Design Rubric Map of Shooter Games

By | 2016-10-17T20:02:32+00:00 September 8th, 2016|Analytics, Video Games|6 Comments

Here at Quantic Foundry, we’re always experimenting with new ways of visualizing and making sense of the data we have collected via the Gamer Motivation Profile. In this post, we’ll describe a Design Rubric Map that we’re working on.

If you’re a regular reader of our blog and familiar with how we collected our data, feel free to skip the next section.

Data from the Gamer Motivation Profile

The Gamer Motivation Profile allows gamers to take a 5-minute survey to get a personalized report of their gaming motivations, and see how they compare with other gamers. Over 239,000 gamers worldwide have taken this survey. The 12 motivations that are measured in our model were identified via statistical analysis of how gaming motivations cluster together.

See how you compare with other gamers. Take a 5-minute survey and get your Gamer Motivation Profile.

In the Gamer Motivation Profile, we also ask gamers to list some games they’ve enjoyed playing. This allows us to generate motivation profiles not only for individual gamers, but also for game titles. We do this by aggregating the motivation profiles from the gamers who listed that game as a game they enjoy.

The Meaning Behind Variance

When we create genre maps, such as these for the Shooter genre in June, one basic way to identify the most salient motivation axes is to pick the two motivations with the highest standard deviations. These are the motivations that vary the most among gamers (in that particular genre space).

But there’s a more important meaning behind these variances. When a motivation’s variance is low:

  • The audience is very similar in terms how much they care about it and its related game mechanics.
  • It is easy to achieve broad coverage because of the narrow audience spread.
  • The risk of alienating gamers is very low.
  • From a game design perspective, these motivations are the core design pillars and “must-haves” of the genre.

When the variance is low, it is easy to achieve broad coverage because of the narrow audience spread.

On the flip side, when a motivation’s variance is high:

  • The audience varies a lot in in terms of how much they care about it and its related game mechanics.
  • It is more difficult to achieve broad coverage because of the audience spread.
  • There is a higher risk of alienating gamers who fall outside the coverage area.
  • From a game design perspective, the dev team has to make tough choices about how they want to differentiate their game and what audience they want to target.

The Design Rubric Map

To generate the Design Rubric Map of shooter gamers, we sampled all the gamers (~40k) who mentioned at least one of the game titles from the earlier post on shooter games.

The standard deviation only captures part of the picture though. A motivation may have low variance both because it is consistently very important or very unimportant in the genre. To visualize importance as well as variance on the same map, we can plot the average score of the motivations against their standard deviations. Note that the 50%-tile on the vertical axis reflects the average score among our baseline gamer sample.

2016-09-07-rubric-map-by-gamers-shooters

As we mentioned, the average score of motivations conveys the importance or appeal of that motivation. And the standard deviation conveys the coverage risk. Let’s relabel the axes and highlight some interesting zones of the graph.

2016-09-07-rubric-map-by-gamers-shooters-02-overlayed

Core Features: In the left-most zone are the motivations that have the most consistent scores among Shooter gamers. They are low risk in terms of game design because it is easy to achieve broad coverage of the audience. These motivations are typically the established and expected pillars of the genre. Here in the Shooter genre, the 3 motivations in this zone also score high on importance. Gamers expect Shooter games with lots of Destruction (guns, chaos, mayhem), Excitement (fast-paced, action, surprises), and Community (being on team, chatting, interacting). But this also means there is a great deal of constraint on the design decisions because there is a highly formulaic expectation of these motivations. For example, a turn-based Shooter without guns/bombs would alienate most Shooter gamers.

Safe Bets: In the middle zone are motivations with a moderate amount of variance. It’s still relatively easy to achieve decent audience coverage, and there is now more room for design choices and differentiation. For example, Power is about leveling up and making progress, and there is a moderate range of design possibilities around these game mechanics (i.e., complexity of character levels and weapon upgrades) that would still achieve decent coverage. Thus, even though Competition (duels, matches, leaderboard) is the most important motivation in the genre, its moderate variance means that there is more leeway around design choices than the motivations in the Core Features zone. In other words, games like Counter-Strike (higher on Competition) and Payday (lower on Competition) can both appeal to subsets of gamers in the Shooter audience.

Risky Options: In the right-most zone are the motivations with the most variance. Thus, there is a wider range of viable design options, but in every opportunity space, there is a shallower pool of gamers and a higher risk of alienating gamers who fall outside of that coverage area. There are two general approaches for attacking this space. The safer approach would be to create optional features that cater to a wider slice of the audience. For example, the game has a light narrative, but all the cut scenes and dialogue are easy to skip without impacting gameplay. The riskier approach would be to take a strong stance on one or more of these motivations to create a clear differentiator and target a more niche audience.

Other Ways of Slicing

This is still very much an approach we are experimenting with, so let us know if you spot any other interesting ways to slice the map, or other interpretations of the zones.

Let us know if you spot any other interesting ways to slice the map.

About the Author:

Nick is the co-founder and analytics lead of Quantic Foundry. He combines social science and data science to understand gamer behavior in large-scale game data.

6 Comments

  1. Mark Terrano September 9, 2016 at 5:28 pm - Reply

    I’m interested if the data breaks down differently for the console audience vs the PC audience (If I recall your dataset is specifically focused on PC gamers) and how it varies by gender – especially the role of character, fantasy and story and discovery.

    Having relevant data to inform design decisions is so important – thanks to you, the staff and the researchers for publishing this information.

    • Mark Terrano September 9, 2016 at 5:30 pm - Reply

      (Went back and looked at your survey results) Given that you have Console results would love to see this broken out by Console and PC gamers to see if (and where) they differ.

    • Nick Yee September 10, 2016 at 1:19 pm - Reply

      Hi Mark – We can definitely slice this by gender, age, and platform using the underlying data. If you’d like to chat more about this, you can reach us at team AT quanticfoundry.com

  2. Corey Zaro September 11, 2016 at 10:30 am - Reply

    What about having diagonal zone barriers in order to include competition with the core features and make a clearer distinction between the safe bets and risky options?

    I look forward to seeing this data sliced by gender, age, and platform.

  3. Aaron Cammarata September 13, 2016 at 7:31 am - Reply

    Hi N&N –

    If I’m understanding this correctly, then there’s really a whole different story here – there’s a horizontal segmenting as well, which is not seen here.

    Specifically: low Z-score, low SD features are those the audience consistently does not like in that genre.

    Correct?

    • Nick Yee September 13, 2016 at 11:22 am - Reply

      That’s correct. The vertical axis is essentially the percentile rank information we provide in the audience reports (with the bar graph format). So when we were considering the possible overlays for this map, we considered a quadrant-style overlay as well, where for example:

      – Low Var / High Importance: “Must-Have”
      – Low Var / Low Importance: “Must Avoid / Deprioritize”

      But in this particular example, there wasn’t anything in the bottom-left and top-right quadrants, so we decided to go with a different overlay instead. I’m still chewing on the quadrant version and other potential overlays.

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