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Rapid Skill Capture in a First-Person Shooter
ABSTRACT
Various aspects of computer game design, including adaptive elements of game levels, characteristics of 'bot'
behavior, and player matching in multiplayer games, would ideally be sensitive to a player's skill level. Yet, while
game difficulty and player learning have been explored in the context of games, there has been little work analyzing
skill per se, and how this is related to the interaction of a player with the controls of the game - the player's input. To
this end, we present a data set of 476 game logs from over 40 players of a first-person shooter game (Red Eclipse)
as a basis of a case study. We then extract features from the keyboard and mouse input and provide an analysis in
relation to skill. Finally, we show that a player's skill can be predicted using less than a minute of their keyboard
presses. We suggest that the techniques used here are useful for adapting games to match players' skill levels rapidly,
arguably more rapidly than solutions based on performance averaging such as TrueSkill.
Click
tciaig2017.pdf
for full text and more results/details are available on Project Website. Also the data set collected in this project is available
from Keyboard and Mouse Data from a First-Person Shooter: Red Eclipse.