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首页> 外文会议>Pacific Asia conference on knowledge discovery and data mining;PAKDD 2010;Workshop on data mining for healthcare management;DMHM 2010;Pacific Asia workshop on intelligence and security informatics;PAISI 2010;Workshop on feature selection in data mining;FSDM 2010;Workshop on behavior informatics;BI 2010;Workshop on datamining and knowledge discover for e-governance;DMEG 2010;Workshop on knowledge discovery for rural systems;KDRS 2010;Workshop on emerging research trends in vehicle health management;VHM 2010 >Player Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs)
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Player Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs)

机译：大型多人在线角色扮演游戏（MMORPG）中的玩家表现预测

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In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players. This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players. The prediction models provide a projection of player's future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools. First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance prediction. Second, we evaluate the effects of varying lengths of past performance and show that past performance can be a good predictor of future performance up to a certain degree. Third, we show that game players do not regress towards the mean and that prediction models built on buckets using discretization based on binning and histograms lead to higher prediction coverage.
机译：在这项研究中，我们提出了一种综合的绩效管理工具，用于衡量和报告游戏玩家的运营活动。这项研究使用Sony Online Entertainment开发的流行MMORPG EverQuest II中游戏玩家的性能数据来为游戏玩家建立性能预测模型。预测模型根据玩家过去的表现提供了其未来表现的预测，预计这将是对现有玩家表现监控工具的有用补充。首先，我们证明了两种最流行的棒球本垒打预测方法PECOTA [2]和MARCEL [3]的变化可用于玩家表现的预测。其次，我们评估了不同长度的过去表现的影响，并表明过去的表现在某种程度上可以很好地预测未来的表现。第三，我们表明游戏玩家不会回归均值，并且使用基于分箱和直方图的离散化在存储桶上构建的预测模型会导致更高的预测覆盖率。

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