Graph View

Backlinks

The Library

Search

SearchSearch

Game Balancing

Sep 25, 2024, 8 min read

  • The principles outlined here are helpful for balancing games since games are systems.

    • Use slow and durable negative feedback loops to balance positive feedback.

  • Generally we can either Playtest the game or simulate the game to gather data. Analytical tools such as design patterns also help in analysis.

  • It is often easier to perform the game balancing if the randomness mechanisms are removed. This helps detect dominant strategies.

  • Testing for specific strategies and player roles is a good way to understand the viability of these strategies during play .

  • If you have to spend a lot of time trying to make artificial players that successfully control your economy, that’s usually an indication that your economy has a problem

  • Run experiments that aim to answer specific balancing questions.

Balancing Principles

Fairness

  • Players want to feel that the forces working against them do not have an advantage that will make them impossible to defeat
  • Symmetric Games - players have equal powers and resources. Suitable for Competitive games.
    • Balancing necessitates considering first-move advantage (either random or least skilled first)
  • Asymmetrical Games - players have different resources and abilities. We may want this for a variety of reasons
    • To simulate a real world experience
    • To allow for exploration
    • To allow personalization by giving players resources that match their skills.
    • To level the playing field.
    • To create interesting situations
  • One way to balance an Asymmetrical game is to give players the same total “sum value” of resources, with each resources having the same values.
  • Another way is circular balance where there is a rock-paper-scissors dynamic. Something exists as a counter to something else.
  • Relevant Lenses: Fairness,

Challenge vs Success

  • The appropriate level of challenge keeps the player in flow.
  • Some common techniques for balancing:
    • Increase difficulty with each success. However, consider the tense and release pattern
    • Let players get through easy parts fast.
    • Create layers of challenge (for example via a scoring system)
    • Let players choose the difficulty level (this comes at the cost of balancing more versions of the game)
    • Playtesting with a variety of players.
    • Give the losers a break
  • It may make sense to consider what percentage of players do you want to complete the game.
  • Remember: Just learning the game is already a challenge
  • Relevant Lenses: Challenge,

Meaningful Choice

  • Games should have meaningful choices.
  • Dominant strategies ruin game balance and make the game less fun. Balancing should get rid of dominant strategies
  • The number of choices provided to the player should equal the number of things the player wants to do — too much and its overwhelming, too little and the player is frustrated.
  • Consider triangularity - the classic trade-off of low risk low reward and high risk high reward.
  • Relevant Lenses Meaningful Choices, Triangularity

Skill vs Chance

  • Chance and Player skill negate each other.
  • One common way to balance these is to alternate the use of chance and skill in the moment to moment gameplay.
  • According to David Perry, the key to addictive games is designing the game such that players are doing three things
    • Exercising a skill
    • Taking a risk
    • Working a strategy
  • Relevant Lenses: Skill vs Chance

Heads vs Hands

  • Consider how much of the game should involve doing a challenging physical activity vs how much it should involve thinking, again taking into account the preferences of the target demographic.
  • Relevant Lenses: Heads and Hands

Competition vs Cooperation

Short vs Long

  • Balance should take into account the length of the gameplay
  • Too short and choices will not be meaningful enough.
  • Too long and the game may feel like too much of a time commitment.
  • The main factors that affect the duration of the game are the win conditions.

Rewards

  • Players are motivated by a certain desire that can be fulfilled by the game.
  • Games judge players, but they should judge them fairly.
  • The following are some examples of rewards: We balance the rewards by controlling which are given out and when.
    • Rule of thumb: More rewards = better
    • Rewards have diminishing returns. People have a tendency to get acclimated to rewards the more they receive them.
    • Favor variable rewards over fixed rewards.
    • Rewards are generally a better tool for reinforcement than punishment. If you need to encourage something, use a reward.
  • Relevant Lenses: Reward

Punishment

  • The following show examples of punishments.

  • Be careful with setting punishments in game. Balance them by making them reasonable.

    • Punish the player for things that they are able to understand and prevent Otherwise, the player will feel like they are not in control.
    • Punishments that are unfair have their place for players who are motivated by challenges. However, even these players must be able to see how they can prevent the punishment.

  • A combination of light punishments is enough to encourage certain behaviors.

  • Relevant Lenses: Punishment

Freedom vs Controlled Experience

  • There is a trade off in giving the player freedom of choice. Not only that, but it is extra work for the designers in giving the player more options.
  • Consider where to give the player freedom and how much freedom to give.

Simple vs Complex

  • There are two kinds of complexity in a game:

    • Innate Complexity - when the very rules of the game are complex. Generally bad because it makes the rules convoluted.
      • If there are a lot of exceptions to the rule, then there is innate complexity.
    • Emergent Complexity - when simple rules give rise to complex phenomena. Emergence is good.

  • There are two kinds of balancing

    • Artificial Balancing - adding more rules and more innate complexity to balance the game.
    • Natural Balancing - the desired effects emerge naturally from the game.

  • Elegance arises from simple systems that perform robustly in complex situations. Elegant game elements are those that serve many purposes.

    • Whenever possible, favor removing something or using something that already exists than adding something new.

  • Elegance is balanced with character. Too much elegance makes the game too simple. Adding character entails adding lovable, quirky elements that make the game unique.

    • Elegance is simplicity. Character is complexity. Both are needed.

  • Relevant Lenses: Simplicity and Complexity, Elegance, Character

Detail vs Imagination

  • Decide exactly what details should be provided, and which should be provided to the player’s imagination

  • Some tips or how to do it well:

    • Only detail what you can do well. When you cannot detail it in high fidelity, use the player’s imagination to do the detailing for you.
    • Give details the imagination can use - Give details that the player can easily understand. This serves to introduce knowledge that is easily parsed.
    • Familiar things do not need much detail. The imagination cannot help when the player is not familiar with the setting.
    • The binocular effect. Use a little detail to get a lot of imagination. Briefly show a detailed view then zoom out and let the imagination take care of the rest.
    • Give details that inspire imagination. Appeal to the player’s fantasy.

  • Relevant Lenses: Imagination

How to Balance a Game

  • Use the lens of the problem statement. Balancing is a design issue.

  • Double and halve. That is, narrow down the parameters of the game by doing a binary search. Push the values farther than what intuition tells us.

    • Change something so that you can feel the difference right away.

  • Train your intuition by guessing exactly.

  • Document the model Write down what you think the relationships are between the things being balanced. This helps clarify things and gives a framework to record results of balancing.

  • Tune the model as you tune the game. Essentially, follow the scientific method.

  • Plan to balance. Put systems that make it easy to change the values you expect to balance. A variation of iteration.

  • Most of the time, the players shouldn’t balance the game. The players are biased— they want to win and poor balance may help with that.

    • However, take into account player feedback.

  • Beware the pitfalls of dynamic game balancing — that is, difficulty that adjusts to the players. This is not to say it is unviable, but that it comes with its own challenges.

    • It spoils the reality of the world. If the abilities of their opponents are relative to the player, then the illusion that they are fixed challenges to be mastered disappears .
    • It is exploitable. If the game gets easier when they play badly, they may just play badly to make the game easy.
    • Players improve with practice.

  • Relevant Lenses: , Economy Balance

Links