POA-100 Methodology

Last updated 3 August 2026
Framework version 1.0

Purpose of the framework

The POA-100 framework evaluates the fundamental investment quality of a crypto-asset through a structured, evidence-based process. It is designed to reduce narrative bias, expose weak token economics and make judgments comparable across sectors.

The score measures the quality of the asset at the stated research date. It is not a probability of future returns, a price target or an automatic trading signal.

Every complete assessment contains four distinct conclusions.

  • POA-100 fundamental score
  • investment recommendation
  • risk classification
  • research confidence

These outputs answer different questions and should not be treated as interchangeable.

The nine scoring categories

1. Team, governance and backers — 10 points

This category examines the experience, execution record and transparency of the team, together with governance structure, key-person dependence and the influence of investors or strategic backers.

Institutional support can improve access to capital, talent and distribution. It can also create concentration, governance or unlock risk. Backers receive credit only where their involvement improves the probability of execution.

2. Product and technical maturity — 15 points

This category evaluates whether the product exists, functions under real conditions and solves a defined problem.

Relevant evidence includes mainnet status, technical architecture, audits, developer activity, uptime, integrations and the gap between documented capability and promotional claims. A technically ambitious white paper receives less credit than a narrower product with verifiable production use.

3. Adoption and competitive position — 15 points

Adoption is evaluated through users, developers, customers, transaction activity, integrations, liquidity and repeat demand. Reported activity is examined for subsidies, wash behaviour, sybil incentives and temporary campaigns.

Competitive position includes differentiation, switching costs, distribution, network effects and the probability that value will accrue to the protocol under examination.

4. Token utility and value capture — 15 points

This category asks why the token should exist and whether increasing product usage creates durable demand for it.

Relevant mechanisms include payment, staking, collateral, security, governance, access, fee capture, burns and buybacks. Utility receives limited credit when the same product could operate with stablecoins or conventional payments without weakening the network.

A strong business attached to a weak token can score highly on product quality while remaining weak in this category.

5. Tokenomics and supply risk — 12 points

The analysis covers maximum supply, circulating supply, emissions, vesting, insider ownership, treasury control, concentration and the likely path of dilution.

Reported circulating supply is compared across project disclosures, block explorers, CoinMarketCap, CoinGecko, unlock providers and on-chain evidence where available. Material discrepancies reduce the score until they are resolved.

Scarcity claims receive credit only when supply reduction is enforceable, economically meaningful and examined together with newly unlocked or emitted supply.

6. Revenue and economic transparency — 10 points

This category evaluates fees, protocol revenue, holder revenue, incentives, operating expenditure and the economic relationship between usage and token holders.

Gross fees, protocol revenue, treasury income and token-holder value capture are treated as separate quantities. Annualised figures are identified as extrapolations and adjusted where short observation periods or unusual market conditions could distort them.

Projects receive less credit when economic claims cannot be reconciled with public data.

7. Market timing and catalysts — 8 points

This category examines sector demand, liquidity conditions, regulatory developments, product launches, listings, halvings, unlocks and other identifiable events that may affect the investment case.

Narrative momentum can improve timing while leaving fundamental quality unchanged. Catalysts receive credit according to their probability, timeframe and likely economic effect.

8. Execution, security and regulatory risk — 8 points

This category covers implementation complexity, smart-contract and infrastructure risk, legal exposure, governance intervention, dependency on partners and the possibility of adverse regulation.

Risk is evaluated through both probability and consequence. A low-frequency event can remain decisive when its potential damage is existential.

9. Risk-adjusted attractiveness — 7 points

The final category evaluates the asymmetry available after considering valuation, liquidity, dilution, downside scenarios and competing opportunities.

This category does not replace the separate valuation analysis. It provides limited score recognition where the relationship between potential reward and fundamental risk is unusually favourable or unfavourable.

Score interpretation

85–100 — Exceptional

The asset demonstrates unusual fundamental strength, credible value capture and manageable structural risk. Scores in this range should be rare.

75–84 — Attractive

The investment case is fundamentally strong, although valuation, timing or identifiable risks may still prevent an immediate purchase.

65–74 — Selective or watch

The project contains substantial strengths together with weaknesses that require monitoring, position limits or a more attractive entry price.

55–64 — Speculative

The thesis depends heavily on execution, narrative continuation, favourable market conditions or unresolved token economics.

40–54 — Weak

Structural problems outweigh the current evidence for durable value creation.

0–39 — Reject

The asset presents severe deficiencies, unreliable information, dysfunctional token economics or risks incompatible with a responsible investment case.

Why the score does not determine the recommendation

A POA-100 score evaluates fundamental quality. The recommendation incorporates additional variables.

  • current market price
  • fully diluted valuation
  • circulating valuation
  • expected dilution
  • liquidity
  • identifiable catalysts
  • portfolio concentration
  • downside scenarios
  • opportunity cost
  • the investor’s existing position

A high-quality asset can receive a Hold recommendation when valuation already discounts an optimistic future. A lower-scoring asset can receive a limited speculative Buy when the market price offers exceptional asymmetry. Such cases require explicit explanation.

Recommendation definitions

Buy

The expected risk-adjusted return is favourable at the assessed valuation. A Buy may still require position limits, staged entries or specific execution conditions.

Hold

The existing investment case remains valid, while current valuation, uncertainty or portfolio considerations provide insufficient justification for increasing exposure.

Watch

The project merits continued research, although the evidence, valuation or timing does not support investment exposure.

Reduce

The thesis has weakened or the position has become excessive relative to risk. Partial exposure may remain justified.

Sell or Avoid

The expected return no longer compensates for the identified risks, the thesis has been invalidated or a structural deficiency prevents responsible exposure.

Research confidence

Research Confidence is reported separately on a scale from 0 to 100. It measures the reliability and completeness of the evidence behind the analysis.

Confidence rises when primary documentation, on-chain data, audited financial information and multiple independent sources agree. It falls when supply figures conflict, economic data is unavailable, the project is very early, wallets cannot be classified or important claims depend on unverifiable disclosures.

A high POA-100 score with low Research Confidence deserves greater caution than the same score supported by mature and transparent evidence.

Risk classification

Risk classification reflects the probability and severity of permanent capital loss. It considers volatility, liquidity, dilution, custody, security, governance, regulatory exposure and the maturity of the asset.

The standard classifications are Low, Moderate, High and Very High. Within crypto markets, a Low classification remains relative to other crypto-assets and should not be interpreted as low risk in conventional investment terms.

Source hierarchy

Proof of Analysis generally prioritises evidence in the following order.

  1. On-chain records and verifiable contract data
  2. Official protocol documentation, governance records and repositories
  3. Audits, regulatory filings and independently verifiable financial disclosures
  4. Established data providers and analytics platforms
  5. Project interviews, announcements and public statements
  6. Secondary reporting and community analysis

CoinMarketCap project pages are treated as an important research source, particularly their Token Unlocks, Holders, Markets and About sections. Material claims are cross-checked against primary sources, on-chain evidence and independent analytics whenever possible.

Conflicting information

Crypto data providers frequently use different definitions of circulating, unlocked, transferable and total supply. Proof of Analysis does not resolve such conflicts by selecting the most favourable number.

Where a discrepancy could alter valuation or risk, the analysis presents the competing figures, identifies the preferred interpretation and reduces Research Confidence when the evidence remains inconclusive.

Score caps and overrides

Certain deficiencies can limit the final score regardless of strength elsewhere.

Examples include unverifiable supply, ineffective token utility, extreme insider concentration, critical security failures, fabricated adoption data, undisclosed paid promotion or evidence that token holders possess no plausible route to economic value capture.

An existential legal, technical or governance risk may also override the numerical score and produce an Avoid recommendation.

Updates and historical scores

Every score belongs to a stated research date and framework version. Historical scores are preserved unless a factual or methodological error requires correction.

A later score may change because product adoption, token supply, valuation or risk has changed. The earlier judgment remains part of the research record.

Material corrections and score revisions follow the policy described on the Transparency page.

Limitations

Structured scoring improves consistency while retaining analytical judgment. Category boundaries can overlap, evidence quality varies across projects and numerical precision can create an appearance of certainty that the underlying market does not support.

The framework is therefore a disciplined decision aid. It cannot eliminate uncertainty, forecast market prices or replace individual risk assessment.