Inferring Player Rating from Performance Data in Massively Multiplayer Online Role-Playing Games (MMORPGs)

Shim, Kyong Jin; Ahmad, Muhammad Aurangzeb; Pathak, Nishith; Srivastava, Jaideep

doi:10.1109/cse.2009.452

Cited by 20 publications

(18 citation statements)

References 15 publications

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“…Given the same amount of time, apprentice-only players and non-participating players attain more XP points. Figure 2 shows a comparison of Combat Success Indexes [3]. In lower players levels, non-participating players tend to achieve lower Combat Success Index compared to those that participate in mentoring.…”

Section: Mentoring and Player Performancementioning

confidence: 94%

“…Figure 1 shows a comparison of Combat Efficiency Indexes [3]. The blue line represents the Combat Efficiency Index of players that do not participate in mentoring at all, while the purple line represents players that both mentor and apprentice.…”

Section: Mentoring and Player Performancementioning

confidence: 99%

“…This study analyzes the mentoring network in EverQuest II [4] and examines the effects of mentoring on player performance [3]. The mentor system acts as a channel for both the mentor and the apprentice to interact with each other.…”

Section: Introductionmentioning

confidence: 99%

“…Recent years have seen an explosive growth in digital game sales including MMORPGs. As people spend more time in virtual environments [1], researchers have recently taken notice that virtual environments such as EverQuest II serve as a major mechanism for socialization [2], [3].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Mentoring on Player Performance in Massively Multiplayer Online Role-Playing Games (MMORPGs)

Shim

Hsu

Srivastava

2011

2011 International Conference on Advances in Social Networks Analysis and Mining

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Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and have communities comprising millions of subscribers. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. In this study, we examine and report our findings on the effects of mentoring activities on player performance in EverQuest II, a popular MMORPG developed by Sony Online Entertainment.

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Section: Mentoring and Player Performancementioning

confidence: 94%

Section: Mentoring and Player Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effects of Mentoring on Player Performance in Massively Multiplayer Online Role-Playing Games (MMORPGs)

Shim

Hsu

Srivastava

2011

2011 International Conference on Advances in Social Networks Analysis and Mining

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“…Also, novice players can team up with more advanced players to get familiarized with the game via the game's mentorship system. [11] reports that as the player level increases, combat team formation becomes a more common occurrence and that playing in groups leads to higher success ratio (lower death ratio) for the participating individual players.…”

Section: Impact Of Group Formation On Performancementioning

confidence: 97%

Behavioral profiles of character types in EverQuest II

Shim

Srivastava

2010

Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games

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This study examines behavioral profiles of different character types in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. The study uses the game's player activity data to construct behavioral profiles of online game players for the applications of normal behavior recognition and anomaly detection. The behavioral profiles give insight into not only what players do in the game to level up but also how they perform on different types of task and how they work with other players. The behavioral profiles this study reports provide insight into online game player behavior, and it provides information useful for choosing a character type suitable for one's objectives in playing the game. In this study, we develop a framework for automatic behavior profiling. The proposed framework consists of the following key components; 1) segmentation analysis of historical player behaviors, 2) behavior profiling of input users (new players whose behaviors we want to label, and 3) recommendation of tasks based on input users' objectives.

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