How to Implement EIP-1559 Fee Market Analysis for Your Team

EIP-1559 fundamentally restructured Ethereum's transaction fee mechanism, replacing a simple first-price auction with a base fee that adjusts per block and a priority fee (tip) for miners/validators. For any team building on Ethereum—whether it's a wallet, a DEX, or an analytics platform—understanding this new market is critical. Effective analysis helps optimize user experience by predicting gas costs, informs protocol parameter tuning, and provides insights into network demand cycles. This guide outlines the key data points and methodologies for implementing a robust EIP-1559 fee market analysis system.

The core of your analysis should track the base fee, a protocol-determined minimum gas price that is burned. Its adjustment algorithm targets 50% block fullness. You can query the current and historical base fee via the eth_feeHistory RPC method or from indexed data providers like The Graph. Monitoring the base fee's volatility, its correlation with block size (gasUsed / gasLimit), and its long-term trend (especially post-Merge) reveals fundamental network congestion patterns. For example, a sustained base fee above 30 Gwei typically indicates high demand for block space from activities like NFT mints or large DEX swaps.

Beyond the base fee, you must analyze the priority fee market. This is the extra tip users pay to prioritize their transactions, and it's where real-time competition happens. To model this, collect data on the distribution of priority fees in included transactions. A useful metric is the priorityFeePerGas at various percentiles (e.g., 50th, 90th) over recent blocks. Tools like ethers.js can decode this from transaction receipts. Observing when the 90th percentile tip spikes can signal transient congestion events that the base fee's 15-second lag hasn't yet captured, allowing your application to suggest appropriate fee bumps.

Implementing a gas estimator requires synthesizing this data. A basic model might suggest: maxFeePerGas = (2 * predictedBaseFee) + historicalPriorityFee90th. You can predict the base fee using a simple rule: if the last block was >50% full, the base fee increases by a maximum of 12.5%; if it was <50% full, it decreases. For production systems, consider more sophisticated models or integrate with services like Blocknative's Gas Platform or the Coinbase eth_maxPriorityFeePerGas RPC endpoint. Always allow users to view the components (base fee + tip) separately for transparency.

Finally, analyze the macro impact: ETH burn rate. Since EIP-1559, over 4 million ETH has been burned via the base fee. Tracking the burn rate (base fee * gasUsed) against issuance provides insights into Ethereum's monetary policy and net inflation. This is crucial for treasury management and financial reporting for DAOs and protocols. Use dashboards from Ultrasound.money or query the burn event from the block reward contract. Correlating high burn periods with on-chain activity (e.g., USDT transfers, Uniswap swaps) helps your team understand which applications are driving network economic activity.

The first prerequisite is access to a reliable, indexed Ethereum node. You cannot analyze EIP-1559's fee market dynamics using simple JSON-RPC calls alone. You need a service that provides historical block data, including the baseFeePerGas, gasUsed, gasLimit, and transaction details for each block. Services like Alchemy, Infura, or a self-hosted node with an indexing layer (e.g., TrueBlocks, The Graph) are essential. Your data source must allow you to query for blocks and transactions over specific time ranges to perform trend analysis.

Your team must understand the core fee calculation. Under EIP-1559, a transaction's maxFeePerGas must cover two components: the network's baseFeePerGas (which is burned) and a priorityFee (or tip) for the miner/validator. The actual fee paid is min(maxFeePerGas, baseFeePerGas + priorityFee). You'll need to parse this from transaction receipts. Analyzing the relationship between these values—how users set maxFeePerGas and priorityFee relative to the fluctuating baseFee—is key to understanding user behavior and network congestion.

Establish clear metrics for your analysis. Common KPIs include: - Base Fee Volatility: The standard deviation of baseFeePerGas over time. - Tip Sizing Behavior: The average and median priorityFee as a percentage of the total fee. - Inclusion Efficiency: The rate of transactions where maxFeePerGas was sufficient for inclusion (gasUsed / gasLimit at the block level). - Burn Rate Analysis: The total ETH burned per day via baseFee * gasUsed. Tools like Dune Analytics or Etherscan offer public dashboards for some metrics, but for protocol-specific analysis, you'll need to build your own.

You will need to write scripts to process this data. A common starting point is using Python with web3.py or JavaScript with ethers.js. For example, to fetch a block and its base fee in Python:

pythonCopy
from web3 import Web3
w3 = Web3(Web3.HTTPProvider('YOUR_RPC_URL'))
block = w3.eth.get_block('latest')
base_fee = block['baseFeePerGas']
print(f"Block {block.number} Base Fee: {Web3.from_wei(base_fee, 'gwei')} Gwei")

Batch processing historical blocks is necessary for any longitudinal study.

Finally, contextualize your data with network events. EIP-1559 fee market behavior is heavily influenced by external factors: major NFT mints, DeFi liquidations, or popular token launches can cause sudden baseFee spikes. Correlate your fee metrics with on-chain event data from sources like Flashbots for MEV activity or general gas guzzler reports. This holistic view separates normal fee market mechanics from outlier events driven by specific applications, giving your team actionable insights rather than just raw statistics.

EIP-1559 fundamentally changed Ethereum's fee market by introducing a base fee that burns and a priority fee (tip) for miners/validators. To analyze this market, you must first understand and query the core data structures. Every block header now contains the baseFeePerGas field, a uint256 value representing the minimum gas price for inclusion, which is algorithmically adjusted per block. The transaction object itself was extended with a type field (0x02 for EIP-1559) and new fields: maxPriorityFeePerGas (the tip) and maxFeePerGas (the absolute maximum a user will pay, which must be >= base fee + tip). Your analysis starts by extracting these values from blocks and their transactions.

Building an effective analysis pipeline requires querying historical blockchain data. You can use the eth_getBlockByNumber RPC call with the latest or a specific block number, ensuring the full_transactions parameter is set to true to get the full transaction objects. For batch analysis, services like The Graph, Google BigQuery's public Ethereum dataset, or dedicated blockchain ETL frameworks are more efficient. The key metrics to compute per block are: the actual base fee, the average and distribution of priority fees, and the gas used ratio (gas used / gas target), which drives the base fee adjustment for the next block. Tracking this ratio over time reveals network congestion patterns.

To derive actionable insights, calculate the inclusion efficiency for transactions. This involves comparing a transaction's maxPriorityFeePerGas to the effective priority fee it actually paid (the difference between the transaction's effectiveGasPrice and the block's baseFeePerGas). A high percentage of transactions paying significantly less than their max tip indicates users are overpaying for urgency. You should also monitor the burn rate (base fee * gas used), which directly impacts ETH's monetary policy. Implementing dashboards that track the 7-day moving average of burned ETH versus issued ETH gives your team a clear view of net issuance, a critical metric post-Merge. Tools like Dune Analytics or building custom dashboards with Grafana are common approaches.

For protocol and product teams, simulating fee estimation is crucial. You can implement a simple base fee predictor by tracking the sequential blocks' gas used ratio. The base fee for block N+1 is calculated from block N's base fee and its gas used ratio: baseFee_{N+1} = baseFee_{N} * (1 + 1/8 * (gasUsed_{N} / gasTarget - 1)). This deterministic formula allows you to forecast minimum costs. Furthermore, analyzing the distribution of priority fees helps optimize default settings in wallets and dApps. For example, if analysis shows 90% of transactions are included with a tip of 1.5 Gwei during standard congestion, your product can set a more efficient default maxPriorityFeePerGas rather than relying on aggressive, costly estimates.

Finally, structure your analysis to answer key business questions. Create reports that segment data by time of day, specific dApp activity (by tracing to and from addresses), or transaction type (e.g., DeFi swaps vs. NFT mints). This helps identify cost drivers for your users. Share findings through automated alerts—for instance, notifying developers when the network's median confirmation time for a 10 Gwei tip exceeds 3 blocks, signaling heightened congestion. By systematically implementing this analysis, your team moves from observing fees to understanding and anticipating them, leading to better user experience and more informed infrastructure decisions.

EIP-1559 fundamentally changed Ethereum's fee market by introducing a base fee that adjusts per block based on network demand. Unlike the previous first-price auction model, the base fee is algorithmically set and burned, creating a more predictable fee environment. The key metric for analysis is the base fee per gas, which can fluctuate significantly during periods of high activity. To build an effective analysis system, your first task is to collect this historical data from a reliable node provider or indexing service like The Graph, Alchemy, or Infura.

You need to query for base fee data at regular intervals. A practical approach is to fetch the base fee for every block over a significant timeframe, such as the last 30, 90, or 365 days. This data forms the foundation for calculating volatility. Using a simple Python script with the Web3.py library, you can extract this data. The core call is web3.eth.get_block(block_number), where the returned block object contains the baseFeePerGas attribute (in wei). Store this data with its corresponding block number and timestamp for time-series analysis.

With the raw data collected, you can calculate key volatility metrics. The most straightforward is the percentage change in base fee from block to block. Plotting this over time reveals periods of extreme volatility, often correlating with major NFT mints, token launches, or market events. More advanced analysis involves calculating statistical measures like the standard deviation of the base fee over rolling windows (e.g., 100 blocks or 1 hour). This quantifies the "noisiness" of the fee market and helps identify stable versus chaotic periods for transaction planning.

Visualizing this data is crucial for team understanding. Create charts showing the base fee trend over days or weeks, overlaying significant on-chain events. Use a secondary axis to plot the rolling volatility metric. This visual correlation helps your team answer critical questions: How quickly does the base fee spike during a popular mint? How long does elevated congestion typically last? Establishing these historical baselines is essential for making informed predictions and setting realistic gas budgets for future operations.

Finally, integrate this analysis into a monitoring dashboard. Tools like Grafana or custom web applications can display real-time base fee alongside your historical volatility bands. Set up alerts for when the base fee exceeds a certain percentile of its historical range or when volatility spikes beyond a defined threshold. This proactive monitoring allows your team to schedule high-priority transactions during calmer periods and avoid overpaying during predictable congestion, directly impacting operational efficiency and cost management.

The base fee is the cornerstone of EIP-1559's fee market. It is a protocol-determined minimum fee, calculated algorithmically for each block based on network congestion from the previous block. This fee is destroyed (burned) and permanently removed from the ETH supply. To calculate the ETH burned in a given block, you simply multiply the base fee (in wei) by the gas used in that block: Burned_ETH = base_fee * gas_used. For example, if block #15,000,000 has a base fee of 30 Gwei (30 * 10^9 wei) and uses 15 million gas, the burned amount is 450,000 Gwei, or 0.00045 ETH.

Network revenue, often called priority fee or miner/validator tip, is the portion of the transaction fee paid to the block proposer. This is the amount users add on top of the compulsory base fee to incentivize validators to include their transaction more quickly. The total revenue for a block is the sum of all priority fees from included transactions. You can calculate it by subtracting the burned value from the total transaction fees paid: Network_Revenue = total_fees_paid - (base_fee * gas_used). This revenue is the variable, user-driven component of the fee market.

To analyze these metrics programmatically, you can query an Ethereum node or use a provider like Alchemy or Infura. Using the eth_getBlockByNumber RPC call returns a block object containing the baseFeePerGas and an array of transactions. By iterating through the transactions and summing their gasPrice (for legacy txs) or maxPriorityFeePerGas (for Type 2 txs), you can compute the totals. Libraries like ethers.js or web3.py abstract this process. The key is to correctly handle different transaction types (EIP-1559 Type 2 vs. legacy) when parsing fee data.

For team dashboards and historical analysis, aggregating this data is essential. You might track metrics like: Daily Burned ETH, Average Priority Fee, and the Burn-to-Revenue Ratio. A high ratio indicates most fees are being destroyed, while a low ratio suggests validators are capturing more value. Tracking these over time reveals network usage patterns and the economic impact of EIP-1559. Services like Ultrasound.Money provide real-time aggregates, but for custom analysis, building a pipeline from an archive node or indexed data (e.g., Dune Analytics, The Graph) is necessary.

Understanding the split between burned ETH and validator revenue is crucial for financial modeling and protocol analysis. It directly impacts Ethereum's monetary policy (net issuance) and validator economics. When simulating transaction costs or analyzing on-chain activity, always break down the total fee into its two components. This clarity helps in forecasting validator yields, assessing the deflationary pressure from burning, and making informed decisions about transaction bundling or fee estimation strategies for your applications.

The priority fee, or tip, is the portion of the total transaction fee paid directly to validators to incentivize them to include a transaction in the next block. Under EIP-1559, users specify a maxPriorityFeePerGas. The actual tip paid is the difference between the miner (now validator) reward and the network's current baseFeePerGas, capped by the user's maximum. Analyzing the distribution of these paid tips reveals the real-time market price for block space. You can query this data from a node or block explorer API by examining transaction receipts for a given block range and calculating effectivePriorityFee = gasPrice - baseFee for each transaction.

To implement this analysis, you need to collect historical block data. Using a provider like Ethers.js, you can fetch blocks and their transactions. The key is to extract the gasPrice from each transaction and the baseFeePerGas from the block header. For legacy pre-EIP-1559 transactions, the entire gasPrice can be considered a tip. A practical code snippet to calculate the tip for a transaction in a block looks like this:

javascriptCopy
async function getTipForTransaction(block, tx) {
  const baseFee = block.baseFeePerGas;
  const gasPrice = tx.gasPrice;
  // For Type 2 (EIP-1559) transactions
  if (tx.type === 2) {
    return gasPrice.sub(baseFee);
  }
  // For legacy transactions
  return gasPrice;
}

Visualizing the data is crucial for insight. Plotting a histogram of tip values (in Gwei) over a 24-hour period shows the distribution. You'll typically observe a long-tail distribution: most transactions pay a modest tip (e.g., 1-3 Gwei) for standard inclusion, while a smaller number pay significantly higher tips (10+ Gwei) during congestion spikes or for urgent arbitrage transactions. Calculating statistics like the median, 90th percentile (P90), and maximum tip paid per block gives your team concrete metrics. For example, if the P90 tip is consistently 5 Gwei, setting a maxPriorityFeePerGas of 5.5 Gwei will statistically ensure your transactions are included in the top 90% of the priority queue.

This analysis directly informs transaction batching and scheduling strategies. By monitoring tip distributions, your protocol can decide when to submit non-urgant transactions (like scheduled contract updates) during low-tip periods to save costs. Conversely, during DeFi liquidations or NFT mint events, you can programmatically increase the tip based on the observed P90 to guarantee timely execution. Tools like the Blocknative Gas Platform API or Etherscan's gas tracker provide aggregated views, but building an internal dashboard with your own historical data allows for custom alerts and strategy backtesting tailored to your specific transaction patterns and risk tolerance.

The core feedback loop of EIP-1559 is governed by the base fee, which automatically adjusts up or down based on block fullness. A block's fullness is measured by its gasUsed relative to the gasTarget, which is exactly half of the block's gasLimit. For example, on Ethereum Mainnet, the gasLimit is 30 million, making the gasTarget 15 million. When consecutive blocks have a gasUsed greater than the target, the base fee increases; when they are below, it decreases. This mechanism aims to keep the average block at 50% capacity, creating predictable fee adjustments.

To monitor this, your team should query the last N blocks (a common window is 10-20 blocks) and calculate the average gasUsed. Compare this to the gasTarget to gauge network congestion. You can fetch this data using the eth_getBlockByNumber RPC call. The adjustment formula is applied deterministically: the base fee for block N+1 is calculated from block N's base fee, gasUsed, and gasTarget. Understanding this calculation allows you to predict fee trends rather than just react to them.

Implementing a simple monitoring dashboard is crucial. Track the base fee trend, the rolling average block fullness, and the burn rate (base fee * gasUsed). A sustained fullness above 100% signals high demand and rising fees, while prolonged low fullness indicates lower network activity. For development teams, this data informs optimal transaction timing for batch operations or contract deployments. Setting alerts for when the rolling average fullness exceeds a threshold (e.g., 90%) can help proactively manage gas budgets.

Beyond passive monitoring, this data enables target adjustment analysis. The protocol's goal is long-term equilibrium at the gasTarget. By analyzing historical periods of sustained high fullness, you can assess whether the current target is optimal for network throughput or if it's contributing to volatile fee spikes. While the gasTarget is not frequently changed, understanding its role is key for long-term scalability research and contributing to network upgrade discussions, such as proposals to adjust the target in future hard forks.

Successfully implementing EIP-1559 analysis requires moving from theory to a reliable data pipeline. Your first action should be to set up a dedicated data ingestion service. Use a node provider like Alchemy or Infura to stream real-time blocks. Parse each block to extract the baseFeePerGas, total gas used, and the priorityFeePerGas (tip) for individual transactions. Store this data in a time-series database (e.g., TimescaleDB) or a data warehouse. This foundational dataset is critical for all subsequent analysis, from tracking long-term base fee trends to identifying periods of high network congestion.

With data flowing, build dashboards and alerts for your team. Key metrics to monitor include: the base fee burn rate (ETH permanently removed from supply), the average tip size as a percentage of the total fee, and the frequency of blocks using the full 2x base fee multiplier. Set up alerts for extreme volatility; a base fee spike of over 100% in 5 blocks may indicate a mempool-clearing event like an NFT mint. Tools like Grafana connected to your database, or platforms like Dune Analytics for shared queries, make this data actionable for developers, product managers, and executives.

Finally, integrate these insights into your product's transaction strategy. For your team's dApp or wallet, implement dynamic fee estimation that uses historical base fee trends and mempool analysis, rather than static gas price settings. Consider using the eth_maxPriorityFeePerGas RPC call from providers, but validate it against your own data. For batch transactions or treasury management, schedule high-gas operations during predictable low-activity periods, typically weekend hours UTC. Continuously refine your models; the fee market evolves with layer-2 adoption and new EIPs. Your analysis system is a competitive advantage in building a superior user experience.