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The Quantic Motivation Model and 9 Gamer Types
This is the third and (potentially) final part of a series exploring how we can better talk about types of fun and categories of gamers.
Previously we looked at:
The 8 Kinds of Fun: Improving our vocabulary to describe fun by Marc LeBlanc and others.
The Bartle Taxonomy of Player Types: Grouping players into quadrants based on work by Richard Bartle.
This week, we are looking at the Quantic Gamer Types! Don’t miss the poll at the end!
The Gamer Motivation Model
Quantic Foundry researchers Nick Yee and Nicolas Ducheneaut have used social science and data science to analyze data from over 1.25 million gamers. This has allowed them to develop an empirical model of (video) gamer motivations.
Their research led them to identify six key motivations:
Action: Explosions and chaos! Some players want fast paced destruction and excitement.
Social: Whether its head-to-head matches or being on a team, socially motivated players are looking for competition and community.
Mastery: Tough decisions and high difficulty drive mastery focused players. They seek challenge and strategy.
Achievement: Get all the loot, unlock all the missions, and get a 100% rating. Achievement motivated players are looking for completion and power.
Immersion: Deep plots and interesting characters engage immersion motivated players. They are looking for fantasy and story.
Creativity: Self-expression and exploration are important to creativity motivated players. They thrive on design and discovery.
It’s worth noting that some of these motivations clearly match quadrants of the Bartle Taxonomy. Socializers vs. Social and Achievers vs. Achievement are almost direct matches. Explorers vs. Creativity also are very similar.
It’s interesting that while the Bartle Taxonomy was based on people playing MUDs in the 1990s and the Quantic Motivations are based on current gamers, the motivations remain largely the same.
The 9 Quantic Gamer Types
The Quantic motivation data can also be sliced and grouped in a different way.
Based on the hundreds of thousands of gamers who have taken the Gamer Motivation Profile survey, Quantic used their motivation scores to try to develop specific profiles of gamers.
Their analysis grouped the profiles into 9 Gamer Types:
Acrobat (Challenge + Discovery): Reflex and twitch based gamers who like repetition. They don’t need fancy world-building to have a good time.
Gardener (Completion): Zero stress grinding. No desire for deep strategy or hard decisions. Mining ore and chopping trees all day long.
Slayer (Fantasy + Story + Destruction): Narrative focused games in which they are the main character. The game doesn’t need to be fast-paced, as long as they are the hero.
Skirmisher (Destruction + Competition): Team games and limited social interaction. Hopping into a quick FPS match with friends is the video game version of this.
Gladiator (Challenge + Completion + Community): Similar to the Slayer, but looking for more depth. Skill trees, strategic thinking, and a larger world to explore.
Ninja (Competition + Challenge): Very similar to the Acrobats but more about internal skill vs. winning the game.
Bounty Hunter (Destruction + Fantasy): The open world version of the Slayer.
Architect (Strategy + Completion): Players who want to build something that lasts. Progression, strategic decisions, and full control over the world.
Bard (Design + Community + Fantasy): Power doesn’t matter to the Bard. They want social interaction and to shape the world of the game.
I’ve made an attempt to match these gamer types to their underlying motivations in the chart above. Six of them fit fairly well onto the chart. Other gamer types, such as the Bard (Design + Community + Fantasy) are harder to visualize on a 2D matrix.
The same is true with the Quantic gamer types:
Gamers who have a single, dominant gamer type are assigned a primary type (e.g., Architect), whereas gamers who have a noticeable secondary type are assigned a blended gamer type (e.g., Architect / Bard). Think of a blended gamer type as a primary color that leans towards another color–like a Blue that leans towards Green and results in a Turquoise.
With blended types (e.g. Ninja/Slayer) there are now even more categories to use. How many categories can be made before they become less useful?
Comparison to other models
In the Bartle Taxonomy post I mentioned how all models are just abstractions of reality, and yet each can be useful:
I’ve always thought that to be a wonderful way of thinking about models! You can never make a perfect model or abstraction of reality (c.f. gemba), but that doesn’t mean those models and abstractions are worthless.
This is, of course, true for the Quantic Foundry Motivation and Gamer Type models as well. Are they perfect? Certainly not. But they provide additional vocabulary and nomenclature to explore how players interact with games, and to investigate what motivates them.
Here’s one way to approach the models:
The 8 Kinds of Fun is perhaps the most simple and generalized model. Based on theory rather than piles of empirical data. Its application to both tabletop and video games is clear.
The Bartle Taxonomy is slightly more complex, is empirically derived to some extent, but relies on player data that is now almost 30 years old. While based on early video game players, it seems to work for tabletop games just as well.
The Quantic Gamer Types is the most complex and is built using large amounts of empirical data. It’s probably the most video game specific model of the three, and yet there are still applications for tabletop game design.
My personal thought is that there are three major themes that show up across all the models, regardless of method. Let’s give them a fancy title like The Skeleton Code Machine System of Player Nomenclature:
Those who play to win, get the high score, master the game, and love the competition. Socializing, theme, and story are all secondary to this.
Those who want to explore, discover, immerse themselves in fantasy.
Those who want to socialize, talk to others, interact with humans, and the game is just a method to do that.
These motivations/types have existed for at least the last 30 years, and have probably existed from the beginning of the very first games.
Some things to think about:
Countless ways to view players: I’ve covered just three approaches to understanding players, but I’m sure there are a thousand more. There are probably diminishing returns on digging too deep, so pick a model and run with it.
Models are an abstraction of reality: There is only one gemba (現場), the actual place where the action and play happen. Models can help categorize data, and can aid in development. No model can replace watching players play the game, engaging in playtesting, and talking directly to people.
Ask the question: Paraphrasing a bit, Richard Bartle asked players, “What do you want from this game?” I think that’s a fascinating question, and one worth asking. It might help get to the core of what a game’s design should be.
This week’s poll asks which player model you think is the most helpful. Remember, all models are wrong, but perhaps one of them is better than the others. Which one do you think is the best?
— E.P. 💀
P.S. Exeunt Press will have a vendor booth at Save Against Fear in November!
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