MEV Quantification Model

Models must process high-fidelity data from multiple sources to reconstruct the transaction lifecycle. This includes:

• Mempool Data: Pending transactions before inclusion.
• Block Data: Finalized transaction ordering and state changes.
• Event Logs: Smart contract interactions and internal calls.
• Node Traces: Detailed execution paths (e.g., debug_traceTransaction). Integration of these sources allows for precise attribution of value flow from opportunity identification to final settlement.

A core function is categorizing extracted value by its extraction strategy and economic impact. Standard classifications include:

• Arbitrage: Exploiting price differences across DEXs or layers.
• Liquidations: Triggering and profiting from undercollateralized positions in lending protocols.
• Sandwich Attacks: Frontrunning and backrunning a victim's DEX trade.
• Time-Bandit Attacks: Reorganizing blocks to alter historical transactions. Classification enables trend analysis, risk assessment, and the development of counter-strategies.

Models attribute profits to specific actors (searchers, validators, bots) and transactions. This involves:

• Profit Tracing: Following the flow of assets to identify the beneficiary's address.
• Counterfactual Modeling: Simulating a block without the MEV transaction to establish the baseline state and calculate the net extracted value.
• Bundle Decomposition: Parsing complex PBS (Proposer-Builder Separation) bundles to attribute value between builders and searchers. Accurate attribution is critical for measuring validator centralization and ecosystem health.

Quantification occurs across different time horizons and network layers:

• Real-time: Monitoring live mempools and pending bundles for immediate opportunity detection.
• Historical: Analyzing finalized chain data for research and retrospective analysis.
• L1 vs. L2: Applying models to Ethereum mainnet, rollups (Arbitrum, Optimism), and other EVM chains, each with unique mempool and sequencing dynamics.
• Cross-domain: Measuring value extracted across bridges and interconnected chains.

Models produce standardized metrics to summarize MEV activity. Key outputs include:

• Extracted Value (EV): The total profit captured by extractors, often denominated in ETH or USD.
• Supply Curve: The distribution of MEV opportunities by profit size.
• PBS Payment Flow: The value transferred from builders to proposers via coinbase transfers.
• Jevons' Paradox: A metric observing whether efficiency gains (e.g., DEX improvements) actually increase total MEV supply by enabling more complex strategies.

Robust models account for estimation uncertainty and false positives. This involves:

• Bounds Calculation: Providing confidence intervals for profit estimates, as some value transfers are ambiguous.
• Heuristic Validation: Cross-checking model outputs against known attack patterns and labeled datasets.
• Gas Cost Accounting: Precisely netting transaction execution costs (gas fees) from gross profits to determine net gain.
• Oracle Reliability: Assessing the impact of oracle price manipulation on perceived arbitrage opportunities.

A model that calculates the upper bound of value that could be extracted from a given set of pending transactions in a block, regardless of current market infrastructure.

• Key Metric: Potential profit in a perfect execution scenario.
• Method: Simulates optimal transaction ordering across all available DeFi liquidity pools and lending protocols.
• Purpose: Used for academic research and protocol design to understand the economic limits and risks, rather than measuring actual extracted value.

This model quantifies MEV by analyzing the premium paid in transaction fees (priority gas auctions). High fee spikes are often direct indicators of competitive MEV opportunities.

• Key Metric: Excess gas paid above the base network fee.
• Method: Monitors gas prices for transactions landing in the same block and correlates them with profitable arbitrage or liquidation patterns.
• Insight: Helps measure the cost of MEV competition and its impact on network congestion.

Professional block builders are the primary consumers of real-time MEV models. They use sophisticated simulation engines to construct the most profitable blocks by identifying and bundling lucrative arbitrage and liquidations. Validators (or proposers) use these models to select the most profitable block from builders, directly impacting their staking rewards. Quantification is essential for their auction mechanisms and revenue optimization.

Searchers (MEV bots) and institutional trading desks deploy proprietary quantification models to discover and value MEV opportunities in real-time. Their models calculate profitability thresholds, estimate gas costs, and model network latency to execute strategies like DEX arbitrage, liquidations, and sandwich attacks. Accurate models are the difference between profit and loss in this high-speed, competitive environment.

Institutional investors (e.g., hedge funds, asset managers) use MEV quantification to assess the true cost of execution for large trades, which can be inflated by frontrunning. They also evaluate the economic security of Proof-of-Stake networks, where MEV influences validator yields. Regulators and policymakers are beginning to study these models to understand market manipulation risks and inform potential on-chain market oversight frameworks.

Companies like Chainscore Labs, EigenPhi, and Blocknative build and commercialize MEV quantification models as core products. They provide APIs, dashboards, and datasets that power the analytics for all other stakeholders. Their models aggregate on-chain data to produce standardized metrics like Total Extracted Value (TEV), MEV per block, and searcher concentration, becoming the public source of truth for the ecosystem.

Maximum Extractable Value (MEV) is the total value that can be extracted from block production in excess of the standard block reward and gas fees by including, excluding, or reordering transactions within a block. It is the theoretical upper bound that quantification models aim to measure and track in practice. MEV arises from arbitrage, liquidations, and other on-chain opportunities.

Proposer-Builder Separation (PBS) is a protocol design that decouples the roles of block building (selecting and ordering transactions) from block proposing (signing and publishing the block). This architecture is central to modern MEV quantification, as it creates a transparent market where builders compete in auctions to sell blocks containing optimized MEV to proposers (validators).

MEV-Boost is an out-of-protocol implementation of PBS for Ethereum, allowing validators to outsource block construction to a competitive marketplace of builders. It is a primary data source for MEV quantification models, as its public relay APIs provide visibility into the builder bids and block payments that represent realized MEV. Quantification often analyzes the difference between the winning bid and the public mempool's potential value.

The MEV supply chain describes the flow of value extraction from opportunity discovery to final settlement. Key roles include:

• Searchers: Identify and bundle opportunities.
• Builders: Construct optimized blocks from bundles.
• Relays: Facilitate trustless block delivery from builders to proposers.
• Proposers/Validators: Finalize the block. Quantification models track value captured at each stage, especially the payment from builder to proposer.

A core distinction in quantification is between Realized MEV (value actually captured and paid to validators) and Theoretical MEV (the maximum possible value from perfect execution). Models focus on Realized MEV, measured via on-chain payments (e.g., Coinbase transfers in MEV-Boost) and arbitrage profit calculations. The gap between theoretical and realized MEV represents inefficiency, competition, or value retained by searchers/builders.

Private order flow refers to transactions sent directly to builders or searchers instead of the public mempool, creating dark pools of liquidity. This practice significantly impacts MEV quantification by reducing the observable opportunity set in public data. Models must account for this hidden activity, which can obscure true MEV levels and centralize extraction.