× 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.
*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 #1572 - 2019-03-01 19:31:01 to 19:31:10; Start Time 1551468661.0043306

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 20.000000000 () 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/52495 325956 0x542efefef0daa36a6ad36d6981a67c77687cabc55f6fe6dcbb96b37f6cb07a0f
0.278 100.000000002 (133.33333333444443) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/52495 325956 0x17f8ffe350d2d19f8702650d1192e939a8d4b00be87a550d53f3c93234a26445
2.892 20.000000000 (-133.33333333444443) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/18865 373564 0x0a6ab1aae19cc6950072efea5e2be0aad935472e1c86cf47fb619e644b822a02
3.086 100.000000002 (133.33333333444443) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/18865 373564 0x75b673a6c22ecb00ef7910968aa13090d5963365615cb7de3cc3f8c982d5ada5
4.432 5.000000000 (-180.95238095274377) 0x8bC3da587DeF887B5C822105729ee1D6aF05A5ca/322297 104738 0x65ff9e77816ba6279f76a824d2563802c014ba594fe027d688fa1923ecdeb071
6.716 289.417302684 (193.20692098675121) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/52495 325956 0xb64f325eef87989b0d6be5f7dbc840d80e519fa89fb54e3e12c1690d181eb526 7285680 7285680 / 0
6.716 289.417302684 (193.20692098675121) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/52495 325956 0xb64f325eef87989b0d6be5f7dbc840d80e519fa89fb54e3e12c1690d181eb526 7285680 7285680 / 0
7.458 289.417302684 (0.0) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/18865 373564 0xe00f90fe68f768cdd96e97b87e017be018601c8ab2babd57d2f3a2fdb920e7ad
9.550 123.975135000 (-80.04121633713345) 0x6c2d992b7739DFB363a473Cc4F28998b7f1f6dE2/5605 250000 0xdf8b5856aa4632e146d5a0367414f046f3e267a11ab8431a0d0e35b542ff7aeb
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This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. .
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.