  Difficulty is one of the key drivers of player engagement and it is often one
of the aspects that designers tweak most to optimise the player experience;
operationalising it is, therefore, a crucial task for game development studios.
A common practice consists of creating metrics out of data collected by player
interactions with the content; however, this allows for estimation only after
the content is released and does not consider the characteristics of potential
future players.
  In this article, we present a number of potential solutions for the
estimation of difficulty under such conditions, and we showcase the results of
a comparative study intended to understand which method and which types of data
perform better in different scenarios.
  The results reveal that models trained on a combination of cohort statistics
and simulated data produce the most accurate estimations of difficulty in all
scenarios. Furthermore, among these models, artificial neural networks show the
most consistent results.
