Using achievements for analytics, retention, or skill

Achievements can have many purposes in a game.

For analytics

At GDC 2010, Bruce Philips from Microsoft showed how he used Xbox Live achievements to evaluate progression and campaign completion in a dozen FPS. These achievements already existed, and were determined by the game designers, yet provided very useful for analytics.

Traditionally, achievements are binary: you have unlocked it, or you haven't. You can treat achievements as milestones that the player reached. Milestone achievements include: FPS campaign milestones or Forza 4's "import data from Forza 3".

Achievements can also take several levels. For example, Clash of Clans' Release the Beasts achievement takes 4 values: 0 when you start, 1 when you unlock Archer, 2 when you unlock Wall breaker, and 3 when you unlock Dragon. This achievement, by itself, suffices to segment the player base into new, intermediate, advanced, and expert players. It's a double-win: players have more achievements to go for, and you get a better picture than with a binary achievement.

An achievement can keep counting even after it's been unlocked; for example, Clash of Clans' Gold Grab has 3 steps (4 values) but keeps track of the gold you looted. That way, achievements can also be numerical, not just binary or Likert-like.

Achievements can be even more complex and hold metadata such as when you unlocked it, how many days it took you to unlock it, after how many tries, in how much time, and so on. With this metadata, you can label player behavior more precisely. For example, a Clash of Clans player may unlock Gold grab 3 after 3 months where another would only take one month. The later certainly plays much more (number of sessions, session length) than the former.

For retention

Achievements provide long-term goals to the player. Moreover, they can be made so the player always has a near-complete achievement in sight. For example, you reach 490/500 mortars destroyed in Clash of Clans' Mortar Mauler achievement. You play another hour to destroy 10 mortars and get the 10 gems, but by then, another achievement has reached near-completion too, and you could play another hour.

Retention achievements don't always involve grinding. They can encourage the player to explore the design space.

For skill (or luck)

For example, in FTL Repair back to full health with only 1 HP remaining. Skilled players can risk it, for the thrill. But other players can also wait for it to happen by itself.

Other FTL examples include completing a mission in less than X minutes, or not using any weapon until sector 5. Or even taking 5 turns in a row in the card game Ascension.

Blizzard's Hadoop platform

Talk given by Brian Griffith and Amanda Gerdes at the OC Hadoop user group meeting in October 2013.

Blizzard uses the same Hadoop platform for Diablo 3, Starcraft 2, WoW, and Hearthstone. This platform went live in March 2013. Before this platform, game developers would log game events in log files, and use custom scripts to ETL these log files into relational databases for analysis. Problem: cumbersome, hard to maintain, low performance.

Solution: game developers, on their own, decide what to track in their game, and send that data to the platform. Instead of a log file, the game developers send protobuf objects. The 20 nodes in the Hadoop cluster receive and deserialize around a billion objects per day. The message's headers determine where to store each protobuf object within Hadoop. Blizzard also uses Hadoop as an operational data store. The cluster runs map-reduce jobs to filter and aggregate the stored protobuf objects. Currently, the 20 nodes store 60TB, but now that Blizzard realizes what they can do with Hadoop, they plan a 100-node cluster storing 1PB. Current bottleneck: CPU for deserialization. They rarely hit the disk for data so no IO waiting bottleneck.

For the messaging, they use a federation of machines running RabbitMQ. 50 producers worldwide (China, Europe, US, etc.) and 8 consumers (most likely in the Blizzard headquarters in California). When an Internet cable got cut with China, some messages were queued for 40 hours.

The analysts were using Greenplum for processing game data in parallel and ETL. Now that Hadoop is in place, they could start using Pig for ETL. But sales and customer data still come from relational databases, and the Greenplum is great at ETL. So there is no reason to force analysts into Hadoop. Solution: ETL jobs pull data from Hadoop and store it into Greenplum for warehousing. Greenplum is still in charge of its own ETL jobs.

Some data (such as a character's level or class in WoW) stay interesting across patches, but others (such as transmogrification usage) are only interesting when the feature launches. So the platform allows to build KPIs daily (e.g. activity aggregated per character level) and dive deep on demand.

Use case: WoW economy. Every gold transaction is sent to Hadoop. Reduce step aggregates by NPC id, item id, or player id. Makes it possible to:

• Check if the gold sinks follow designers' expectations.
Detect networks of gold farmers.
• Keep an eye on auction house prices.
• More ...

Importing Chinese characters from CSV file to SQL server

Problem:

Error 0xc02020a1: Data Flow Task 1: Data conversion failed. The data conversion for column "Column 0" returned status value 4 and status text "Text was truncated or one or more characters had no match in the target code page.". (SQL Server Import and Export Wizard)

Solution:

Player Experience Panel, Phillips 2010

Player Experience Panel, Phillips at GDC 2010.

(slides, mostly about FPS games)

Each dot represents the average number of days taken to complete a particular achievement. DLC achievements are farther because DLC were released later (not because they are harder or take more time to complete).

Box gameplay peaks when new DLC is released: new content does increase the number of people playing. However, later DLC packs do not peak as much as the first DLC pack.

Influence of gameplay on skill in Halo: Reach

Huang et al. Influence of Gameplay on Skill in Halo Reach, 2013

Data: 3m player data (entire population) for the first 7 months of Halo Reach, and 70 players in a survey. Mixed methods: use survey data to help explain the big dataset.

Most played mode is Team Slayer: from 3v3 to 5v5, one point per kill, first team to reach 50 kills. Match ends after 15 min.

Player skill metric: TrueSkill's mean μ. More frequent players have bigger μ drop at week 1, but their μ increases faster over the weeks.
The longer the break between two games, the bigger drop in skill. It takes 10 games (3h of gameplay) to regain the skill lost after a break of 1 month.
Most of the top 100 players use the DMR (same range as sniper rifle) and sniper rifle.

The social side of gaming - Ducheneaut 2004

Notes from: The social side of gaming: A study of interaction patterns in an MMO, by Ducheneaut and Moore, 2004

• SWG in 2004: 400k subscribers. The game mechanics make the classes interdependent: after fighting, marksmen go to cities to be healed and buffed by medics and entertainers. Medics need materials from scouts, etc. Social interactions clearly happen in cities.
• Collect public chat events (= text + gestures such as '/bow') in the 2 places with most people on a single server. 100Mb of chat logs using the '/log' in-game command daily for a month. Used Perl to parse and MySQL to store and query events: who talks to who, how (eg /shout), and the actual text content.
• 5500 unique players. Up to 1,200 chat events per hour in a single place.
• Player interactions can be:
  • AFK macros: sending more gestures than they receive
  • Short and efficient instrumental talk: "buff plz", sending very few gestures
  • Genuine socialization, with as many gestures given as received
• Entertainers get XP when performing for someone else, and owning a high-level entertainer may be required to become a Jedi master. Hence lots of entertainers were AFK-macroing their buffs.
• Similarly, to become master in a discipline, players need to teach their skills to other apprentice-players. Hence, experts need to interact with newbies. That was also sometimes macroed.
• Problem#1: AFK macroers and live players do not cohabit well in the same places. Live players do not know what to expect.
• Solutions:
  • Different places for AFK and live players [eg Ragnarok's autotrade merchant map] - but then, the AFK players are never visited.
  • Players should be able to know, at a glance, who is available (and live) for a particular service. The existing name tag system, already indicating the player's guild and faction over players' head, could be used for that.
  • Reward live play
• Problem#2: instrumental play (for the points) uses any means to progress fast; that includes macroing. Social play is not point-based, yet 1) social interactions are measured in points, and 2) playing for points requires taking part in social activities.
• Solution: social progress should not be measured from instrumental play data (HP healed, buffs delivered, number of disciples ...), but rather from live social data (social graph ...).

Gold buying patterns

Picks from a paper I wrote about gold buying patterns for FDG 2012. The data comes from an online questionnaire completed from March to May 2010 by 2800+ WoW players from around the world. Unless mentioned, all results are significant with a p-value below 0.01.

• Overall, 14% of people have ever bought gold.
• Men are twice more likely to buy gold than women (17% vs 8%), but there is no difference between Asians and Westerners.
• Achievement increases the likelihood to buy gold, while immersion decreases it. The effect of achievement is stronger on men.

• 12% of people who only play with people they know IRL have bought gold. This ratio increases to 15% for people who play with both RL relatives and friends made IG, and to 21% for people who play only with friends they made IG.
• Overall, people who have taken longer breaks from the game are more likely to buy gold.

• But really, it's a big mess to know which variables influence gold buying: in the correlation graph below, vertices represent variables, and edges bearing positive/negative values indicate positive/negative correlations between two variables. Values closer to 1 in absolute value indicate higher correlations.

• That's where GLM come in handy: they account for interactions between variables in regressions from multiple variables.
• Controlling for all other variables, the odds of buying gold increase when playing on a private server, being a man, having frozen one's subscription, having made friends IG, playing for achievement, and having played the game for a long time. On the other hand, the odds
• Controlling for all other variables, the odds of buying gold decrease when having had a college education, playing for immersion or socializing, and playing with cousins, siblings, or spouse.

MapReduce for MMOs

MapReduce is a powerful tool to parallelize batches of computations. MMOs may sometimes have to run batches, but from what local game companies tell me, nobody in the game industry is currently using MapReduce. I guess, this is mostly due to studios not knowing what to do with it. Here are some examples.

Business intelligence

Basic metrics such as weekly play time or stop rate can give a rough perspective of the retention of an MMO. These metrics can be estimated with a couple SQL queries on dumps of the production database(s). It starts taking more time and effort to distinguish accross server shard, faction, race, or class. Still, a SQL script running for a few hours can do the job. Fancier analyses such as machine learning or social network graphs explorations take even more time and effort. MapReduce can be used to tune machine learning algorithms through Mahout, and even to process graphs (Google's Pregel also seems interesting for parallel processing of graphs: the Pregel version of PageRank takes 15 lines of code).

Detecting bots, hacks, or gold farmers is not as straightforward, but I think it is doable. First, the typical deviant behaviors have to be determined and made explicit by humans. For instance, speed-hackers send too many messages per second to the server, while gold farmers interact with less players, but more intensely, than normal players. Then, detecting deviant behaviors can be a machine learning classification or a graph parsing problem. In both cases, MapReduce can help.

Game-specific

Matchmaking and ladder: Some pre-calculations or updates to parameters of the ladder and match-making algorithms could be done offline by a small MapReduce cluster. A player's skill is unlikely to change much in 12 hours, so a cron task could run the job twice a day. According to Josh Menke from Blizzard, matchmaking involves gradient descent or Gaussian Density Filtering. Not sure whether Mahout supports GDF, but gradient descent is supported.

Tuning and balancing can take days for system designers. MapReduce could do that automatically: each mapper job is given a particular set of system parameters: player 1 has skill A (cost x SP and inflicts y damage) and skill B (cost z SP and heals w HP), player 2 has skill C (...) and skill D (...). Mappers run a few hundred Monte-Carlo simulations of a player 1 versus player 2 match with a fixed set of parameters (player1:A,B; skillA:x,y; skillB:z,w; ...). When done, mappers pass average statistics (win/loss ratio, average amount of gold at the end of the match, ...) of the 100 matches to reducers who sort them. The interesting configurations for balance are those with a win/loss ratio close to 50%. Naturally, this brute-force way of balancing assumes a proficient AI, and designers will still have to tweak the configurations returned by MapReduce so that they feel fun.

Practical concerns

Engineering detail: MMOs have hundreds of shards, but really only one MapReduce cluster should be needed. Each shard could send its jobs to the MapReduce cluster when it needs them done, and wait asynchronously for the MapReduce answer on a particular port. If the MapReduce job uses data from the production database, producing a daily dump may induce a temporary extra load on the shard's database machines, but this should be fine during empty hours.

MapReduce can be a double-edged sword if overused. Exploring the parameter space of learning algorithms too aggressively may lead to less accurate models.

Edit: Some people have been using MapReduce for analytics: mogade's platform and keighl have been using it through mongodb, but it's more of an engineering constraint (scatter-gather queries in a nosql DB to build a ladder board) than an analytics or machine-learning endeavor.

Rcmdr on Ubuntu

On Windows, R comes with its own GUI, but on Ubuntu, GUIs such as Rcmdr have to be installed manually.

Software sources

Add a local CRAN mirror to Ubuntu, e.g. http://cran.stat.ucla.edu/, which corresponds to USA-California 2. Remember the mirror you add, because it will be the only one that will be allowed by Ubuntu to fetch software packages. Anyway, add /bin/linux/ubuntu/oneiric at the end of the URL (change oneiric for your ubuntu version). This should give the screenshot below:

Then execute:

sudo apt-get update
sudo apt-get install r-base r-base-dev

Installing Rcmdr in R

sudo R

Then in the R command line:

install.packages("Rcmdr")

Then, finish the installation of Rcmdr by installing the missing dependencies:

library(Rcmdr)

A popup appears, select "CRAN mirror" (not "local folder"), click OK, and the console should start printing lines like below:

Loading required package: tcltk
Loading required package: car
Loading required package: MASS
Loading required package: nnet
Loading required package: survival
Loading required package: splines
also installing the dependency 'matrixcalc'

trying URL 'http://cran.stat.ucla.edu/src/contrib/matrixcalc_1.0-1.tar.gz'
Content type 'application/x-tar' length 8436 bytes
opened URL

...

The downloaded packages are in
 '/tmp/Rtmp6X20rs/downloaded_packages'

And the Rcmdr window should appear. To run Rcmdr in the future, type library(Rcmdr) in the R command prompt. Ctrl-A then Ctrl-R to run everything.

Encountered errors

'Rcmdr' is not available: An error like package 'Rcmdr' is not available (for r version 2.13.1) pops up if R was installed from the Ubuntu repository. The current version (as of March 2012) is 2.14.2. sudo apt-get update, then sudo apt-get install r-base r-base-dev should fix this. If you have the same error with not available (for r version 2.14.1), it may be that no CRAN mirror was added in the software sources to download Rcmdr from.

GPG error: The software center notifies you about new R updates from whichever mirror you picked. When sudo apt-get update, you get the following error:

W: GPG error: http://cran.stat.ucla.edu oneiric/ Release: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 51716619E084DAB9

This article provides the solution:

gpg --keyserver keyserver.ubuntu.com --recv 51716619E084DAB9
gpg --export --armor 51716619E084DAB9 | sudo apt-key add -

Sources

• Rcmdr install
• R install
• Producing Simple Graphs with R
• Simple R plots
• Official plot documentation
• Regression examples
• The R code used in the example above

21st Century Game Design - Part I

21st Century Game Design, by Chris Bateman and Richard Boon, 2005.

Part I - Games exist primarily to satisfy the needs of an audience

ch1 - Zen game design

Zen Buddhism can not be learned, it can only be experienced. There is no objective perspective on anything. Hence zen game design's tenets: game design reflects needs + there's no single method to design + there exist methods to game design. These methods are:

• first principles: what you want to do -> game world abstraction -> design -> implementation
• clone and tweak: most common method. existing design -> tweak -> implementation
• meta-rules: goal = provoking debate. meta-rules -> design -> implementation
• expressing technology: in teams without actual game designers. technology -> game implementation
• Frankenstein: art or technical materials -> design -> implementation
• story-driven: narrative -> design -> implementation

Participants in the game project: audience, publisher, producer, programmers, artists, marketing/PR, license holder. Example: saving for causal audience is vital; for hardcore audience, it should not break gameplay; for programmers, it's a technical detail; for producer, it's looking at how other games do it.

ch2 - Designing for the market

The commercial success for a medium clears the way for artistic expression, not the way around

A game design is successful when the target audience is satisfied. This justifies the need for an audience model. Existing models: simple distinction hardcore/casual, distinction by genre (but genres are too vague), EA's model, and ihobo's model.

Simple hardcore/casual distinction

hardcore	casual
plays lots of games	plays few games
game literate	game illiterate
plays for the challenge	plays to relax, kill time, and just for fun
segment can be polarized: many can buy the same title	hard to polarize, diverse and disparate

EA's model:

EA's model take-away: do not ignore hardcores because they are the ones pushing a game to broader segments. Corollary: no TV ads are needed if the game is not made for casuals.

iHobo's model:

Evangelist clusters = gaming press, mainstream press, and the 3 million of hardcores in the world. Target clusters = Testosterone (9M players worldwide), lifestyle (30M), and family (90M) gamers.

Design tools for market penetration (aka demographic game design):

• Looking for good gameplay (ie the game being performance-oriented, with stats, clear goals and victory conditions) vs good toyplay (unorganized). Hardcores are driven by gameplay, but lifestyle and family gamers are driven by both.
• Controls should remain accessible for casuals.
• The minimum play session length is usually expressed in terms of the duration of a level or the time between two save points. For casuals, it should be below 15 minutes, but hardcores do not mind core activities of a game taking at least an hour or two. Ex: a typical DotA match takes 45 to 60 minutes, whereas a (small size) Mine Sweeper can take less than a minute. Nintendo games are also famous for allowing the player to quit at any time and provide core activities of at most a few minutes.
• The average play session length is also lower for casuals: they may complete one level at a time, whereas hardcores can aim at 10 levels per play session.
• Play window: total time spent playing the game. The longer the play window, the longer hardcores will spend evangelizing the game. Therefore, despite most of the players not completing the game, content is crucial! The play window can also be extended by introducing hidden features, higher difficulty levels, variety in characters to play with (to increase replayability), and online PVP (although that only works for Testosterone and hardcore gamers).

Phases of penetration: taking the example of The Sims.

1. Hardcore penetration: the game needs challenge, progress, and depth.
2. Hardcore evangelism: the game needs to appeal to the Lifestyle gamer, easy to reach fun, strong marketing, and a strong license.
3. Casual penetration: the game needs fun, toys, short minimum play session.
4. Casual evangelism: the game needs to get the attention of the mainstream press.

ch3 - Myers-Briggs typology of gamers

Assumption: nature of games people enjoy and frequency of play vary with player personality and reaction to situations. The Myers-Briggs model was developed in the 1940s and indicates how an individual would prefer to react to situations in general. See the Myers-Briggs type frequencies in the US. Four pairs of traits:

Type	Opposite type	Game design
Introversion (50% of pop) think then act, needs private time, 1-to-1 communication and relationships	Extroversion (50% of pop) act then think, likes people, deprived when alone	Most games are played by introverts. Extraverts can take long breaks from the game, so provide a todo list for them when they come back to play, otherwise they'll forget what they had to do in their previous play session. Extraverts like DDR because of its performance aspect.
Sensing (70% of pop) live in the present, apply common sense, based on prior experience, likes clear and concrete info	iNtuition (30% of pop) live in near future, new and imaginative approaches, based on theory, comfortable with fuzzy information, seek for patterns)	Learning and problem solving are frequent gameplay elements in many genres. Learning: in tutorials, S will accept linear series of lessons, but N would rather guess by themselves. Problem solving: S will use trial and error, while N will like to use their lateral thinking skills. Therefore, make lateral thinking puzzles (at most) secondary objectives, or allow the player to progress without having completed all of them. Ex: Super Mario 64 only requires 30 stars to unlock new levels. S want simple and usual mechanics, while N won't mind having to guess the rules and a steep learning curve.
Thinking (30% of women, 60% of men) decide from facts and logic, objective, focus on task, think that conflicts are sometimes unavoidable	Feeling (70% of women, 40% of men) decide from emotion, subjective, focus on consequences to people, wish to avoid conflicts	Clear goals for T. Personal encouragement for F, but T may feel patronized. Solution: useful AND aesthetic/fun items are rewards that will satisfy both T and F. Gathering collectibles give goals to T, but should not be a grind. F are motivated and rewarded when they see their actions have impact on the world or other characters. T enjoy receiving critical feedback (a game over with tips), but F will take it personally. Ex: Zelda gives clear goals (good for T), falling or getting hit results in losing half a heart (and not instant death) and Link has an impact on the game world (good for F).
Judging (55% of pop) plan then move, single task at a time, ahead of deadlines, targets and routines to manage life	Perceiving (45% of pop) plan as you go, multitask, work better before deadline, avoid routine and commitment	J want to beat the game (get all the secret bonuses) and complete objectives. P want to improve their abilities, and enjoy the process. For P, goals completed = feedback that they're on track. Non-linear structure is good for P because if they don't like a level, they can try another and keep progressing. J needs to know what to do to progress. Ex: in Tony Hawk or GTA, players need to collect points (good for J) but they can collect them the way they want (various kinds of skate figures or driving/killing missions or sandbox play, good for P).

TJ vs FP: TJ want challenges to overcome (what most current games provide), FP want easy fun (cf Sims or casual games).

Study hypothesis: hardcore player is a 14-28 year old tech savvy male who plays up to 8 games per month. Supposedly, he plays on his own (hence I), is methodological, goal-oriented enjoys conflicts (T), plays games until completion and looks for perfect score/overachiever (J). Previous quantitative work from the Bartle test by Andreasen showed the average hardcore MMO player is IST. Therefore, let's suppose hardcores are IT. Overall, 15% of women and 35% of men are of type IT.

ch4 - DGD1

DGD1 is intended as a tool to aid in market-oriented game design.

Methods: between 2002 and 2004, ask 408 participants (incl 122 women) to answer a 32-question Myers-Briggs personality test, as well as questions on purchasing and playing habits, and do you consider yourself hardcore, casual, or no idea?. Only look at people who play at least one game per year. Survey advertised on hardcore and casual websites/game portals + university students.

Results: clustering gave a sketchy and incomplete result, and FE and SI dimensions did not help to cluster, but 4 clusters appeared anyway: conqueror (TJ), manager (TP), Wanderer (FP), and participant (FJ). Hypothesis rejected: hardcores are found in E and S (and not only I and T). Still, I and N are higher for hardcores and MMO players than casuals. For each of the four types, twice more respondents reported they were casuals than hardcores.

The DGD1 demographic model

Type	Hardcores Desc	Casuals Desc	Progress	Story	Social
Conqueror	ITJ. Want meaningful challenges, strategies and puzzles, want to complete the game. Want lots of content, try to beat themselves. The game is too easy if they don't die at least a few times. Anger, frustration, boredom, and fiero.	ISTJ. FPS and racing games, they play to compete and win. Rely on genre conventions and do not like deviations from the genre. Fiero (although it's oblivious to them) and schadenfreude in PVP, or in GTA for rampages	Rapid advancement: stats in RPG, better gear in FPS	Focus on plot twists/events, not on characters	Online: vocal hardcores from forums and blogs. They also like to win discussions
Manager	ITP. Strategy and tactics. Winning is less important than mastering the game systems: process-oriented, not goal oriented. Conquerors consider them rivals and targets. Patient. Look for challenging but not impossible. Don't look for hidden features but rather refine their current knowledge. Fiero. Civ series.	ISTP. Want familiar settings and realism. Like construction and management games like SimCity. Hate being stuck even if they suck. Hate interruptions and like smooth difficulty curves.	Steady. Give up if no reliable strategy is found quickly.	Plot, not characters.	None?
Wanderer	INFP. Easy fun and toyplay, not challenges. Variety keeps the fun going. Complete levels in aesthetically pleasing ways. Cf Puzzle Bobble/Bust-a-Move: simple controls, bright colors, and actions with direct and satisfying changes to the environment. See also Mario Party and Super Monkey Ball. Need to be able to give up the current task for another different task. May turn to Conqueror or Manager relatives for help. Emotions: finesse, aesthetics, wonder, awe and mystery, but no fiero.	ENFP. Want to accomplish something in the game world without the need for challenges. Games = way to relax. Feeling of progression or else boredom. Lack of market vectors to reach them [although nowadays there's Facebook]	New toys, colorful and imaginative environments	Emotions. Empathy to characters or investment in world/immersion.	Talk about what they like but avoid arguments
Participant	FJ. Games as social entertainment. Cf DDR, The Sims. Little survey data about this group.		Narrative of group of players	Characters and emotions, but in control of them, not just spectator.	Multiplayer, but must face other players in person, not just online (no MMO)

ch5 - Player abilities

Flow = subjects believe they can complete their activity. Subjects have clear goals and direct and clear feedback. Effortless involvement. Goals should be short-term for participant and conqueror, but long-term for Wanderer and manager because they like to figure out the short-term goals themselves.

Caillois' table of the four categories of play helps understand how flow is related to toyplay. In the table, there really is a continuum between Paidia and Ludus.

The relation between the four play styles of DGD1 and Caillois' categories of games

Conqueror Agon	Manager Agon (Alea tolerated)
Participant Mimicry	Wanderer Mimicry (Alea tolerated)

Caillois' table of the four categories of play

-	Agon (competition)	Alea (chance)	Mimicry (simulation)	Ilinx (vertigo)
Paidia (spontaneous play)	Spontaneous races	Counting out rhymes, coin flipping	Masks and disguisement	Children whirling, swinging
Ludus (structured play)	Sports	Betting, lotteries	Theatre	Skiing, mountain climbing

People with high Myers-Briggs Feeling scores prefer avoiding conflicts, therefore they don't like Agon. They're also more likely to like Mimicry since they focus on people. For example, Wanderers appreciate finesse, which is a component of Mimicry. Ilinx resembles immersion, it appeals to everyone.

Temperament theory gives patterns of behaviors, while Myers-Briggs gives patterns of perception or judgement.

Temperament theory

Temperament	Core needs	Myers-Briggs traits	Skills	% of pop
Rational	Knowledge, competence	NT	Strategic: Think and plan ahead, identify the means to achieve a goal, coordinate actions strategically	10%
Idealist	Unique identity, search for meaning and significance	NF	Diplomatic: Resolve conflicts while recognizing individuality, empathy, find similarities through abstraction	15%
Artisan	Freedom to act and ability to impact	SP	Tactical: Read the current content and manage the situation, work out the next step and take action, improvise to overcome problems	25%
Guardian	Belonging and sense of responsibility/duty	SJ	Logistical: Organizing and meeting needs, optimizing and standardizing, protect and ensure safety	50%

Temperament, Myers-Briggs and DGD1

Type	Myers-Briggs traits	Hardcore temperament trait	Casual temperament trait	Flow provenance	Examples
Conqueror	TJ	strategic	logistical	Capacity to see in advance how to address problems (strategic) and iterate/repeat to improve/optimize the solution (logistical). Willingness to fail and repeat	Production of units in RTS, monsters or bosses with patterns (cf Doom monsters)
Manager	TP	strategic	tactical	Planning ahead (strategic) and reacting to rapidly changing situations (tactical). Hardcores like to get lost in their thoughts, ideally without time limitations. Casuals have flow in the action, and need short-term goals.	RTS have both spontaneous maneuvers and long-term strategies. Civ, Chess or puzzles for hardcores.
Wanderer	FP	diplomatic	tactical	Immersion, explicit short-term goals (tactical). Completion of goals is not a big thing, it happens almost as a side-effect of exploration. Give them time to explore.	Platformers (goal is obvious and challenges relatively easy)
Participant	FJ	diplomatic	logistical	Feeling of belonging, toyplay, optimize relationships (logistical) with other characters or players, immerse themselves in social situation	The Sims, Animal Crossing

Casual audience is best approached with familiar settings and content, and with gameplay that revolves around optimization or thinking on your feet (tactical). Hardcores prefer original games that give them a sense of identity (diplomatic), and problems to solve (strategic), e.g. Final Fantasy focuses on story and strategic battles.

MMO player research methods

Types of data of interest in MMO player studies:

• demographic data: country, gender, age, job, psychological traits, tech-saviness, happiness, revenue
• marketing data: how much spent on games per month, how many games bought,
• play data (applicable to all games and game genres): weekly play time, average play session duration, game and genre literacy,
• genre-specific and game-specific data: for MMOs and WoW in particular: who you play with, guild position, achievement/immersion/social motivation scores

Challenges of player studies: using tools and methods to convert data into useful information, and avoiding erroneous conclusions by crossing results obtained from various methods.

List of qualitative tools.

Methods	Qualitative	Quantitative
Data collection tools	Note taking or recording during open-ended interviews, lab studies, think aloud, or participant observation/ethnographic play. All methods gather all types of data - you just need to ask. Snowball sampling is useful to collect more people concerned by the same phenomenon (eg people from the same guild).	Questionnaires can gather multiple types of data together, but beware: what people say they do differs from what they actually do. Non-obtrusive logging of game data has the advantage of being objective, and can be done using programmable game add-ons that players need to install. It's even possible to cross game and marketing data together from the developer/publisher side (cf the EverQuest dataset from SOE).
Data analysis tools	Note tagging and affinity diagrams are methods used during the note parsing phase. Stop interviewing when respondents do not say/show anything new anymore. Preferably, do not wait to start parsing the data; parsing as notes are taken gives an immediate feedback loop useful to detect useless interview questions, and to know when to stop accumulating data.	The most popular tools to use for stat analysis are SPSS, R, and Excel. Obtaining causal relationships is usually quite hard. On the other hand, simple comparisons and correlations are often successful. Regressions can work too. Data mining/machine learning using Weka or Matlab. Clustering (PCA, LDA, and even KPCA if small dataset) can identify patterns during exploratory phases. Feature selection or decision trees to identify the most important features. Poisson process or Markov chains for temporal evolutions. Bayes, neural network, SVM, random forests, and others for classification.
Pros and cons	Deep, and useful to hint at the reasons explaining a particular phenomena, especially in MMOs, where the metagame (forums, blogs, chats) has a huge influence on the actual game. Snowball sampling in WoW brings lots of players from the same guild(s), or with similar opinions. Therefore (and also because of small sample size): poor ability to generalize. Qualitative methods can also be used as exploratory studies to help build a quantitative questionnaire with relevant questions.	Broad and useful to detect surface trends. Sample bias/representativity: it's very hard to select a representative sample of the player population. For example, selecting players from wow.com brings a lot of hardcore players (since hardcore players read forums while the most casual players don't). Very hard to explain completely a phenomenon because there's always important features missing from the dataset. Machine learning is also difficult for that same reason. It's very easy to get lost in post-hoc effects, or to simply not be able to explain a particular number because you've never played the game. Generally, demographic data can not be caused by game-specific data: it's not because people are hardcore that they're young, but rather the opposite.
Researchers	Bardzell, Bartle, Kow, Nardi, Pace, Pearce, TL Taylor	Andreasen (= quantitative Bartle test), Bateman, Ducheneaut, Seay (not working on that domain anymore?), Williams, Yee,