our results shed light on the underlying social dynamics taking place in show business and raise questions about the fairness of the system. Our predictive model for actors is also far from the randomness that is displayed for scientists and artists,” study author from the Queen Mary University of London Oliver Williams said.
For the study, the researchers used data of Internet Movie Database (IMDb) and looked at the careers of over 2.4 million actors from around the world from 1888 to 2016 to analyse and predict success on the silver screen.
They found that careers are clustered into ‘hot’ and ‘cold’ streaks, as individuals do not tend to work at a steady rate in a business where unemployment rates hover at around 90 per cent.
There is also huge evidence of gender biases in the industry, as most of the patterns were observed different for actors and actresses, the study said.
According to the outcomes, the total number of jobs in a career is underpinned by the rich-get-richer phenomenon.
What is interesting about this observation is that the rich-get-richer effects are well known to develop out of arbitrary and unpredictable random events that get amplified.
Hence, an actor’s success could be down to their circumstances rather than the acting ability.
“We think the approach and methods developed in this paper could be of interest to the film industry: for example, they could provide complementary data analytics to IMDb. This does also bring with it a number of open questions,” said Lucas Lacasa from the Queen Mary University of London.