Circularity Score

A high Circularity Score indicates a healthy, self-sustaining ecosystem where assets are actively used within the protocol's native applications. This is a key metric for evaluating long-term viability, as it shows value is being retained and reused rather than being drained through one-way transactions to centralized exchanges or external bridges. Analysts use it to gauge protocol stickiness and resilience against capital flight.

In lending protocols like Aave or Compound, a high Circularity Score for a specific asset (e.g., a governance token) suggests it is primarily used as collateral within the ecosystem rather than being sold. This reduces sell-side pressure and creates a more stable economic flywheel. It helps assess the risk of collateral devaluation and the strength of the protocol's internal economy.

Decentralized Autonomous Organizations (DAOs) use the Circularity Score to audit their treasury's impact. A low score for the DAO's native token might indicate treasury assets are being dumped on the open market, harming token holders. A high score suggests treasury funds are being deployed productively within the ecosystem—e.g., for liquidity provisioning, grants, or staking—which reinforces the token's utility and value.

Venture capitalists and institutional analysts incorporate the Circularity Score into their fundamental analysis framework. It moves beyond simple Total Value Locked (TVL) or transaction count by answering: Is the capital productive and sticky? A protocol with high circularity is often seen as having stronger network effects and a more defensible moat, as users and capital are engaged in a reinforcing loop.

A declining Circularity Score acts as an early warning signal for economic leakage. It can pinpoint when a protocol's incentives are misaligned, causing users to extract value (e.g., farm and dump yield) instead of participating in the ecosystem. Developers can use this data to redesign tokenomics, adjust emission schedules, or introduce new utility mechanisms to plug leaks and improve circularity.

When analyzing Layer 2 rollups or app-specific chains, the Circularity Score reveals whether the chain is fostering a native economy or simply acting as a bridge/transfer hub. A healthy L2 should show high circularity for its native gas token and key assets, indicating active on-chain commerce, DeFi, and NFT trading rather than just being a throughput layer for settling back to Layer 1.

Venture capitalists incorporate the Circularity Score into their technical due diligence for early-stage blockchain projects. It serves as a quantifiable metric to assess token utility and economic design flaws before investment. A persistently high score post-token generation event (TGE) can be a red flag for a weak product-market fit or misaligned incentive structures.

The score enables apples-to-apples comparison of token economies across different blockchains. For instance, comparing the Circularity Scores of liquid staking tokens (e.g., stETH, rETH, sFRXETH) reveals differences in how these assets are utilized within their respective DeFi ecosystems, highlighting which are treated more as productive collateral versus those that are primarily traded.

In GameFi and NFT projects, the score measures the internal economic loop. A healthy project shows tokens and NFTs flowing between users, the marketplace, and the game's treasury (medium circularity). An unhealthy signal is a high score where rewards are instantly sold on external markets, or a very low score indicating a stagnant, non-functional economy with no economic activity.

The score is not a single metric but an aggregate of multiple on-chain indicators. These typically include:

• Token Velocity: Measures the frequency of token transactions.
• Concentration Risk: Analyzes token distribution among holders.
• Utility Demand: Assesses usage in fees, staking, or governance.
• Economic Security: Evaluates the cost to attack the network versus its value. Combining these dimensions prevents a single flawed metric from skewing the overall assessment.

Raw on-chain data must be processed to create a comparable score. This involves:

• Normalization: Scaling different metrics (e.g., transaction count vs. stake value) to a common range (e.g., 0-100).
• Weight Assignment: Applying strategic importance to each dimension. For example, economic security might be weighted more heavily for a Proof-of-Stake chain than token velocity.
• Benchmarking: Comparing a protocol's metrics against industry peers or historical baselines to contextualize the score.

The score's reliability depends entirely on the quality and transparency of its underlying data. Key considerations are:

• Node Source: Using a dedicated, synchronized archive node to ensure data completeness.
• On-Chain Provenance: Relying solely on immutable ledger data, avoiding subjective off-chain sentiment or social metrics.
• Calculation Transparency: The methodology and formulas should be open-source, allowing for audit and replication of the score.

A protocol's economic health changes over time. The score must account for this through:

• Time Windows: Analyzing activity over defined periods (e.g., 7-day, 30-day rolling averages) to smooth volatility and identify trends.
• Snapshot Frequency: Determining how often the score is recalculated (e.g., daily, hourly). Real-time scores reflect immediate conditions but can be noisy.
• Historical Context: Tracking score evolution to distinguish between temporary fluctuations and structural shifts in protocol economics.

A one-size-fits-all model fails to capture unique mechanics. Design must accommodate:

• Consensus Mechanisms: Metrics for Proof-of-Work (miner revenue/security) differ from Proof-of-Stake (validator stake concentration, slashing).
• Tokenomics: Models with high inflation, burning, or ve-token locking require custom indicator adjustments.
• Ecosystem Phase: Weightings may differ for a nascent DeFi protocol versus an established Layer 1, as their economic priorities vary.

No metric is perfect. Important limitations to understand include:

• Lagging Indicator: The score reflects past on-chain activity, not future performance.
• Exogenous Shocks: It may not fully capture the impact of major market events or exploits not directly visible in the selected metrics.
• Model Risk: The chosen weights and formulas embed assumptions that may become outdated as the ecosystem evolves. Regular model review is essential.

A Sybil attack is a security threat where a single entity creates and controls a large number of fake identities (Sybils) to subvert a network's reputation or consensus system. In DeFi, this is often done to manipulate governance votes, airdrop distributions, or liquidity mining rewards. The Circularity Score is a primary defense mechanism designed to detect and quantify this specific behavior by analyzing transaction graph patterns between wallets.

Transaction graph analysis is the network science methodology underpinning the Circularity Score. It models blockchain addresses as nodes and token transfers as edges, creating a directed graph. By applying algorithms to this graph, analysts can identify suspicious clusters, such as:

• Circular flows of tokens between a small set of addresses.
• Hub-and-spoke structures indicative of centralized control.
• Anomalously high internal transaction density within a suspected Sybil cluster.

On-chain identity refers to the persistent behavioral fingerprint of a wallet or user, derived from their transaction history, asset holdings, and interaction patterns. Unlike a legal identity, it is pseudonymous and based solely on public ledger data. Tools like the Circularity Score contribute to building this identity by assessing whether a wallet's activity is organic or part of a coordinated Sybil operation, which is crucial for trustless reputation systems.

Airdrop farming is the practice of strategically interacting with a protocol to qualify for a future token distribution. While legitimate users participate, it is a primary incentive for Sybil attackers to create hundreds of wallets mimicking real activity. Protocols use the Circularity Score and similar metrics to filter out these fake users, ensuring tokens are distributed to genuine participants and protecting the token's economic value post-launch.

Wallet clustering is the process of using heuristics and algorithms to group multiple blockchain addresses likely controlled by the same entity. Common techniques include analyzing common input ownership and funding sources. The Circularity Score acts as a powerful heuristic within this process, identifying clusters not just by funding, but by their interactive behavior—specifically, the circular transfer of value that is economically irrational for independent actors.

Governance attack vectors are methods used to maliciously influence a decentralized autonomous organization (DAO). A primary vector is the Sybil attack, where an attacker uses many wallets to accumulate voting power. A high Circularity Score within a set of voting addresses is a strong signal of such an attack, allowing DAOs to implement safeguards like proof-of-personhood checks or weighted voting based on legitimacy scores.