Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs)

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

This paper examines online player performance in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. The study uses the game's player performance data to devise performance metrics for online players. We report three major findings. First, we show that the game's point-scaling system overestimates performances of lower level players and underestimates performances of higher level players. We present a novel pointscaling system based on the game's player performance data that addresses the underestimation and overestimation problems. Second, we present a highly accurate predictive model for player performance as a function of past behavior. Third, we show that playing in groups impacts individual performance and that player-level characteristics alone are insufficient in explaining an individual's performance, which calls for a different set of performance metrics methods.

Original languageEnglish
Title of host publicationProceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009
Pages1199-1204
Number of pages6
Volume4
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Social Computing, SocialCom 2009 - Vancouver, BC
Duration: 29 Aug 200931 Aug 2009

Other

Other2009 IEEE International Conference on Social Computing, SocialCom 2009
CityVancouver, BC
Period29/8/0931/8/09

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

Cite this

Shim, K. J., Ahmad, M. A., Pathak, N., & Srivastava, J. (2009). Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs). In Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009 (Vol. 4, pp. 1199-1204). [5283063] https://doi.org/10.1109/CSE.2009.452

Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs). / Shim, Kyong Jin; Ahmad, Muhammad Aurangzeb; Pathak, Nishith; Srivastava, Jaideep.

Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009. Vol. 4 2009. p. 1199-1204 5283063.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shim, KJ, Ahmad, MA, Pathak, N & Srivastava, J 2009, Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs). in Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009. vol. 4, 5283063, pp. 1199-1204, 2009 IEEE International Conference on Social Computing, SocialCom 2009, Vancouver, BC, 29/8/09. https://doi.org/10.1109/CSE.2009.452
Shim KJ, Ahmad MA, Pathak N, Srivastava J. Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs). In Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009. Vol. 4. 2009. p. 1199-1204. 5283063 https://doi.org/10.1109/CSE.2009.452
Shim, Kyong Jin ; Ahmad, Muhammad Aurangzeb ; Pathak, Nishith ; Srivastava, Jaideep. / Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs). Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009. Vol. 4 2009. pp. 1199-1204
@inproceedings{e3c7b7c490f94189a92b157f599b0424,
title = "Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs)",
abstract = "This paper examines online player performance in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. The study uses the game's player performance data to devise performance metrics for online players. We report three major findings. First, we show that the game's point-scaling system overestimates performances of lower level players and underestimates performances of higher level players. We present a novel pointscaling system based on the game's player performance data that addresses the underestimation and overestimation problems. Second, we present a highly accurate predictive model for player performance as a function of past behavior. Third, we show that playing in groups impacts individual performance and that player-level characteristics alone are insufficient in explaining an individual's performance, which calls for a different set of performance metrics methods.",
author = "Shim, {Kyong Jin} and Ahmad, {Muhammad Aurangzeb} and Nishith Pathak and Jaideep Srivastava",
year = "2009",
doi = "10.1109/CSE.2009.452",
language = "English",
isbn = "9780769538235",
volume = "4",
pages = "1199--1204",
booktitle = "Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009",

}

TY - GEN

T1 - Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs)

AU - Shim, Kyong Jin

AU - Ahmad, Muhammad Aurangzeb

AU - Pathak, Nishith

AU - Srivastava, Jaideep

PY - 2009

Y1 - 2009

N2 - This paper examines online player performance in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. The study uses the game's player performance data to devise performance metrics for online players. We report three major findings. First, we show that the game's point-scaling system overestimates performances of lower level players and underestimates performances of higher level players. We present a novel pointscaling system based on the game's player performance data that addresses the underestimation and overestimation problems. Second, we present a highly accurate predictive model for player performance as a function of past behavior. Third, we show that playing in groups impacts individual performance and that player-level characteristics alone are insufficient in explaining an individual's performance, which calls for a different set of performance metrics methods.

AB - This paper examines online player performance in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. The study uses the game's player performance data to devise performance metrics for online players. We report three major findings. First, we show that the game's point-scaling system overestimates performances of lower level players and underestimates performances of higher level players. We present a novel pointscaling system based on the game's player performance data that addresses the underestimation and overestimation problems. Second, we present a highly accurate predictive model for player performance as a function of past behavior. Third, we show that playing in groups impacts individual performance and that player-level characteristics alone are insufficient in explaining an individual's performance, which calls for a different set of performance metrics methods.

UR - http://www.scopus.com/inward/record.url?scp=70849127985&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70849127985&partnerID=8YFLogxK

U2 - 10.1109/CSE.2009.452

DO - 10.1109/CSE.2009.452

M3 - Conference contribution

SN - 9780769538235

VL - 4

SP - 1199

EP - 1204

BT - Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009

ER -