The current models of skill in video games make one of two impositions on players: either to provide an estimate of their own skill, or complete several games before they can be properly assessed. However, in order to experience the most enjoyment and greatest sense of immersion, players need to play against the right difficulty. In order to assign the appropriate difficulty, the player's skill must first be captured accurately and quickly, before the player gets frustrated or bored. Rather than relying on game results that need to be averaged over several games, this thesis proposes predicting a player's skill from their behaviour within the first game. In order to do this, we explore methods for measuring skill in both a multiplayer and single-player game and methods for extracting appropriate information from the player's behaviour. The resulting predictions can then be used to automatically assign an appropriate difficulty to the player. In a multiplayer environment, we first demonstrate that a player's final rank canbe predicted within the first 30 seconds of a game with a correlation of over 0.8.This process is transferred to a single-player first-person shooter, where our modelis shown to assign difficulties comparable to a player's own assessment of theirskill within the first 30 seconds of a campaign. We argue that these methods forcapturing skill in a first-person shooter are transferable to other genres, and havethe potential to improve difficulty selection systems.

This paper aimed to discover how player satisfaction is affected by context-aware elements based on physical, real-world factors in an action role-playing game. The possibility of utilizing player modelling to counteract the reduction in player empowerment in a game heavily influenced by external factors was also explored. This was accomplished by creating an ARPG heavily integrated with various weather, and daytime, context data obtained from web-based APIs. Followed by conducting qualitative tests with help of eleven participants during a time period of a week. The evaluated results were presented in a manner of relevance for both context-driven applications as a whole, and for further exploration within game developments utilization of state of the art technology.