Answer first — To detect MEV against your own trades in 2026, follow a four-step forensic process: (1) pull the transaction receipt and identify the block; (2) inspect adjacent transactions in the same block for matching pool/matching tokens; (3) compare your effective price to the pool's pre-trade quote; (4) attribute the slippage to MEV (sandwich/back-run) vs natural price impact vs ordering luck. Tools that make this fast: EigenPhi, MEV-Inspect-py, Etherscan's MEV labels, and Libmev. For Solana: Eclipse and Jito-Explorer. Most retail traders discover they're being sandwiched on 5–20% of their swaps once they actually look.
Why Bother Detecting
Three reasons:
- Routing changes. If Aggregator A leaks 12 bps per swap on average and Aggregator B leaks 3 bps, you switch.
- Behavioral changes. If you're being sandwiched mostly on trades > $5,000, you split orders or use a private mempool.
- Real loss accounting. MEV is a real, quantifiable trading cost. Most people underestimate it because they never measure it.
Step 1: Pull The Transaction Receipt
Find your trade's transaction hash. From the receipt, you need:
- The block number
- The transaction index within the block (your position in the ordering)
- The pool address you swapped against
- The token amounts you swapped (in and out)
- The gas paid and the priority fee paid
On Etherscan, all of this is on the transaction detail page. Programmatically: eth_getTransactionByHash + eth_getTransactionReceipt.
Step 2: Inspect Adjacent Transactions
Look at the transactions in the same block, ordered by index. Two patterns indicate MEV:
Pattern A: Sandwich (Front-Run / Back-Run)
If you see:
- Transaction (your index - 1): A swap on the same pool in the same direction as yours
- Transaction (your index + 1): A swap on the same pool in the opposite direction
That's a classic sandwich. The front-run pushed the price up before your fill, your trade filled at the worse price, and the back-run captured the imbalance.
Pattern B: Back-Run (No Front-Run)
If you see no front-run, but a same-pool opposite-direction swap immediately after yours — that's a pure back-run. Less damaging to you (doesn't change your fill price), but indicates your trade was visible in the mempool.
Pattern C: JIT Liquidity
If you see IncreaseLiquidity then DecreaseLiquidity on the same pool with very small intervening swap count — that's JIT (just-in-time) liquidity. A searcher added liquidity, captured your trade's fee, and removed it.
Step 3: Compare Effective Price To Pool Quote
Independent of pattern detection, do a price comparison:
- Note the pool reserves (or tick state for V3) at block N-1
- Compute the expected output of your trade against those reserves
- Compare to your actual output from the receipt
Formula:
mev_loss_bps = (expected_output - actual_output) / expected_output * 10000 - expected_price_impact_bps
If mev_loss_bps > 5–10 and you can match the front-run/back-run pattern, you've been sandwiched.
Step 4: Attribute The Slippage
| Source | Detection |
|---|---|
| Natural price impact | Predicted by AMM math from your trade size |
| Other-trade ordering | Another (non-malicious) user filled before you |
| MEV (sandwich / back-run / JIT) | Matches the patterns in step 2 |
Most real-world slippage on retail swaps is a mix. The MEV component is typically 3–25 bps for small public-mempool swaps, larger for big swaps and long-tail tokens.
Tools That Do The Heavy Lifting
EigenPhi
Best-in-class MEV analytics. Paste a transaction hash → labeled breakdown: sandwich front-run, victim, back-run, profit captured. Works for Ethereum mainnet, several L2s, and BNB Chain.
MEV-Inspect-py (Flashbots)
Open-source library that classifies MEV-extraction patterns from block data. Best for running your own analysis on your trading history. Requires a full node or archive RPC.
Etherscan's MEV labels
Etherscan tags known MEV bot addresses and shows MEV-extracted value in the block detail view. Quick and accessible.
Libmev
Open-source library and dashboard. Good for self-hosted analysts who want a Web UI without paying for EigenPhi's enterprise tier.
Solana-specific: Jito-Explorer + Eclipse
For Solana trades, Jito's bundle explorer shows tipped transactions and bundle composition. Eclipse offers MEV analytics with sandwich detection on major DEX programs.
Quick-Look Workflow (5 minutes)
- Copy your transaction hash
- Paste into EigenPhi (free tier covers most retail-size trades)
- Look at the "Victim Loss" field — that's your MEV cost in USD
- If significant, check whether the trade went through a public mempool. If yes, your routing leaked.
What Detection Tells You About Routing
Patterns you'll see repeatedly:
- Public-mempool swaps on long-tail tokens: MEV leakage ranges 15–80 bps. Routing through aggregators with private orderflow (1inch Fusion, CoW, Paraswap Delta) typically cuts this to <3 bps.
- Large swaps on blue-chip pairs: Even on major pairs, swaps above ~$50k consistently leak 5–20 bps to back-running.
- Small swaps: For <$500 trades, gas costs dominate and MEV loss is usually <$0.50.
What To Do With The Data
Action 1: Change Routing
Switch to a private-orderflow aggregator (1inch Fusion, CoW, Paraswap Delta) or submit through a private relay.
Action 2: Use Private Mempool
For sniper-style trades, submit through Flashbots Protect, MEV-Share, or your chain's equivalent.
Action 3: Split And Time Trades
For large trades, split into chunks below the threshold where MEV becomes cost-effective for searchers (~$5,000-$10,000 on mainnet).
Bottom Line
If you've never measured MEV against your own trades, you're almost certainly losing more than you think. The tools take 5 minutes to learn. Most retail traders cut their MEV exposure by 60–90% within a month of starting to measure — the act of looking changes the routing decisions.
Full post + FAQ at ai-frb.com. FRB Agent is a non-custodial desktop MEV agent for Windows — download free.
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