Polygon MEV Ecosystem Overview

• Polygon MEV ecosystem is a distinct blockchain domain characterized by open-orderflow visibility, low-cost blockspace, and structured auction channels.
• Recent empirical studies reveal that atomic arbitrage and backrunning—via methods like FastLane—dominate measurable MEV extraction with notable validator and searcher profit disparities.
• Methodological challenges and mitigation strategies, including spam prevention, fair ordering, and privacy-preserving techniques, are central to optimizing MEV extraction and redistribution on Polygon.

The Polygon MEV ecosystem is the ensemble of ordering rights, searcher strategies, auction interfaces, validator incentives, and DeFi state transitions through which extractable value is realized on Polygon. Across the current literature, Polygon is not treated as a simple replica of Ethereum, but as a distinct ordering domain shaped by open-orderflow visibility, cheap blockspace, spam-prone competition, and the later emergence of structured auction channels such as FastLane/Atlas. Within the empirical work most directly focused on Polygon, the central measurable activity is Atomic Arbitrage (AA) and closely related backrunning, while adjacent studies extend the picture to opportunity creation, sealed-bid bidding games, attribution, and mitigation design (Vostrikov et al., 29 Aug 2025, Bagourd et al., 2023).

1. Conceptual scope and core definitions

MEV on Polygon is typically defined in the standard transaction-ordering sense: it is the additional value validators or miners can capture by reordering, including, or censoring transactions within a block. General game-theoretic work models Polygon as a distinct blockchain “domain,” with searchers acting on local transaction visibility and sequencers or validators instantiating the effective ordering mechanism; one of its direct Polygon-specific observations is that “domains like Polygon PoS, where the propagation of transactions is probabilistic, incentivise searchers to spam transactions to extract MEV” (Mazorra et al., 2022). Survey work further separates value-diverting strategies such as front-running, sandwiching, and time-bandit behavior from mixed or potentially value-creating strategies such as arbitrage and liquidations (Materwala et al., 2024).

Polygon-focused empirical work narrows this broad space operationally. “Unpacking Maximum Extractable Value on Polygon: A Study on Atomic Arbitrage” identifies four high-level categories—Atomic MEV, Semi-Atomic MEV, Statistical Arbitrage, and Intent-Based Swaps—and centers its analysis on Atomic Arbitrage as the dominant measurable backrunning-related form during its observation window (Vostrikov et al., 29 Aug 2025). In that framework, a transaction with $N$ swaps over asset set $\mathcal{A}$ is classified as AA iff it satisfies three conditions: $N \ge 2,$

$$\Delta(A)=\sum_{n=1}^{N}\delta_{n,A}\ge 0 \quad \forall A\in\mathcal{A},$$

$$\text{Profit}=\sum_{A\in\mathcal{A}}\Delta(A)\cdot P(A)-\tau-\beta > 0.$$

This criterion is notably stricter than many practical arbitrage formulations because it requires non-negative net change for every involved asset, not merely positive aggregate value.

Backrunning, in the same study, is defined as placing a transaction immediately after a target transaction to capture value created by that transaction. On Polygon, two operational variants are distinguished. “Spam-based backrunning” broadcasts multiple identical or near-identical transactions to improve the chance that one lands in the correct position. “Auction-based backrunning” uses a structured auction mechanism, concretely instantiated by FastLane in the Polygon setting (Vostrikov et al., 29 Aug 2025).

2. Ordering regimes and execution styles

The ordering architecture emphasized in the Polygon literature is historically public and competition-heavy. The Polygon-specific AA study argues that Polygon long lacked the dominant private-relay equilibrium associated with Ethereum, which left searchers competing more directly through mempool visibility, transaction spam, and Priority Gas Auction-like behavior. It therefore divides Polygon AA into two execution styles: Spam-based AA, defined as AA transactions not linked to FastLane, and FastLane-based AA, defined as the intersection of AA with the FastLane transaction set. The study covers Uniswap V2, Uniswap V3, Algebra, and partially Balancer, and presents FastLane as a structural change that reduces network strain while improving execution efficiency (Vostrikov et al., 29 Aug 2025).

A second Polygon-focused measurement paper, studying Polygon PoS as part of an L2/scaling comparison, reinforces this characterization from a different angle. It treats Polygon as a network where cheap gas enables PGAs, transaction spamming, and probabilistic search behavior. Its description of the backrun timing problem is especially revealing: if a target transaction appears in block $b$, a backrun sent after observing it will only arrive in block $b+2$, so a rival searcher landing in $b+1$ can capture the opportunity first. The same paper states that Polygon is “well known for its PGAs and transaction spamming,” and reports an example in which more than 10% of a block consists of empty transactions from a single searcher (Bagourd et al., 2023).

The two execution styles are also visible at transaction level. One plain arbitrage example in the AA study is transaction 0xc6591b9... in block 58,329,504, which arbitraged between Uniswap V3 and Uniswap V2: the trader exchanged 3.9383 BUSD for 5.3139 WMATIC, sent back 5.0018 WMATIC, and retained about 0.3121 WMATIC as profit. A later FastLane example, transaction 0xd331d7... in block 55,181,032, used a FastLane mechanism with length 3, generated about \$120,000 in trading volume, and yielded 2,801 WMATIC profit while interacting with QuickSwap’s WMATIC-WETH pool and additional liquidity sources (Vostrikov et al., 29 Aug 2025). These examples are illustrative of the broader shift from brute-force public competition to structured auction-mediated execution.

3. Empirical scale and chronology

Reported Polygon MEV totals are not directly additive, because the underlying studies measure different objects: lower-bound detected MEV, AA-only extraction, Atlas opportunity auctions, or opportunity-creation attribution. Read together, they describe a moving ecosystem rather than a single scalar.

Study and interval	Scope	Polygon result
(Bagourd et al., 2023) since inception to roughly mid-2023	Detected swaps and liquidations on Polygon PoS	Lower bound of \$213 million and 7.7 million detected MEV transactions
(Vostrikov et al., 29 Aug 2025) Jan 2023–Oct 2024	AA on Uniswap V2, V3, Algebra, and partial Balancer	About \$12 million AA MEV; AA is roughly 90% of backrunning-related MEV; FastLane is 13% of total extracted AA MEV
(Seoev et al., 16 Oct 2025) Dec 2024–Sep 2025	Atlas/FastLane opportunity auctions	223,356 OppTx; 17 unique searchers total; typically 5–8 active searchers per week
(Seoev et al., 30 Apr 2026) Mar 2–29 2026, plus Feb 4 2026 benchmark	Attribution of AA opportunity creation	360,026 AA transactions with total extracted MEV volume of \$334,799 in March; 96.7% effectively single-source in the February benchmark

Scope differences are substantial. The \$213 million figure is a lower bound derived from detected swaps and liquidations, with the authors stressing that the measurement is incomplete and priced mark-to-market rather than by realized exit value [2309.00629]. The \$12 million figure is narrower: it considers only AA, over 22 months and 23 million blocks, and interprets AA as the dominant measurable backrunning-related category rather than the entire MEV universe (Vostrikov et al., 29 Aug 2025). The Atlas paper does not estimate chain-wide MEV; it studies sealed-bid competition over opportunity transactions. The attribution paper measures which earlier transactions create realized AA opportunities, not all extracted value on Polygon (Seoev et al., 16 Oct 2025, Seoev et al., 30 Apr 2026).

The asset mix is also concentrated. In the lower-bound Polygon PoS study, profit-taking frequency is dominated by matic-network at 52.32%, usd-coin at 21.37%, and weth at 17.54%, with the top three summing to more than 91% of observed profit-taking frequency (Bagourd et al., 2023). The AA study’s case evidence similarly centers on WMATIC, WETH, WBTC, and BUSD, suggesting that profitable Polygon AA routes cluster around wrapped blue-chip assets and major routing tokens (Vostrikov et al., 29 Aug 2025).

The distribution of profits is heavily skewed. The Polygon PoS quantification study reports median detected MEV transaction profit of \$0.011** and mean profit of **\$27.74, while stating that 99.99% of all transactions have profit less than or equal to \$\mathcal{A}$01 (Bagourd et al., 2023). This suggests a dense field of low-margin opportunities with a small number of extreme outliers.

4. Searchers, validators, and bidding markets

Polygon MEV is not only a set of opportunities; it is also a market structure. The AA study describes a transition from noisy spam competition toward more structured auction-based routing. Over its 22-month window, Spam-based AA is more prevalent by transaction count, but FastLane-based AA captures disproportionately more value per transaction. FastLane’s share in both transaction count and MEV volume rises over time; the number of unique spam searchers trends toward consolidation, whereas the number of unique FastLane searchers increases. The same study’s most distributionally important claim is that validators receive over 75% of extracted MEV through direct bids and gas fees, indicating a strongly producer-favoring market (Vostrikov et al., 29 Aug 2025).

By the Atlas period, Polygon’s auction environment is described as a sealed-bid, low-latency, partially observable market. For each publicly visible OppTx, searchers submit bundles containing a signed SolverOperation to a FastLane node, which opens an auction window of about 250 ms, validates competing submissions, and forwards the winner to the validator. Participation requires bonded collateral in atlETH, and the dataset from December 2024 to September 2025 contains 223,356 OppTx, 17 unique searchers total, and no more than 15 unique active searchers per week, with 5–8 active searchers in most weeks (Seoev et al., 16 Oct 2025). In reduced form, the bidding problem is expressed through a bid fraction $\mathcal{A}$1, a win indicator $\mathcal{A}$2, and realized profit

$\mathcal{A}$3

The paper’s empirical takeaway is that winning more auctions is not equivalent to making more money: its history-conditioned PPO bidder often posts lower win rates but higher profit capture than more aggressive strategies, indicating that selective bid shading is economically superior in this market (Seoev et al., 16 Oct 2025).

The low-latency auction architecture also produces operational security externalities. A 2026 cryptographic study argues that some Polygon MEV searchers, under “sub-second response times in sealed-bid auctions,” employed unsafe ECDSA nonce-generation practices, including constant $\mathcal{A}$4 values, sequential reuse, and cross-wallet collisions. The paper provides concrete Polygon transaction-hash examples and states that passive attackers can recover private keys from public signatures via elementary modular algebra; the cross-wallet case is especially severe because one flawed shared signing backend can compromise multiple accounts simultaneously (Madhwal et al., 3 May 2026). This suggests that Polygon’s auction design has consequences not only for rents and congestion but also for signer implementation risk.

5. Opportunity creation, concentration, and measurement limits

A later line of work shifts attention from extraction to causation: which on-chain transactions create the conditions that Polygon arbitrageurs exploit. In a deterministic state-machine formulation, the attribution problem is defined over a block sequence $\mathcal{A}$5 with state evolution $\mathcal{A}$6. For a realized arbitrage $\mathcal{A}$7, the candidate source set is

$\mathcal{A}$8

searched in the same block and up to $\mathcal{A}$9 earlier blocks. The Polygon attribution paper evaluates four methods—bot-data-driven, simulation-based, coefficient-based, and Shapley-based—and recommends simulation as the main practical workhorse (Seoev et al., 30 Apr 2026).

Its strongest empirical result is the single-source hypothesis. On a February 2026 benchmark of 2,526 atomic arbitrage events, 96.7% are effectively single-source, meaning one transaction accounts for more than 70% of positive Shapley value; only 83 events, or 3.3%, exhibit tied maximum Shapley values indicative of genuine multi-source creation (Seoev et al., 30 Apr 2026). Opportunity creation is also highly concentrated. The paper states that the top 1% of arbitrageurs capture 80% of extracted value, the top 1% of opportunity-creating transactions generate a similar 80% share of MEV opportunities, and the top 5% of protocols generate 73% of value. Among opportunity transactions involving identifiable AMMs, protocol participation frequencies are led by Uniswap V3 at 58.0%, Algebra at 29.6%, Uniswap V4 at 28.9%, Uniswap V2 at 23.2%, and DODO at 8.2% (Seoev et al., 30 Apr 2026). This suggests that Polygon arbitrage opportunity supply is not diffuse; it is emitted disproportionately by a small subset of concentrated-liquidity venues and transactions.

Measurement of Polygon MEV remains methodologically incomplete. The AA study is explicit that its detection process is heuristic rather than ground-truth labeled; it depends on trace parsing, swap counting, asset-level delta computation, common-denomination pricing, fee and bid subtraction, and FastLane membership tests. It also states that the implementation is under-specified at low level: there is no trace-reconstruction algorithm, pool-graph algorithm, or precise token-pricing oracle specification beyond the profit equation, and protocol coverage is limited to Uniswap V2, Uniswap V3, Algebra, and only partial Balancer (Vostrikov et al., 29 Aug 2025). The same study excludes non-AA FastLane MEV from analysis and omits non-AA spam MEV altogether.

A broader methodological implication comes from an Ethereum-native but EVM-transferable detection paper. Its ABI-free, token-transfer-graph approach does not study Polygon directly, yet it suggests a plausible Polygon measurement pipeline in which GraphSAGE, GAT, or related models detect arbitrage from transfer topology rather than contract-specific ABIs (Park et al., 2023). This suggests, rather than demonstrates, that Polygon-wide MEV monitoring could become less brittle if it relies more on transfer-graph structure and less on manually maintained protocol decoders.

6. Mitigation, redistribution, and unresolved design choices

Polygon-specific empirical work is consistent in one policy implication: robust transaction ordering mechanisms are needed. The AA study explicitly identifies spam-heavy backrunning as harmful and points toward intent-based and solver-based systems—including CoW Swap, 1inch Fusion, and FastLane Atlas—as directions that may reduce harmful MEV at the source by bundling user intent and solver execution before third-party extraction occurs (Vostrikov et al., 29 Aug 2025). The same study predicts that “MEV prevention may replace MEV extraction as the dominant paradigm,” with searchers becoming solvers and opportunity transactions bundled with solver operations in a single transaction.

Privacy-preserving approaches offer a different mitigation logic. The PACCs framework—Private, Anonymous, Collateralizable Commitments—argues that if transaction intent is hidden before execution, the usual user-facing MEV opportunities can be removed and shifted toward censorship incentives instead. The paper explicitly states that scalability solutions like Polygon make the protocol “completely feasible in terms of usage cost,” while also making clear that the residual problem is censorship rather than ordinary frontrunning or sandwiching (McMenamin et al., 2023). In Polygon terms, this is a move from orderflow extraction toward relayer or sequencer suppression risk.

Survey work on Ethereum and L2 mitigation places the main design options into four families: fair ordering, privacy-preserving methods, smart contract-level protection, and Proposer-Builder Separation (PBS). One of its strongest conclusions is that fair ordering can significantly reduce front-running but does not by itself solve censorship, whereas delay encryption is “especially promising” because of its trustless character (Alipanahloo et al., 2024). A broader DeFi survey likewise distinguishes between reducing harmful MEV and merely democratizing it through private pools and structured relays (Materwala et al., 2024). These works do not study Polygon empirically, but they imply that any Polygon architecture choice must trade among throughput, latency, visibility, censorship resistance, and centralization.

Redistribution is a separate question from mitigation. A control-theoretic paper proposes a dynamic MEV extraction rate $N \ge 2,$0, where $N \ge 2,$1 is the fraction retained by extractors and $N \ge 2,$2 is returned to users. Its update rule,

$N \ge 2,$3

treats MEV sharing as a protocol parameter rather than a fixed market outcome (Braga et al., 2024). A plausible implication is that such mechanisms are especially relevant in Polygon-like settings where validators capture a large majority of measured ordering rents, although the paper itself is conceptual and not Polygon-specific.

Finally, the broader cross-domain literature implies that Polygon MEV cannot remain a purely single-chain topic. A survey of cross-domain MEV argues that sequencers and order-flow auctions have the greatest potential to mitigate MEV, but also face some of the biggest technical barriers (McMenamin, 2023). Read against the Polygon-specific studies, this suggests that future Polygon MEV analysis will have to incorporate bridge timing, sequencer last-look, and multi-domain orderflow routing, even though the current Polygon measurements remain dominated by same-domain atomic arbitrage and backrunning.