× 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 #160 - 2018-08-01 00:28:15 to 00:28:25; Start Time 1533083295.9585035

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 215.146474068 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/3989 128584 0x8fb75e7d02488a03582518fcbef17231969c42ce58bc65168087c6e759062597
0.602 215.146474068 (0.0) 0xf666666666E8025c503209ac580a5aA4b39A090c/311 128584 0x642d9cba28798075cba114121dca49a60b097429e89164966dd0903a29ac9683
0.638 295.542154341 (31.4852048002184) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3989 143512 0xf9e811f18bcce22237f4e53a30df58fb9e1e65c886d6fc726ca8a1cfc58eaf2a
1.291 294.363522797 (-0.39960000036557575) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4292 151050 0x707001c8131c16a5d63de95bb4df61884d87dd1dff65bd01c3766009c90ca1a4
2.607 294.952249842 (0.19980019959881146) 0xf666666666E8025c503209ac580a5aA4b39A090c/311 143490 0x9a934e6544ee5959ab30f95c467a3841bf951d22b35a18d6c369eb234362c3b7
3.196 500.417988754 (51.665433012603366) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4292 151050 0x07eb22aa88efab627832183ba120c6738e5005c294d89f85f719db538b5f78b0
3.773 501.418824731 (0.1998001996988874) 0xf666666666E8025c503209ac580a5aA4b39A090c/311 143490 0xc3b8f95c2263c665ecec7e28fa9a9fb20eb005f83894512e2af52277204c7b24
4.313 502.421662380 (0.19980019970824528) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3989 143512 0x40e77203ce9b5da5583472ebfb7a205c141d358bc03a89a4dc9b768a36641b94
4.923 850.710580881 (51.47891793068743) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4292 151050 0xf5d9c6210cb8820da77bfa22da1c242adc7efabe3699d13396e644c36e9aa476 6066098 6066098 / 5
9.403 499.681419019 (-51.989224149431365) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4292 151169 0x8dc0124026dd932540eb5d4b8b41587c2c7ad022dc2159099d6f30ad082ee5f4
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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.