Online gaming has now become an extremely com- petitive business. As there are so many game titles released every month, gamers have become more difficult to please and fickle in affection. Therefore, it would be beneficial if we can forecast how addictive a game is before publishing it on the market. The capability of game addictiveness forecasting will enable developers to continuously adjust the game design and enable publishers to assess the potential market value in a game’s early development stages.
In this paper, we propose to forecast a game’s addictiveness based on players’ emotion when they are exploring the game. Based on the account activity traces of 11 commercial online games, we develop a forecasting model that takes electromyo- graphic measures of players as the input and outputs the addic- tiveness index of a game. We hope that with our methodology, the game industry could save hopeless investment and target more accurately to provide more entertaining experience.