Kaizen Scoring System
The Kaizen Scoring System is the core intelligence engine that evaluates Web3 projects and provides actionable risk insights. Our proprietary algorithm combines multiple data sources and analytical methods to generate comprehensive security scores.
Score Range and Categories
Primary Score Range
Score Scale: 0-100 (integer values)
Scoring Direction: Higher scores indicate lower risk
Update Frequency: Real-time with 30-second intervals for active monitoring
Score Categories
90-100
Excellent
Very Low Risk
Highly secure projects with robust fundamentals
75-89
Good
Low Risk
Solid projects with minor concerns
60-74
Moderate
Medium Risk
Projects with notable risks requiring caution
40-59
Poor
High Risk
Projects with significant red flags
20-39
Very Poor
Very High Risk
Projects with severe security concerns
0-19
Critical
Extreme Risk
Likely scams, rug pulls, or honeypots
Sub-Score Components
Each primary score is composed of weighted sub-scores:
Primary Score = (
Liquidity Score × 0.25 +
Code Quality Score × 0.20 +
Social Intelligence Score × 0.15 +
Ownership Score × 0.15 +
Developer Activity Score × 0.10 +
Market Behavior Score × 0.10 +
Security Audit Score × 0.05
)
Risk Classification
Classification Methodology
Green Zone (75-100)
Verified contract source code
Locked or burned liquidity (>6 months)
Active and transparent development team
No major red flags detected
Strong community engagement
Yellow Zone (40-74)
Some verification missing
Moderate liquidity concerns
Limited development transparency
Minor technical issues detected
Mixed social sentiment
Red Zone (0-39)
Unverified or suspicious code
Unlocked liquidity or low liquidity
Anonymous or inactive developers
Multiple red flags present
Negative social indicators
Risk Factors Hierarchy
Critical Risk Factors (Immediate red flags)
Honeypot contracts detected
Unlimited minting functions
Hidden backdoors in code
Suspicious fund movements
Known scammer associations
Major Risk Factors
Unverified smart contracts
Low liquidity (<$10k)
Anonymous development team
Rapid token distribution
Suspicious social activity
Minor Risk Factors
Recent project launch (<7 days)
Limited social presence
Low trading volume
Basic tokenomics
Minimal documentation
Confidence Levels
Confidence Scoring Methodology
Confidence levels indicate the reliability of our scoring based on available data and analysis depth.
Very High
95-100%
Comprehensive analysis with multiple data sources
48+ hours of monitoring, verified contracts, social data
High
85-94%
Solid analysis with good data coverage
24+ hours of monitoring, most data sources available
Medium
70-84%
Adequate analysis with some limitations
12+ hours of monitoring, basic data available
Low
50-69%
Limited analysis due to data constraints
<12 hours of monitoring, minimal data
Very Low
<50%
Insufficient data for reliable scoring
New project, limited information
Confidence Calculation Formula
Confidence = (
Data_Completeness × 0.30 +
Analysis_Depth × 0.25 +
Source_Reliability × 0.20 +
Time_Coverage × 0.15 +
Verification_Status × 0.10
) × 100
Score Interpretation Guidelines
For End Users
Score 90-100: Proceed with Confidence
Generally safe for investment consideration
Continue monitoring for any changes
Review specific risk factors for complete picture
Score 75-89: Proceed with Caution
Acceptable risk level for experienced users
Monitor closely for developments
Consider smaller position sizes
Score 60-74: Exercise Extreme Caution
Suitable only for risk-tolerant investors
Requires continuous monitoring
Consider waiting for improvements
Score 40-59: High Risk - Not Recommended
Significant concerns identified
Only for speculative trading
Use minimal capital allocation
Score 0-39: Avoid - Extreme Risk
Multiple critical issues detected
High probability of loss
Not recommended under any circumstances
For Developers and Auditors
Technical Indicators to Monitor:
Contract verification status
Ownership concentration ratios
Liquidity lock mechanisms
Code complexity metrics
External dependency risks
Social Indicators to Track:
Community engagement levels
Developer communication frequency
Social sentiment trends
Influencer involvement patterns
Manipulation detection signals
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