Design: Player Psychographic Profiles (Part 2)

In the second article about Player Psychographic Profiles, Chip Beauvais begins to analyse each of the individual profiles. The previous article can be found here.

So, you’re designing a game. Along the way, you will be faced with many options, and you’ll want to pick the one that makes your game better. Which one to choose won’t always be clear, but here are some questions that will help you decide.1. Who is the game for?
2. What are those players like?
3. What do those players like?
4. How do I create those experiences?


I’d like to explicitly reference the foundation of these prototypes: the original Player Psychographic profiles used by Wizards of the Coast in designing Magic: the Gathering. If you’re familiar with Timmy, Johnny, and Spike, you’ve probably already noticed how they inspired the profiles listed here. If you’re not familiar with the profiles, here’s an excellent place to begin:

Thanks to Mark Rosewater (@maro254) for discussing these ideas with me, and to Jon (@RunAGame) for bringing this omission to my attention.

Profiles and Game Design Concepts

Any framework is only valuable to the extent that it can be applied to your game design for additional insight. In this article we’ll dig deeper into the profiles. We’ll introduce a few game design concepts that are particularly relevant for that profile. I’ll also suggest questions for your playtest feedback form.
The profiles fall into 3 main groups, based on what motivates them.

  • Erin and Ingrid are driven by the goal of the game.
  • Jenny and Kim are driven by the mechanics of the game.
  • Anastasia and Leah are driven by the play experience.

In this article, we’ll start by examining the play preferences of Erin and Ingrid, our goal-driven profiles. As designers, we have direct control over the goal and mechanics of the game. Thus, it’s easier to anticipate the reaction of (and thus, design for) the first four types of players.


Designing for Erin the Competitive

Short version: Erin
Concepts: Lenticular Complexity, Return-To-Skill, Balance and Dominant Strategy

Remind me who Erin is again?
Erin enjoys the challenge of making better decisions than her opponents. She formulating a better strategy and executing it perfectly. If she’s defeated by a better strategy (or better execution), she’ll adopt the victor’s approach in the next game. Improving at a game is thrilling and rewarding.

What is lenticular complexity?
Lenticular complexity is initially hidden to new players, but can be revealed by careful consideration over multiple plays. Erin wants to master the game by developing strategies and heuristics which she can tweak and perfect over the game’s lifetime.

Consider Star Realms. New players use the Explorer card as an upgraded Scout – it gives twice as much trade, and is therefore worth keeping in your deck. Experienced players also consider whether to trash the Explorer after playing it to get the additional power. This requires considering the overall strength of the rest of your deck, and how close the game is to the end. Veteran players realize that buying an Explorer doesn’t risk giving your opponent an opportunity to purchase more powerful cards than those already in the center row. (Reference: this BGG thread: )

By hiding the complexity of the game, new players are less likely to be overwhelmed by the game, and are more likely to remain in the player pool for longer. This gives Erin the benefit of a consistent supply of less-skilled players to defeat.

What is return-to-skill?
“Return-to-skill” is a concept described in Characteristics of Games. Imagine dividing players into ascending groups, where each player in a group is likely to defeat any player in a lower group. “The length of the skill chain [measures] return to skill: how much a player can hope to leverage whatever skills she may have in order to achieve victory.”

This is the most important quality that a game can have for Erin. The more skilled player must win more often. Once defeated by a player she deems inferior, Erin won’t play the game again. In this case, she either has to change her view of her opponent (“She’s better than I thought”), or her view of the game (“Turns out, this is just a luck-fest.”).

Consider San Juan. If Erin considers the Guild Hall strategy dominant, and loses to a player who is using the City Hall strategy, she will have to do one of the following to resolve her cognitive dissonance:

  1. Reconsider which strategy is actually better.
  2. Dismiss San Juan as too luck-based.

Note: If this percentage is too high (e.g. the more skilled player always wins), then the game may have difficulty retaining new players long enough for them to master the game. Also, having mastered the game, most players (not Erin) will lose interest and move on. But, Erin doesn’t care about these consequences (even if the available opponent pool is decreased as a result).

Organized play makes it easier for Erin to find opponents at the right level. When the rest of her gaming group collectively decides not to play this game with her anymore (because she always wins), Erin will either need to find new opponents or move on to another game.

What is a dominant strategy?
There are two different types of joy that a game can provide. The initial joy is one of discovery – finding new strategies, trying them out, and tweaking them as necessary to squeeze every ounce of advantage out of them.

The other type of joy is the one of victory. Having mastered the game, Erin plays it whenever possible against the best opposition she can find. Going to larger conventions and tournaments is an excellent opportunity to flex these muscles and show off her skill.

Discovering a dominant strategy doesn’t ruin a game for Erin, it simply changes the type of joy she gets from playing the game.

Consider a two-player fighting game, such as Mortal Kombat. Within some game groups, certain strategies or combinations will be identified as over-powered, broken, or “cheesy”. Some players will agree not to use these strategies, even though they are perfectly valid within the context of the game. Erin, on the other hand, sees these strategies as perfectly acceptable.

Consider Magic: the Gathering. Erin is the player who copies the decklist that placed in the top 8 in last week’s Pro Tour Qualifier, and brings it to the local game shop for Friday Night Magic. She hopes that other players will do the same.

Consider A Few Acres of Snow. Once a dominant strategy (the Hallifax Hammer) was discovered, some players acknowledged that the strategy existed, and simply agreed to not use that strategy (as it was overpowered). Erin, in this situation, would consider that behavior bizarre, and would continue to use the dominant strategy as long as it led her to victory.

What should you ask in your playtest report?
Does this game seem to have depth? Do you feel like there is more to learn? (Lenticular Complexity)

How often would you expect to win against a less experienced player? How often would you expect to lose to a more experienced player? (Return to Skill)

Is there a strategy that seems more powerful than others? What strategy did you use? What strategy would you use next time you play? (Balance/Dominant Strategy)


Designing for Ingrid, the Optimizer

Short version: Ingrid
Concepts: Leader-Anointing, Output Randomness, Board Stability

Remind me who Ingrid is, again?
Ingrid approaches every game as a puzzle to be solved, or as a competitive math problem. The player who solves the puzzle best should win.

What is Leader-Anointing?
Be careful of direct player interaction, especially where one player can cause other players to lose (or to win). If the acting player cannot actually win, but can determine the winner (or loser), this is commonly known as Kingmaking, but I’ll refer to it as Leader-Anointing (thanks Matt Wolfe). As Ingrid wants the (objectively) best player to win, she detests Leader-Anointing, even if she is selected as the winner. Unlike Erin, winning the game isn’t as important to Ingrid as seeing the best player declared the victor.

There are a few ways to eliminate Leader-Anointing from your design. You can limit player’s ability to affect their opponents. This may cause some people to describe your game as “multiplayer solitaire”, and may turn off players like Leah, who want to interact with the other players, and Erin, who wants to be able to stomp her opponents. Ingrid, however, doesn’t require player interaction for the games she enjoys.

You can also solve the Leader-Anointing problem by controlling the timing of the endgame. Before any player feels that he or she is effectively eliminated from the game, the game should end. This will prevent any player from being put in the Leader-Anointing position. Alternatively, if a player no longer has a shot at victory, find a way to eliminate that player from the game, so he or she can no longer affect the remaining players. However, player elimination causes other problems (such as, what does that player do while waiting for the rest of the table to finish) unless the end of the game is near.

What is Output Randomness?
While not everything unexpected is fun, most fun things are unexpected. This is part of the reason that warning someone of an upcoming plot twist “spoils” the fun of watching a movie. As such, a lot of fun games have unexpected, or random events. The timing of the random events can significantly impact how they are perceived by the players.

A random initial setup which doesn’t place any players at a significant disadvantage is fine for Ingrid, and may lend the game more replayability. Even mid-game randomness is fine, as long as it can be mitigated by clever play. But a late-game random element that has a large impact on a player’s chance to win (e.g. is “swingy”) will make Ingrid question why she put so much time and effort into the rest of the game.

Another aspect of random elements in your game is input vs. output randomness, to use terms coined by the Ludology podcast. For example, you could select 3 random integers for a, b, and c, and ask Ingrid to solve the equation ax^2 + bx + c = 0. That is, for Ingrid, an interesting puzzle. But after she solves the puzzle, if you roll a die to determine how many points she gets, this is output randomness, and not satisfying.

Consider Risk. In a single battle, the attacking player decides how many forces to commit, and then rolls the dice to see the outcome (output randomness). Imagine a variant in which a player rolls the dice first, and then decide how many forces to commit (and possibly lose). The outcome of the battle, while still randomly determined by the dice, would be within the player’s control.

Consider Killer Bunnies. Ingrid may have gathered more carrots than any other player (which she considers playing well), but when another player’s carrot is declared the winner, she will feel cheated out of a well-deserved victory.

What is board stability?
Any number of players are welcome at the table. However, for some games, more players means more change between turns, which can make it difficult to execute a strategy. Ingrid likes to plan ahead.

As long as downtime (time between turns) can be used to plan her next turn, Ingrid will be happy, and will not rush other players. However, if the game state changes significantly between turns, Ingrid will not bother wasting her time planning her next move. Limiting the degree to which players can affect each other will not only avoid the Leader-Anointing problem discussed above, but will also ensure that some of the time between turns can be used profitably for planning.

Consider Dominion: At the end of each turn, the player draws the next hand and starts planning. While there are still some unknown factors (what opponents will do, which cards you will draw), there’s enough information for a player to optimize their next turn while other people play.

Consider Ascension. In Dominion, there are (at least) 8 copies of each card in the supply at the start of the game. For most of the game, for most of the piles, players can expect to have the opportunity to purchase any card they like. In Ascension, players choose from a row of ever-changing cards. This makes the game feel more tactical, and less strategic.

Consider Alhambra. Ingrid has created a finely-tuned strategy that evaluates each tile based the number of walls, the amount of currency she has in hand, the VP value of that building, and her standing in that building relative to other players. However, with a full complement of  players, tiles will be revealed and purchased between turns. Her loss won’t be due to poor tile evaluation (suboptimal play), but because she didn’t get the opportunity to purchase the tiles she needed. For this reason, Ingrid might love Alhambra with 4 players, but detest it with 6.

What should you ask in your playtest report?
Was there too little, too much, or just the right amount of player interaction?

What had more of an impact on the outcome of the game: your choices, other player’s choices, or luck? Did the final result feel out of your control?

Were you able to plan ahead? If not, why not?

Next Time…

Next time we will consider the profiles of Jenny and Kim who are both are driven by the mechanics of the game.

About the author.

Chip Beauvais started designing games in 2006, and hasn’t been able to stop.
He has a masters degree in Mathematics from Tufts University.
He lives in Maynard, MA with his wife, daughter and two bunnies.

You can contact Chip via his website, or on Twitter @the_FlyingSheep

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