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World of Warcraft Avatar History Dataset
Yeng-Ting LeeDepartment of Electrical
Engineering, National TaiwanUniversity
Taipei, [email protected]
Kuan-Ta ChenInstitute of Information
Science, Academia SinicaTaipei, Taiwan
Yun-Maw ChengDepartment of ComputerScience and Engineering,
Tatung UniversityTaipei, Taiwan
Chin-Laung LeiDepartment of Electrical
Engineering, National TaiwanUniversity
Taipei, [email protected]
ABSTRACTFrom the perspective of game system designers, players’ be-havior is one of the most important factors they must con-sider when designing game systems. To gain a fundamentalunderstanding of the game play behavior of online gamers,exploring users’ game play time provides a good startingpoint. This is because the concept of game play time is ap-plicable to all genres of games and it enables us to model thesystem workload as well as the impact of system and networkQoS on users’ behavior. It can even help us predict play-ers’ loyalty to specific games. In this paper, we present theWorld of Warcraft Avatar History (WoWAH) dataset, whichcomprises the records of 91, 065 avatars. The data includesthe avatars’ game play times and a number of attributes,such as their race, profession, current level, and in-game lo-cations, during a 1, 107-day period between Jan. 2006 andJan. 2009. We believe the WOWAH dataset could be usedfor various creative purposes, now that it is a public asset ofthe research community. It is available for free download athttp://mmnet.iis.sinica.edu.tw/dl/wowah/.
Categories and Subject DescriptorsH.1.2 [Models and Principles]: User/Machine Systems—Human factors; K.8.0 [Personal Com-puting]: General—Games
General TermsHuman Factors, Measurement
KeywordsGame session, MMOG, MMORPG, User behavior
Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.MMSys’11, February 23–25, 2011, San Jose, California, USA.Copyright 2011 ACM 978-1-4503-0517-4/11/02 ...$10.00.
Figure 1: A screenshot of World of Warcraft.
1. INTRODUCTIONMassively multiplayer online games (MMOGs) are a pop-
ular type of entertainment on the Internet. In [26], it isreported that over 55% of Internet users are also onlinegamers, while the number of MMOG subscribers worldwidegrew to 21 million in 2010. For the popular game Star-Craft II1, one of the biggest real-time strategy (RTS) titlesin 2010, the average number of active players is 500, 000 perday; while the number of players listed on the Battle.netscoreboard2 is around 1.7 million [1].
Among the various types of MMOGs, massively multi-player online role-playing games (MMORPGs) are the mostpopular genre, with a 95% share of the MMOG market [22].According to [25], the MMORPG market is currently worthin excess of US$6 billion worldwide, with an anticipatedvalue of US$8 billion by 2014.
From the perspective of game system designers, players’behavior is one of the most important factors they mustconsider when designing game systems. However, under-standing player behavior is not a trivial task because thereare so many types of player behavior to be considered, andthe behavior is usually game-dependent. For example, how
1http://us.battle.net/sc2/en/2http://www.sc2ranks.com/ranks/
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players control troop movements in real-time strategies andhow players interact with each other (e.g., chatting, trading,or fighting) in role-play games may be important issues insome game genres, but not in others. Moreover, some typesof player behavior are more general than others. For exam-ple, how players react to imperfect network conditions is ageneral problem in all real-time network games, so it is aconcern for all game designers.
To gain a fundamental understanding of users’ online gameplay behavior, we believe that exploring users’ game playtime provides a good starting point because game play timeis the most general type of player behavior; thus, it is appli-cable to all genres of games. Game play time refers to all theinformation related to the time between when a player logsinto a game and the time he logs out. How can informationabout game play time benefit the research community? Thefollowing list describes some of the uses of such information.
1. System workload modeling/prediction: The ar-rival and departure process of game players determinesthe workload of game systems [7,9]. Thus, if we couldmodel and even predict game players’ arrival and de-parture events, we could improve the provisioning andallocation of system resources, and simultaneously main-tain satisfactory QoS (Quality of Service) levels [3, 5].
2. System QoS modeling/prediction: The early de-parture of game players may indicate that a game sys-tem is providing less-than-satisfactory QoS. By com-bining the information about system- and network-level QoS with game play time data, we can model theimpact of QoS factors on player departure processesand improve system designs based on such models [4].
3. Player loyalty modeling/prediction: Players mayquit a game because they are dissatisfied with thegame’s design or content, or possibly due to cheat-ing by other players. In other words, to some degree,quitting behavior may indicate low user satisfaction.Thus, we may infer a player’s loyalty to a particulargame based on his game play history [18,19].
In this paper, we present the World of Warcraft AvatarHistory (WoWAH) dataset, which provides information aboutthe game play time of 91, 065 avatars and a number of theirattributes. We focus on the World of Warcraft (WoW) be-cause it is the most popular MMORPG in the world (asof December 2010). According to MMOData.net [8], thegame’s 12 million subscribers accounted for 62% of the MMOGmarket in March 2010 [14]. Because of the game’s popular-ity, it has motivated studies by researchers in various aca-demic fields, such as psychology [24], social interaction [6,15], and game play behavior [2, 9, 10,11,16].
The WoWAH dataset comprises continual observations ofthe status of 91, 065 avatars over a 3-year period (Jan. 2006to Jan. 2009). During each observation we recorded thenames of the avatars that were online and their respec-tive attributes, including their current levels and in-gamelocations. Observations were made at 10-minute intervals;hence, during the 3-year period, we made approximately157, 680 observations (samples) on a WoW server. Becausethe samples are dense, we can extract the avatars’ game playhistory from the 10-minute observations (i.e., if an avatarappeared in one sample but not in the subsequent one, we
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Figure 2: The survival curve of players’ subscriptiontimes.
assumed it had logged out in the time between the two sam-ples).
The WOWAH dataset is now publicly available athttp://mmnet.iis.sinica.edu.tw/dl/wowah/. Researchersare free to download it for their own research purposes. Inthe remainder of the paper, we explain how we compiled thedataset in Section 2, and discuss its basic statistics in Sec-tion 3. We present two sample studies based on the datasetin Section 4, and then summarize our conclusions in Sec-tion 5.
2. DATASET DESCRIPTIONIn this section, we explain how we collected the avatars’
game play history in an automated fashion. After describingthe data collection methodology, we discuss the naming andformat of log files, and conclude with a summary of thetraces.
2.1 Data Collection MethodologyWe used the who command to collect the trace at regular
intervals. The command, which asks the game server to pro-vide a list of avatars in the same faction that are currentlyonline, is available to every player in the game; hence, any-one can obtain the game play history of all the avatars inthe same faction from the server.
To collect the trace, we created a character in a World ofWarcraft realm (the Light’s Hope realm in Taiwan) and keptit online throughout the 3-year study period. The characterwas controlled by a program written in the Lua scriptinglanguage [23], which World of Warcraft uses for interfacecustomization. Our program automatically collected a list ofthe online avatars every 10 minutes. If an avatar logins andlogouts within 10 minutes, we may not be able to observe hisre-login activity in consecutive snapshots. However, we donot think this is a significant problem because most Worldof Warcraft sessions are much longer than 10 minutes [17].
Because of scalability considerations, World of Warcraftservers restrict the number of avatars returned in response toa query to a maximum of 50. Thus, we have to narrow downour query ranges by dividing all the avatars into differentraces, professions, and levels. For example, in the initialquery, we need to ask the server to list all the avatars inthe Fighter class whose levels are between level 1 and level20. Then, in the second query, we ask the server to list all
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the avatars in the Fighter class whose levels are betweenlevel 21 and level 40, and so on. This technique allows usto systematically list the entire set of online avatars despitethe restriction of the query function.
2.2 Log File NamingThe directory structure of the dataset archive comprises
two levels of sub-directories. Each sub-directory in the firstlevel represents the traces in a season with the directoryname format yyyy-fm-lm, where fm, lm, and yyyy refer, re-spectively, to the first month, last month, and year of thetraces in the sub-directory. For example, for the traces in2006, there are four first-level directories, namely, 2006-01-03, 2006-04-06, 2006-07-09, and 2006-10-12. Each of thesecond-level sub-directories contains the traces collected ina day with the directory name format yyyy-mm-dd, where mmdenotes the month and dd denotes the day of the month ofthe traces in the directory. With a few exceptions, which wediscuss shortly, there are 144 files in each second-level sub-directory. Each file contains all the information observedabout the avatars online during the sampling period. Thereason for 144 files is that there are 1, 440 minutes in a dayand we make observations every 10 minutes. The name for-mat of the log files is hh-mm-ss, where hh, mm, and ss rep-resent, respectively, the hour, minute, and second of thesampling time.
2.3 Log FormatA log file is composed of two arrays: Persistant_Storage
and RoundInfo. All the information collected about theavatars’ history is stored in the Persistant_Storage array.Each element stores the information about an avatar ob-served during the sampling period; thus, the number of ele-ments is equal to the number of avatars online in that sam-pling interval. An element is a string that contains 11 fieldsseparated by commas. The 11 fields are dummy, query time,query sequence number, avatar ID, guild, level, race,class, zone, dummy, dummy. The meanings and valid valuesof the fields are detailed in Table 2. We also provide threesample records in Table 3. The first record, which relatesto the initial query, indicates that we observed an avatarwith ID 467 at 23:59:39 on 01/01/2006, and the avatar wasa level-1 non-guilded Orc Warrior in Orgrimmar.
2.4 Data SummaryWe collected the data over 1, 107 days between Jan. 2006
and Jan. 2009. During the monitored period, 91, 065 avatars,and 667, 032 sessions associated with the avatars were ob-served. Because the sampling interval was 10 minutes, thereshould have been 159, 408 samples, each providing the sta-tus of all the avatars online during the sampling period.However, 21, 324 samples were missing due to server main-tenance3 and occasional client compatibility problems. Asa result, the total number of samples was 138, 084. In thedataset archive, the details of the missing samples are storedin the missingdata directory. To protect players’ privacy,we mapped the avatars’ names and guild names randomly aspositive integers with a consistent mapping (i.e., the samenames were always mapped to the same integers). A sum-mary of the dataset is presented in Table 1.
34am to 12pm every Thursday is a weekly maintenance downtime.
Table 1: Dataset SummaryRealm TW-Light’s HopeFaction HordeStart date 2006-01-01End date 2009-01-10Duration 1,107 daysSampling rate 144 samples per day# of samples 159,408# of missing samples 21,324# of avatars 91,065# of sessions 667,032
Table 2: Field DescriptionField Valid Values
Query Time Between Jan. 2006 and Jan. 2009
Query Seq. # An integer ≥ 1
Avatar ID An integer ≥ 1
Guild An integer within [1, 513]
Level An integer within [1, 80]
RaceBlood Elf, Orc, Tauren, Troll, Un-dead
ClassDeath Knight, Druid, Hunter,Mage, Paladin, Priest, Rogue,Shaman, Warlock, Warrior
Zone One of the 229 zones in WoW world
3. BASIC STATISTICSIn this section, we present some basic statistics derived
from the dataset. By assuming that each avatar is played byone game player, we can analyze the subscription length anddaily game play behavior of World of Warcraft players. Wealso examine the variability and regularity of the numberof avatars online because the information is an importantindicator of the workload on game servers.
3.1 Subscription TimeA player’s subscription time denotes the length of time
since he became a member of the game to the time of his lastlogin, i.e., the player has not logged in since then. Figure 2shows the survival function [5] of players’ subscription times.According to the statistics, 60% of users will subscribe forlonger than one year after their first visit, and 50% willsubscribe for longer than 500 days. These figures indicatethat the game is indeed very attractive, and most playersseem to become addicted to its fantasy world once they areimmersed in it.
3.2 Daily Game Play ActivitiesWe also investigate gamers’ daily play behavior, including
the average daily play time, average daily session count, andaverage session play time. Note that if a gamer does notplay the game for some days, we do not include those daysin his average daily play time. For example, if a gamer’ssubscription period was one year, but he only played for 200days, then his average daily play time was his overall playtime divided by 200 days.
The cumulative distribution functions (CDFs) of the av-
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Table 3: Example Avatar Observation RecordsQuery Time Seq. # Avatar ID Guild Level Race Class Zone
01/01/06 23:59:39 1 467 1 Orc Warrior Orgrimmar01/01/06 23:59:39 1 921 19 1 Orc Shaman Orgrimmar01/02/06 00:03:31 45 1367 8 60 Undead Warrior Arashi Mountain
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Figure 3: CDF of daily play time and session times.
Table 4: Summary of daily game play activities
(Mean, SD)Quantiles (5%, 25%,50%, 75%, 95%)
Session time (hr) (2.8, 1.8) (0.4, 1.0, 1.8, 3.0, 5.5)Daily session count (1.7, 0.9) (1.0, 1.1, 1.4, 2.1, 3.3)Daily play time (hr) (3.7, 2.8) (0.5, 1.6, 3.1, 5.1, 8.8)
erage daily play time and the average session play time areshown in Figure 3(a). We find that 75% of gamers playlonger than 1.9 hours per day on average, and 25% playlonger than 4.9 hours per day, which again indicates thatthe game is very attractive to the gamers. If we analyze theaverage session play time, we find significant “knee” around1 hour and 5 hours, which indicate that after logging intothe game, there is a high probability that players will stayfor at least one hour, but usually no longer than 5 hours. Be-cause of the long session property, players probably do notlogin into the game too many times a day; hence, the dailysession count is not large, as shown in Figure 3(b), wheremore than 80% of gamers have less than 2 session counts perday on average. We summarize the quantiles and averagesof the average daily play time, average session play time,and average daily session count in Table 4.
3.3 Variations in the Number of Avatars On-line
In this subsection, we consider how the number of avatarsonline evolves over time. We begin by plotting the averagemaximum, mean, median, and minimum numbers of avatarsin different time scales, as shown in Figure 4. The top graphin the Figure 4 shows that, except for daily variations, thereare no obvious systematic variations at larger time scales.By contrast, the middle graph shows that daily variationsdominate the variability in the evolution of the number ofavatars. The blackout period on Thursday morning is dueto weekly maintenance downtime scheduled by the operator.The bottom graph plots daily variations in the number ofavatars. Clearly, there are large fluctuations in the numberof avatars (between 0 and 600) in a 24-hour period. Onaverage, the quietest period is around 7am and the busiestperiod is around 11pm. This finding shows that many peo-
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Figure 4: The averaged avatar number processshown in different time scales. The top two graphsare plotted based on daily averages and the bottomone is plotted based on the hourly averages.
ple play all night, and therefore implies that the game isaddictive.
We also consider the distribution of the number avatarsin consecutive periods for different time scales, as shown inFigure 5. The left-hand graph in the figure shows that thenumber of avatars in different months are similar with aslightly decreasing trend over the 9-month period. The onlyexception is January, which we suspect is a consequence ofthe Chinese New Year vacation. During such vacations, Chi-nese people gather with their families, play mahjong, andtravel; thus, in our trace, the number of avatars in Januarywas slightly lower than in February. The middle graph inFigure 5 shows that, as expected, the distribution of thenumber of avatars in different weeks is similar. Meanwhile,in the right-hand graph, the distribution of avatars on eachday of the week indicates that the overall game play timeis significantly different on weekdays and weekends, withSunday attracting more gamers than Saturday. The dis-tribution curve of Thursday is due to the scheduled weekly
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Figure 5: The cumulative distribution functions of averaged avatar numbers in different time scales. Theleftmost graph is based on monthly averaged numbers; the middle graph is based on weekly averaged numbers;the rightmost graph is based on daily averaged numbers.
maintenance. The deviation of the curve from those of otherweekdays implies that gamers return immediately after themaintenance period without “wasting” off-game time, whichagain reveals the addictiveness of the game.
3.4 Regularity of the Number of Avatars On-line
The strong regularity over consecutive weeks and daysand the strong variability within a 24-hour period can befurther examined by auto-correlation function (ACF) plotson corresponding time scales, as shown in Figure 6. Thetop graph in the figure exhibits strong weekly regularity,while the middle graph exhibits strong daily regularity inthe evolution of the number of avatars. The ACF of thenumber of avatars over the number of hours in the bottomgraph shows that there is no regularity within a 24-hourperiod; however, the large coefficients with a lag of less than2 hours indicate the potential for accurately predicting thenumber of avatars in the next few hours.
4. SAMPLE USES OF THE DATASETWe consider two sample studies that examine different
aspects of the WoWAH dataset. The first study tries tomodel and predict the subscription time of game players andthe usage time of avatars; and the second uses the WoWAHdataset to evaluate the effectiveness of a server consolidationstrategy designed for MMORPGs. We present the studieshere in the hope of motivating potential users of the datasetto find other creative uses.
4.1 Player Unsubscription PredictionIn [19], based on the WoWAH dataset, the authors pre-
sented a prediction model of online gamers’ intentions thattakes a player’s game hours as input and predicts whetheror not the player will decide to continue in the game once hiscurrent subscription expires. Predictions about players’ non-renewal decisions are important to game operators becausethe decisions affect the operators’ revenue directly. The ra-tionale of the scheme is that, if the intentions of players canbe predicted before they actually leave a game, the gameoperator can take remedial action to prevent the players’ de-parture and improve the game based on feedback provided
by those players. The authors developed a scheme to predicta gamer’s departure [19]. For hardcore players, the schemeenables game operators to predict players’ non-renewal de-cisions two months prior to expiry of their membership witha compound accuracy of over 80%. In addition, the authorsconducted a generalizability analysis, which showed that thescheme is generalizable across different MMORPGs and thatit can be applied to both game play and avatar usage pre-dictions.
4.2 Server Consolidation TechniquesWith the advent of virtualization technology [20], con-
solidation of MMORPG servers [13] is now possible, eventhough various system architectures may be involved. By us-ing an appropriate server consolidation strategy, an operatorcan reduce investments in hardware (by consolidating differ-ent game servers on a physical computer) and energy con-sumption (by putting idle servers into the sleep mode when-ever appropriate), while maintaining user-perceived servicequality. In [12], the authors proposed using a zone-basedserver consolidation strategy for MMORPGs to reduce theconsiderable investments in hardware and the energy con-sumption. Based on theWoWAH dataset, they implementedthe proposed zone-based server consolidation algorithm, whichreallocates zones among a set of server clusters regularly.The algorithm’s impact in terms of the number of serversrequired and the energy consumption was also evaluated.Moreover, the authors presented a technique to upscale thenumber of avatars based on the avatars’ game play his-tory observed on a single server from the WoWAH dataset.Specifically, they modeled the number of avatars in a realmas a normal distribution with mean 2, 640 and standard devi-ation 1, 500 (derived from the data in the Warcraft Census4
and WoW Database5) assuming that the maximum numberof avatars per realm was 7, 500. After obtaining the numberof avatars in each realm (with the above normal distribu-tion), they computed the number of avatars in each zonebased on the relative number of avatars in each zone in theWoWAH trace.
4http://www.warcraftrealms.com/census.php5http://www.hotwow.com/
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Figure 6: The auto-correlation functions of theavatar number process in different times scales. Thetop graph is based on daily averaged numbers; themiddle graph is based on hourly averaged numbers;and the bottom graph is based on the avatar numberobserved at each 10 minutes.
5. CONCLUSIONIn this paper, we have presented the WoWAH dataset, a
three-year trace of avatars’ game play history in World ofWarcraft. We believe the trace is of value and could benefitthe research community for the following reasons:
1. World of Warcraft, which currently has 12 million sub-scribers, has been the most popular MMORPG in theworld since 2006 [21].
2. The dataset comprises avatars’ game play records fora three-year period from Jan. 2006 to Jan. 2009. Dur-ing that time, game designers released two importantexpansions, namely, The Burning Crusade and Wrathof the Lich King.
3. The value of the dataset has been demonstrated instudies of user behavior and game server clouds, asmentioned in Section 4.
In addition to the presented sample studies, we believe thatgame designers and researchers will find further creative usesfor the dataset now that it is a public asset of the researchcommunity. The dataset is available for free download athttp://mmnet.iis.sinica.edu.tw/dl/wowah.
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