× Welcome to frontrun me, the premier site for visualizing Ethereum gas auctions. These gas auctions are often bids by frontrunners, arbitrageurs, and other programmatic network actors that seek to exploit inefficiencies in on-chain systems, ultimately profiting miners. On this site, we monitor these behaviors on the network in real-time as these bots compete for block priority in rapid-fire all-pay auctions*, showing users and mechanism designers the potential rent-seeking economy created by orderbook inefficiencies on-chain. Timing data is sourced from a global network of 8 nodes peered deeply with Ethereum. Read our full paper!
*Modern auctions are a special kind of all-pay auction, in which the winner of the auction pays some constant percentage of their bid; they do not pay full gas, as they do not pay for execution, but they must pay for attempted execution.

WARNING: THIS IS AN ALPHA VERSION OF THE SITE WITH PRELIMINARY DATA AND KNOWN BUGS. Use at your own risk and follow @ProjectChicago_ for announcements on release and more data being added.

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Individual Real-Time Auction Data

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Note: only auctions with a single detected "winner" (mined with 2+ log events) are displayed in this view. Other auctions may contain multiple sub-auctions, as the split is heuristic.

Gas Auction #1589 - 2018-11-26 10:24:09 to 10:24:11; Start Time 1543227849.4185784

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 120.000000000 () 0xf646CBe3B030fb6c2569215F0117dbA58baDB95E/53143 105000 0x7aab32d1564781c30eff27dffecf94cbf57b21293b60d831884f61150c9fb5a1
0.935 38.139975465 (-103.52856612541652) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/387 500000 0x37d6e483dbaeef7f8cc8be03a0a7250b7c8962d791095e4ca800eb8af4ae1079
1.138 11.000000000 (-110.45986575361837) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/387 500000 0xdc4f11e37fedb36b048f6df1a408069e8849b282a8a38d202010220a3992faaa
1.387 41.953974012 (116.90897459361771) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/409 500000 0x00b3aba6bba3305fa7b59e90cbca70d3c51a31b2a47e19a2733f1c1acfd58afa
1.438 43.098172275 (2.6905805742726745) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/387 500000 0x811b354aa1ca92caf92b433ece0c2c37326cca98174de867dd07d3c32feece52
1.467 11.000000000 (-118.66638344760973) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/409 500000 0x61510e65fb95cf2f9d7d4bea73daa1b80423b974ad84e968ea4a44691c0f9820
1.724 47.407990633 (124.66784163750981) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/409 500000 0x2c16fc1b0011ca7bc5679463043c8ec7c344cad7bfbc5eb3c4c75715a659364d
1.808 48.700934670 (2.6905805739139637) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/387 500000 0x109b76447daac394169997d6ca6085009327651aeef97ffa35176e09c97e6001
2.290 55.032056177 (12.206572769772016) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/387 42000 0x9cbb4186768fd5c8d26208f20b22e457e2217b9ffef0392f0ef90ea0913cb044
2.557 53.571029415 (-2.6905805742735236) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/409 42000 0xd8eace80810e2166b02dc764af563daa4a74cfec84217c77cc920f7d92c0ddef
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This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1650441.
We would also like to thank NSF CNS-1330599, CNS-1514163, CNS-1564102, and CNS-1704615, ARL W911NF-16-1-0145, and IC3 Industry Partners.
Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.