Player performance prediction in massively multiplayer online role-playing games (MMORPGs)

Kyong Jin Shim, Richa Sharan, Jaideep Srivastava

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

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages71-80
Number of pages10
Volume6119 LNAI
EditionPART 2
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad
Duration: 21 Jun 201024 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6119 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
CityHyderabad
Period21/6/1024/6/10

Fingerprint

Performance Prediction
Game
Prediction Model
Performance Management
Binning
Performance Monitoring
Prediction
Performance Model
Histogram
Predictors
Coverage
Discretization
Projection
Monitoring
Evaluate

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Shim, K. J., Sharan, R., & Srivastava, J. (2010). Player performance prediction in massively multiplayer online role-playing games (MMORPGs). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6119 LNAI, pp. 71-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6119 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-13672-6_8

Player performance prediction in massively multiplayer online role-playing games (MMORPGs). / Shim, Kyong Jin; Sharan, Richa; Srivastava, Jaideep.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6119 LNAI PART 2. ed. 2010. p. 71-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6119 LNAI, No. PART 2).

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

Shim, KJ, Sharan, R & Srivastava, J 2010, Player performance prediction in massively multiplayer online role-playing games (MMORPGs). in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 6119 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6119 LNAI, pp. 71-80, 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010, Hyderabad, 21/6/10. https://doi.org/10.1007/978-3-642-13672-6_8
Shim KJ, Sharan R, Srivastava J. Player performance prediction in massively multiplayer online role-playing games (MMORPGs). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 6119 LNAI. 2010. p. 71-80. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-13672-6_8
Shim, Kyong Jin ; Sharan, Richa ; Srivastava, Jaideep. / Player performance prediction in massively multiplayer online role-playing games (MMORPGs). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6119 LNAI PART 2. ed. 2010. pp. 71-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
@inproceedings{4b869382f9cf4eeca615498614f43186,
title = "Player performance prediction in massively multiplayer online role-playing games (MMORPGs)",
abstract = "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.",
author = "Shim, {Kyong Jin} and Richa Sharan and Jaideep Srivastava",
year = "2010",
doi = "10.1007/978-3-642-13672-6_8",
language = "English",
isbn = "3642136710",
volume = "6119 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "71--80",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 2",

}

TY - GEN

T1 - Player performance prediction in massively multiplayer online role-playing games (MMORPGs)

AU - Shim, Kyong Jin

AU - Sharan, Richa

AU - Srivastava, Jaideep

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=79956292127&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79956292127&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-13672-6_8

DO - 10.1007/978-3-642-13672-6_8

M3 - Conference contribution

AN - SCOPUS:79956292127

SN - 3642136710

SN - 9783642136719

VL - 6119 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 71

EP - 80

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -