When players quit (playing scrabble)

Abstract

What features contribute to player enjoyment and player retention has been a popular research topic in video games research; however, the question of what causes players to quit a game has received little attention by comparison. In this paper, we examine 5 quantitative features of the game Scrabblesque in order to determine what behaviors are predictors of a player prematurely ending a game session. We identified a feature transformation that notably improves prediction accuracy. We used a naive Bayes model to determine that there are several transformed feature sequences that are accurate predictors of players terminating game sessions before the end of the game.We also identify several trends that exist in these sequences to give a more general idea as to what behaviors are characteristic early indicators of players quitting. Copyright © 2012, Association for the Advancement of Artificial Intelligence. Copyright © 2012, Association for the Advancement of Artificial Intelligence.