ABSTRACT | In this work, I investigate and quantify several aspects of success in different creative fields and professions, covering film, music, literature, and science, relying on large-scale data.
First, building on previous research, I present a novel approach to decompose the success of creative individuals into two orthogonal components, one encompassing all external factors affecting fluctuations in one’s success (luck), and the other encoding the individual's direct contribution to it (skill). Then I use this impact-decomposition method to compare the different fields in terms of how exposed the individuals’ expected success is to unforeseeable events and how robust it is based on the individuals’ previous success.
Second, I analyze the temporal evolution of the collaboration network of movie directors and its relationship to the directors’ success. I point out a significant correlation between these two quantities, and how network analysis can help identify two different categories of film directors: those for whom network peaks first and success follows, and those for whom it works the other way around. I also give an attempt to quantitatively differentiate these intrinsically different behaviors.
Last, I analyze the dynamics of the annual top 100 ranking of the best DJs of the world and relate it to the underlying collaboration patterns in electronic music. I also discuss the importance of mentorship for success and combine these different dimensions to build predictive machine learning models on the upcoming year’s top100, and next years' possible new entries.
BIO | Milán’s PhD thesis is about studying, quantifying, and modeling key components of career success on various artistic fields and comparing them to scientific ones. For this, he collects and analyzes large scale data about motion picture, music and literature. Besides science of success, he is generally interested in data mining, data and network visualization, and social media.