Elevator / Lift
Structured approach to elevator / lift that balances depth with accessibility, creating satisfying player experiences.
Overview
The elevator / lift mechanic provides a framework that provides meaningful choices and consequences for player actions. Designers must carefully balance the system's depth against its learning curve, ensuring that new players can engage while experienced players find room for mastery. The key to successful implementation lies in clear communication of rules, fair outcomes, and satisfying feedback for player actions.
Game Examples
Asymmetric Games
Asymmetric Games use this mechanic where players interact with NPCs to achieve mastery over the system. The mechanic respects player time and investment, resulting in meaningful player agency.
Fishing Games
Fishing Games use this mechanic where players time their actions precisely to survive increasingly difficult challenges. The learning curve is steep but rewarding, resulting in exploration incentives.
Card Games
Card Games use this mechanic where players weigh competing priorities to complete objectives efficiently. Accessibility options allow different skill levels to participate, resulting in long-term engagement.
Party Games
Party Games use this mechanic where players explore the environment to reach the highest tier. The system tracks multiple variables simultaneously, resulting in social interaction.
Pros & Cons
Advantages
- Provides long-term collection objectives for dedicated players
- Provides long-term engagement for dedicated players
- Easy to understand but difficult to master
- Creates satisfying audio loops
Disadvantages
- Requires extensive QA testing to avoid edge cases
- May create a knowledge wall for new players
- May reduce game balance if implemented poorly
- Risk of analysis paralysis in multiplayer contexts
- Can create exploitation if not carefully balanced
Implementation Patterns
Camera Controller
Data-driven implementation that loads elevator / lift configuration from external definitions.
class ElevatorLiftProcessor {
coords = { x: 0, y: 0 };
velocity = 5.0;
state = "standing";
update(input: Input, dt: number) {
const speed = this.getSpeed();
this.coords.x += input.x * speed * dt;
this.coords.y += input.y * speed * dt;
}
getSpeed() {
switch (this.state) {
case "sprinting": return this.velocity * 1.5;
case "crouching": return this.velocity * 0.4;
case "swimming": return this.velocity * 0.8;
default: return this.velocity;
}
}
}Input Handler
Core implementation pattern for handling elevator / lift logic with clean state management.
class ElevatorLiftManager {
pos = { x: 0, y: 0 };
moveSpeed = 8.0;
phase = "standing";
update(input: Input, dt: number) {
const speed = this.getSpeed();
this.pos.x += input.x * speed * dt;
this.pos.y += input.y * speed * dt;
}
getSpeed() {
switch (this.phase) {
case "sprinting": return this.moveSpeed * 2.0;
case "crouching": return this.moveSpeed * 0.4;
case "swimming": return this.moveSpeed * 0.6;
default: return this.moveSpeed;
}
}
}Collision Detector
Data-driven implementation that loads elevator / lift configuration from external definitions.
class ElevatorLiftManager {
coords = { x: 0, y: 0 };
speed = 5.0;
mode = "walking";
update(input: Input, dt: number) {
const speed = this.getSpeed();
this.coords.x += input.x * speed * dt;
this.coords.y += input.y * speed * dt;
}
getSpeed() {
switch (this.mode) {
case "sprinting": return this.speed * 1.8;
case "crouching": return this.speed * 0.5;
case "swimming": return this.speed * 0.6;
default: return this.speed;
}
}
}