  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
difficulty and player learning have been explored in the context of games,
there has been little work analyzing skill per se, and how it pertains to a
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 analyze different metrics of skill and show that some of these
can be predicted using only a few seconds of keyboard and mouse input. We argue
that the techniques used here are useful for adapting games to match players'
skill levels rapidly, perhaps more rapidly than solutions based on performance
averaging such as TrueSkill.
