× 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 #962 - 2018-10-20 13:28:41 to 13:28:42; Start Time 1540042121.4307919

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 1943.729519120 () 0x0000bAA8F700aF2476492b19e378d61b90454982/254 199404 0x210420b96c1477f4f21a39738473873a5bd767cfe708f2349c45768323a10ee2
0.075 1943.729519120 (0.0) 0xf666666666E8025c503209ac580a5aA4b39A090c/468 199404 0x004102675a184578ca898b497bdcc8204a2250dd834eda82221a7b31e214c238
0.312 2021.478699884 (3.9215686274114203) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4880 240766 0x29bf954cef61d8c0c0490fdf997a7f9e2453b65c805112264fc36650ce4b83f6
0.571 2215.851651796 (9.174311926608969) 0xf666666666E8025c503209ac580a5aA4b39A090c/468 234525 0x38e7438b019f4c08b575afc3922ccbe00175b1d9d9aa019778bcbdcdfea40892
0.603 2215.851651796 (0.0) 0x0000bAA8F700aF2476492b19e378d61b90454982/254 234567 0x2617a34c3774a61014faa722647056662936845bbf14d87b99c31b236296b632
0.608 3436.513789802 (43.19119669873653) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4880 240766 0xce89d1658c12a542cd8064de28751ae4ce2d5cf318faead03dca8d82b2f43d59
0.659 3443.386817382 (0.1998001998117001) 0xf666666666E8025c503209ac580a5aA4b39A090c/468 234525 0x53ab2365d4c3524d8113a6c3713f80223b482f78c809d836e9ddf372c22aae51
0.672 2021.478699884 (-52.038174151058044) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4880 241004 0x51d86fec7b2f00d964408e0fc41e364acc90909b794db9fd80b095d73d0047e3
0.698 3450.273591016 (52.224399613564756) 0x0000bAA8F700aF2476492b19e378d61b90454982/254 234567 0x63ef936ba325ad55dc92090990e3ac345922899e91fbadddbcf0f57cadfac477
0.759 3436.513789802 (-0.3996000007877588) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4880 241004 0xfe15dc30baccaeea97d572acf81b9cc318d10c98efda65646293407b31f2e953
0.809 5842.073442663 (51.85185185184547) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4880 240766 0xfbded2bf9866302e4246bf6641d2ee1bb585914cd4a37908cc10651053557fa9 6550490 6550490 / 159
0.953 5842.073442663 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4880 241004 0xc28978cc0a44076a1db1f12cda8e8607825de3758fa15d213e9223ceefe8c478
1.507 5842.073442663 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4880 240948 0x361b9813aac5ff2336e40003977414ddc985f95b0e02a2db41e92d51c866f6cf
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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.