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  • How To Measure Fun For Game Designers

    [10.08.20]
    - Roman Szomolai

  • Measurement is all about decisions

    Design is a sequence of decisions about what to make and what not to make. To make this decision we need to have a theory about how each part of our designed game works and what experience it induces. To have the best possible theory we have to improve it as much as possible, and to improve it we have to have as many as good measurements as it is possible. Best designers are not one, who think they know how things should be, but those who are able to update their theory as fast as possible (and this is basically iteration on design).  

    Every singular decision is connected to a specific subset of hypotheses in theory. These are usually hypotheses regarding proxies or approximate to the proxies we use. Closer the proxies are to our question stronger connection they have. Therefore to make decision we should measure and explore the closest proxies. For example: What you eat and how much you move is closer to your health than fuel consumption of a bus in your city. Nevertheless, both are part of an energy transformation theory. Hence learning about the combustion system may help us with our diet, learning about calories will help us way more.

    As you can see, closer the connection more value we get from our investment. You should never invest more resources into measurements than you will get from it. What value you can get from investing in measurement? Well main candidates would be faster iterations, lowering opportunity cost, evading retroactive fixing, evading out of scope or under scope features, more effective testing.   

    Beware the noise

    One of the most dangerous parts of collecting data is over-analyzing and over collecting them. I have seen many developers do this. Do you think you didn't? When was the last time you went through Reddit or steam reviews and then came back to the office with a whole new idea about what should you change? This is also the case of that. Humans are pattern making machines. We see faces in clouds, moods in yellow circles with dots and patterns where there are none. Sometimes you can have too much data for your own good, especially if data are not accurate or precise. To lower the chance of the noise talking you should always strive to refutability by looking for counter examples and corroborate by employing multiple different kind of measurements.

    But I don't have a data scientist!

    ...and you don't really need one (but they are extremely useful and I love all of them). It is about properly set parameters of measurement again. Analyst will offer you quite precise and accurate data. No measurement at all will offer you precisely and accurately no data. There is vast space between these two points where you can operate quite cheaply and fast.

    Here are some examples of measurements in various degrees. I will always add advanced and minimal variant examples. I doubt that you will use the advanced variant anytime soon, but that is ok. Also take notice that it is just a simple summarization of a few methods, it should serve more as inspiration for additional research than exhaustive list.

    Heuristics:

    With this method we are looking for rules of thumb. Some very simple rules that may lead to specific consequences. This is domain of "player type" of game creators and seniors who played it all already. Short Heuristic analysis can reveal a lot even before you start the game. Good news is that you probably already do this, just not systematically. For example: Does this platformer have a coyote time and how far? Does healing potion have the same colour as health (probably red)? It is well known approach in service and product design (try to ask your closest UX designer).  

    • Minimal variant: Just play the game. What are things that you expected to happen and didn't. Show your game to somebody else who likes to play games and listen for what they notice as first. 

    • Advanced variant: Have extremely detailed journal constant of different introspections in various games and cultural artefact. Improve your heuristics by continuous playing all possible games. Make a library of patterns (Like you can find in Art of Game Design by Jesse Schell or Game Mechanics: Advanced Game Design by Ernest W. Adams and Joris Dormans)

    Playtest: 

    Playing games and seeing how it works. What happens in the game. It is basically starting the whole thing and seeing it running. There are a lot of very good articles, Reddit posts and videos on how to playtest for good reason. Playtesting is the cornerstone of any game development. If you are not doing this already then I don't know what you are doing. 

    • Minimal variant: Play the game on your own or with few people; as soon as possible. Repeat all the time. 

    • Advanced variant: oh boy, so many. Kleenex playtesting, focus group testing, company wide testing. Have a dedicated team of pro players or ex pro players and let them play every new iteration of the game...

    Discursive analysis

    Discursive analysis is the analysis of language used in context of a specific topic. People are expressing most of their feedback by language (especially on the internet). Trying to look into this language may offer a very interesting picture. 

    For example: in game Hunt: Showdown (2019) players created a new term "instaburners", this term is a reference to people who start to burn enemy players once they are downed. Burned players cannot be revived by their teammates. Instaburner is somebody who burns an enemy as soon as possible. Fact that this new term was invented tells us a lot about how often this happens and what kind of connotations this can have in game.

    • Minimal variant: read feedback on games and think about the kind of language people use. For example: Do they say it is "stupid" or "dumb"?  It may have a very different meaning. Maybe try to ask in feedbacks "what kind of dumb it is? Using word clouds to see word representation in feedback forums. 

    • Advanced variant:

      • ‚ÄčMachine learning will collect all feedback on all possible feedback sites, where it will evaluate patterns, semantics and pragmatics of language.

      • Using Discursive force (hello surprise mechanics) and then measure its impact on text.

      • Experienced language researcher analyze data with software as atlas.ti.

      • Somebody will dive deep into the community (community manager for example) and will explain to the team what are players talking about. 

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