Project 8: Platformer Mechanics and Feel Tuning

A platformer movement lab with live tweakable acceleration, friction, gravity scales, and camera damping.

Quick Reference

Attribute Value
Difficulty Level 3
Time Estimate 2 to 3 weeks
Main Programming Language C# (.NET 8) + MonoGame
Alternative Programming Languages F#, C++ (raylib), Godot C#
Coolness Level Level 4
Business Potential Level 2
Prerequisites Deterministic loop basics, debugging discipline, content pipeline fundamentals
Key Topics Feel metrics, Telemetry driven tuning, Camera and motion comfort

1. Learning Objectives

  1. Translate one concrete production question into a testable implementation plan.
  2. Implement and validate the feature in a MonoGame runtime context.
  3. Instrument success and failure paths with actionable diagnostics.
  4. Produce a repeatable demo artifact for portfolio or interview use.

2. All Theory Needed (Per-Concept Breakdown)

Feel metrics

Fundamentals Feel metrics is central to this project because it defines the non-negotiable behavioral contract for the feature. You should be able to describe valid inputs, legal state transitions, and expected outputs under normal and failure conditions.

Deep Dive into the concept Treat Feel metrics as a boundary-setting mechanism. Start by defining the smallest deterministic scenario that proves the feature works. Stress that scenario under altered timing, altered content inputs, and altered user actions. If behavior changes unexpectedly, document hidden coupling and sequence assumptions. Keep transitions explicit and observable via logs or debug panels. Connect each transition to an event record so regression analysis is possible after refactors.

Telemetry driven tuning

Fundamentals Telemetry driven tuning ensures the project scales from local prototype behavior to repeatable system behavior.

Deep Dive into the concept Use Telemetry driven tuning to reason about data flow ownership and mutation timing. Document where writes occur, when validation runs, and how rollback behaves if a write fails.

Camera and motion comfort

Fundamentals Camera and motion comfort connects this project to shipping reality by forcing you to think about operational constraints early.

Deep Dive into the concept Define one production-like failure mode related to Camera and motion comfort and build a mitigation checklist. The solution is complete when you can demonstrate both a golden path and a controlled failure path.

3. Project Specification

3.1 What You Will Build

A game-feel laboratory scene with live movement tuning controls, benchmark tracks, and saveable tuning presets.

Visible game deliverable:

  • Side-view test room with ramps, gaps, and moving platforms
  • Live sliders for acceleration, friction, gravity, jump cut, camera damping
  • Metric panel for apex time, stop distance, input latency

3.2 Functional Requirements

  1. Expose movement constants through live-adjustable controls.
  2. Record benchmark metrics over repeated test runs.
  3. Save and load tuning presets with deterministic labels.
  4. Visualize trajectory and velocity traces for quick comparison.

3.3 Non-Functional Requirements

  • Performance: Must remain inside project-appropriate frame budget.
  • Reliability: Must recover from at least one injected failure mode.
  • Usability: Outcome must be observable by a reviewer in under two minutes.

3.4 Example Usage / Output

[FEEL] apex_ms=412 stop_px=53 latency_ms=28
[FEEL] preset=TournamentCandidate saved
[FEEL] benchmark_run id=7 score=PASS

3.5 Data Formats / Schemas / Protocols

  • Event record: {timestamp, module, action, result}
  • Feature state snapshot: {version, state, counters, flags}

3.6 Edge Cases

  • Extremely low friction leading to infinite drift.
  • Very high gravity causing collision overshoot.
  • Preset load with missing/invalid fields.

3.7 Real World Outcome

This is a game-facing outcome you can see and play immediately.

What you will see in the game window:

  • Side-view test room with ramps, gaps, and moving platforms
  • Live sliders for acceleration, friction, gravity, jump cut, camera damping
  • Metric panel for apex time, stop distance, input latency

Project 8 Platformer Mechanics and Feel Tuning Window Mockup

How you interact:

  • A/D move
  • Space jump
  • Tab toggles slider panel

3.7.1 How to Run (Copy/Paste)

$ dotnet restore
$ dotnet build
$ dotnet run --project src/Game -- --scene feel-lab

3.7.2 Golden Path Demo (Deterministic)

  1. Start the scene and confirm all HUD panels load.
  2. Perform the three core interactions listed above.
  3. Verify the success signal appears without warnings.

3.7.3 If CLI: exact transcript

$ dotnet run --project src/Game -- --scene feel-lab
[FEEL] apex_ms=412 stop_px=53 latency_ms=28
[FEEL] preset=TournamentCandidate saved
[FEEL] benchmark_run id=7 score=PASS

3.7.7 If GUI / Desktop

+------------------------------------------------------+
| feel-lab                                   [F1 HUD] |
|------------------------------------------------------|
| PLAYFIELD: gameplay objects and interactions         |
| HUD: key metrics + status badges                    |
| STATUS: success/failure cues and prompts            |
+------------------------------------------------------+

4. Solution Architecture

4.1 High-Level Design

Input -> Movement Integrator -> Collision -> Camera -> Metrics Recorder -> Preset Store

Input -> Movement Integrator -> Collision -> Camera -> Metrics Recorder -> Preset Store

4.2 Key Components

Component Responsibility Key Decisions
FeelParameterSet Stores tunable movement/camera values Versioned preset format
BenchmarkRunner Executes repeatable movement trials Consistent start conditions per run
FeelHUD Displays and compares movement metrics Immediate feedback on parameter changes

4.4 Algorithm Overview

  1. Validate preconditions.
  2. Apply deterministic transition.
  3. Emit feedback and telemetry.
  4. Persist if required.

5. Implementation Guide

5.3 The Core Question You’re Answering

“How do you tune movement scientifically instead of by guesswork?”

5.4 Concepts You Must Understand First

  1. Feel metrics
  2. Telemetry driven tuning
  3. Camera and motion comfort

5.5 Questions to Guide Your Design

  1. Which movement metrics matter most for your game genre?
  2. How will you compare presets objectively over multiple trials?
  3. What safety limits prevent invalid tuning values?

5.6 Thinking Exercise

Trace one full success path and one failure path on paper before implementation.

5.7 The Interview Questions They’ll Ask

  1. Why did you pick this architecture boundary?
  2. Which failure mode did you prioritize first and why?
  3. How does your instrumentation accelerate debugging?
  4. How would you scale this feature to a larger game?

5.8 Hints in Layers

  • Hint 1: Stabilize one invariant before feature expansion.
  • Hint 2: Add diagnostics before optimization.
  • Hint 3: Keep platform calls at system boundaries.
  • Hint 4: Re-run deterministic scenario after each refactor.

5.9 Books That Will Help

Topic Book Chapter
Core concept “A Theory of Fun for Game Design by Raph Koster” Relevant concept chapter
Reliability “Release It!” Failure handling chapters
Architecture “Clean Architecture” Boundary and dependency chapters

6. Testing Strategy

  1. Golden path completes and emits success signal.
  2. Injected failure path recovers without crash.
  3. Re-run scenario after restart and confirm consistency.

7. Common Pitfalls & Debugging

  • Hidden initialization order coupling
  • Time-coupled behavior tied to render rate
  • Missing fallback behavior on platform call failure

8. Extensions & Challenges

  • Beginner: add one extra diagnostics panel metric.
  • Intermediate: add replay capture for event flow.
  • Advanced: add automated stress test harness.

9. Real-World Connections

This project mirrors shipping feature-module work in real indie and mid-size game teams.

10. Resources

  • Steamworks official docs
  • MonoGame docs
  • Gemini image generation docs (for asset-related projects)

11. Self-Assessment Checklist

  • I can explain the feature invariant and prove it in a demo.
  • I can trigger and handle one deterministic failure scenario.
  • I can describe tradeoffs and future scaling choices.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Feature works in deterministic golden path.
  • One controlled failure path is handled gracefully.
  • Core diagnostics are visible and documented.

Full Completion:

  • All minimum criteria plus edge-case coverage and regression checks.

Excellence:

  • Includes polished instrumentation and clear productionization notes.