game-analysis-criteria

Game Analysis Criteria

Purpose

Framework for systematic reverse-engineering and analysis of external games to extract actionable insights for player retention, market positioning, and game design decisions.

Templates

Mechanics (per-mechanic reverse engineering)

• mechanics-overview-template.md - Map of all mechanics, core loop diagram, system interconnections
• mechanic-detail-template.md - Per-mechanic deep dive: states, triggers, formulas, balancing, edge cases

Analysis Reports

• technology-report-template.md - Engine, frameworks, networking, performance
• marketing-report-template.md - Marketing strategies, community engagement, influencer relations
• retention-report-template.md - Retention mechanics, monetization, live ops
• market-position-report-template.md - Competitive landscape, market share, audience
• ui-ux-report-template.md - UI/UX design, onboarding, accessibility
• game-feel-report-template.md - Polish, juice, feedback systems, audio design

Synthesis

• executive-summary-template.md - Final synthesis with actionable retention strategies

Storage Convention

All game research is stored per game under the user's project:

docs/game-research/
  {game-slug}/
    raw-research.md              # Raw data from game-researcher
    mechanics/                   # Per-mechanic reverse engineering
      overview.md                # sr-game-designer: all mechanics map, core loop
      {mechanic-slug}.md         # mechanics-developer: detailed per-mechanic analysis
    technology-analysis.md       # Engine, frameworks, performance
    marketing-analysis.md        # Marketing strategies, community
    retention-analysis.md        # Retention mechanics, monetization
    market-position.md           # Competitive landscape
    ui-ux-analysis.md            # UI/UX design analysis
    game-feel-analysis.md        # Polish, juice, feedback
    executive-summary.md         # Final synthesis with recommendations
    source-code/                 # Source code notes (if available)
      architecture.md
      patterns.md

Naming: {game-slug} = lowercase, hyphenated game name (e.g., hollow-knight, stardew-valley, clash-royale)

Analysis Framework

Seven Pillars of Game Analysis

Each game should be analyzed across these dimensions:

Pillar	Key Questions	Primary Agent
1. Core Loop	What is the fundamental gameplay cycle? How long is one loop? What drives repetition?	sr-game-designer
2. Progression	How does the player advance? What are the unlock gates? What creates the "one more turn" feeling?	sr-game-designer
3. Retention Mechanics	What brings players back? Daily hooks? Social obligations? FOMO? Loss aversion?	data-scientist
4. Monetization	What is the revenue model? What are the conversion triggers? How does spending enhance experience?	market-analyst
5. Technology	What engine/framework? How do they handle performance? What are technical differentiators?	mechanics-developer
6. Marketing & Community	How was the game marketed? What communities exist? How is content distributed?	market-analyst
7. Game Feel	What makes the game "feel good"? Audio design, visual feedback, input responsiveness?	game-feel-developer

Retention Analysis Deep Dive

Special focus area — what keeps players engaged:

Short-Term Retention (D1-D7)

• First-time user experience (FTUE) quality
• Tutorial effectiveness
• Time-to-fun measurement
• Initial content variety
• Early reward pacing

Mid-Term Retention (D7-D30)

• Content depth discovery
• Social features activation
• Habit loop formation
• Difficulty curve management
• Second-session hooks

Long-Term Retention (D30+)

• Endgame content systems
• Community integration
• Competitive/cooperative hooks
• Content update cadence
• Sunk cost and identity investment

Source Code Analysis (when available)

For open-source games or games with accessible source code:

Analysis Area	What to Extract
Architecture	Module structure, dependency graph, entry points
State Management	How game state is stored, updated, persisted
Event System	Communication patterns, decoupling strategy
Networking	Client-server model, sync strategy, lag compensation
Performance	Object pooling, rendering pipeline, memory management
AI Systems	Decision trees, state machines, behavior patterns
Content Pipeline	How content is loaded, cached, hot-swapped

Quality Criteria for Research

Data Quality Tiers

Tier	Source Type	Confidence	Usage
Verified	Official developer data, store APIs, public financials	90%+	Direct citation
Inferred	Review aggregation, community data, tool analysis	60-89%	Cite with confidence level
Estimated	Industry benchmarks, comparable titles, analyst reports	30-59%	Mark as estimate
Speculative	Educated guesses based on patterns	<30%	Mark as hypothesis

Minimum Research Requirements

Depth Level	Sources Required	Reports Generated	Estimated Duration
Quick	Store page + 3 reviews + 1 article	Executive Summary only	~10 min
Standard	Store page + 10 reviews + 3 articles + community scan	All 8 reports	~30 min
Deep	Standard + source code + gameplay analysis + developer interviews	All 8 reports + source code analysis	~60 min

Report Quality Gates

Before a report is considered complete:

• All template sections filled or explicitly marked N/A with reason
• Data sources cited for each major claim
• Confidence level stated for numerical estimates
• At least 3 actionable insights per report
• Cross-references to other reports where relevant

Cross-Game Comparison

When analyzing multiple games, generate a comparison matrix:

## Comparison Matrix: [Game A] vs [Game B] vs [Game C]

| Dimension | Game A | Game B | Game C | Our Opportunity |
|-----------|--------|--------|--------|----------------|
| Core Loop Duration | X min | Y min | Z min | Target |
| D1 Retention | X% | Y% | Z% | Target |
| D30 Retention | X% | Y% | Z% | Target |
| ARPU | $X | $Y | $Z | Target |
| Session Length | X min | Y min | Z min | Target |
| Metacritic | X | Y | Z | Target |
| Community Size | X | Y | Z | Target |