Kyong Jin Shim

Team Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs)

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.

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

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 games player performance data to devise performance metrics for online players. We report three major findings. First, we show that the games point-scaling system overestimates performances of lower level players and underestimates performances of higher level players. We present a novel point-scaling system based on the games 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 individuals performance, which calls for a different set of performance metrics methods.

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

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 players 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.

AMOD: a morpholino oligonucleotide selection tool

AMOD is a web-based program that aids in the functional evaluation of nucleotide sequences through sequence characterization and antisense morpholino oligonucleotide (target site) selection. Submitted sequences are analyzed by translation initiation site prediction algorithms and sequence-to-sequence comparisons; results are used to characterize sequence features required for morpholino design. Within a defined subsequence, base composition and homodimerization values are computed for all putative morpholino oligonucleotides. Using these properties, morpholino candidates are selected and compared with genomic and transcriptome databases with the goal to identify target-specific enriched morpholinos. AMOD has been used at the University of Minnesota to design ∼200 morpholinos for a functional genomics screen in zebrafish. The AMOD web server and a tutorial are freely available to both academic and commercial users at .

Player and Team Performance in Everquest II and Halo 3

The market for video games has skyrocketed over the past decade. In the United States alone, the video game industry in 2009 generated almost US

An Exploratory Study of Player and Team Performance in Multiplayer First-Person-Shooter Games

20 billion in sales. Furthermore, according to Lenhart et al. (2008), an estimated 97% of the teenage population and 53% of the adult population are regular game players. Massively multiplayer online games (MMOGs) have become increasingly popular and amassed communities comprised of over 47 million subscribers by the year 2008. MMOGs are online spaces providing users with comprehensive virtual universes, each with its own unique context and mechanics. They range from the fantastical world of elves, dwarfs, and humans to space faring corporations and mirrors of our world. Large numbers of users interact and role-play via in-game mechanics.

An Exploratory Study of Player Performance, Motivation, and Enjoyment in Massively Multiplayer Online Role-Playing Games

In this paper, we report findings from an exploratory study of player and team performance in Halo 3, a popular First-Person-Shooter game developed by Bungie. In the study, we first analyze player and team statistics obtained from the 2008 and 2009 seasons for professional Halo 3 games in order to investigate the impact of change in team composition on player and team performance. We then examine the impact of past performance on future performance of players and teams. Performing a large-scale experiment on a real-world dataset, we observe that player and team performance can be predicted with fairly high accuracy and that information about change in team composition can further improve the prediction results.

Modeling Player Performance in Massively Multiplayer Online Role-Playing Games: The Effects of Diversity in Mentoring Network

Enjoyment is a vital component in the business model of the game industry. Despite research on their relationship to the success or failure of a game, little attention has been paid to the effect of player performance on player enjoyment. This study investigates how player motivation, player performance, and player enjoyment are connected in Ever Quest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. It investigates the impact of task difficulty and player performance on player enjoyment. Estimation of task difficulty and player performance was performed by the analysis of the games operational data such as game logs, while assessment of player enjoyment was based on a large-scale survey. Our findings indicate that the correlations do not fully conform to the flow theory and additionally suggest that the knowledge of player motivations is critical in accurately predicting player enjoyment.

Sequence Alignment Based Analysis of Player Behavior in Massively Multiplayer Online Role-Playing Games (MMORPGs)

This study investigates and reports preliminary findings on player performance prediction approaches which model players past performance and social diversity in mentoring network in Ever Quest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. Our contributions include a better understanding of performance metrics used in the game and a foundation of recommendation systems for mentors and apprentices. We examined three different game servers from the Ever Quest II game logs. In all three servers, the results from our analyses suggest that increase in social diversity in terms of characters and classes encountered moderately negatively correlates with player performance. Based on this finding, we built predictive models to predict players future performance based on past performance and social diversity in terms of mentoring activities. Our results indicate that 1) models employing past performance and social diversity perform better and 2) prediction for mentors is generally better than that for apprentices.

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

This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for predicting inactive game players that either leave the game permanently or stop playing the game for a long period of time. Sequence similarity scores and derived statistics form profile databases of inactive players and active players from the past. SABAF uses global and local sequence alignment algorithms and a unique scoring scheme to measure similarity between activity sequences. SABAF is tested on the game player activity data of Ever Quest II, a popular massively multiplayer online role-playing game developed by Sony Online Entertainment. SABAF consists of the following key components: 1) sequence alignment-based player profile databases, 2) feature selection schemes and prediction model building, and 3) decision support model for determining inactive players.