× 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 #1100 - 2018-10-19 19:36:59 to 19:37:10; Start Time 1539977819.5309591

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 6.100000000 () 0x91BbA529a4e469758CD3832305586fD1e8161eDd/3996 850000 0xdc5c17a7703f8ae87d5ca6894c94293bea52cf31495810a733c43d3250106c5d
2.534 6.100000000 (0.0) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20366 692778 0x2cf5579c1bd793620f42531321ad5732d74018f05fe13c1fe42c62b412e9d807
3.796 6.100000000 (0.0) 0x3bd189a9b1c11684b08f6464866dFa431362A06e/2013 1300000 0xe9a1ebd865264a2311b6f76abf16440b2643dca44706fdbc65a7ab85888495cf
4.290 6.100000000 (0.0) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18266 692266 0xae00b820b57ed4165e641a92f168a1b2b4d8035efe52128a3bcabeea8bcbe675
4.961 134.000000000 (182.58386866523912) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/52519 121000 0x2f9bf9037f758b2d11e3e3aab54f70415bf3e70dcc4df562351a11c3670b200a
4.986 134.000000000 (0.0) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/52520 121000 0x00c40d1314c2ee4b800885c3003df399e2b22a9bfe383a0116418913ee57ff1a
7.014 134.000000000 (0.0) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/52521 121000 0x9ac3f995e366247b2bf981b7ccc7a04fbea749f6a770508e53974fd52adeb88b
7.025 134.000000000 (0.0) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/52522 121000 0x1cd6295b8949564257cf587c13f50bb1982d60c301428e3521937ab9100c4e2b
9.651 7.014999999 (-180.10140765436373) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20366 22000 0x6010fa493778913dcde3cbf4c7dce2fce2307862953521cd71bf02f2575ba04e
11.359 7.014999999 (0.0) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18266 22000 0xc34bb6c71f0e450cddb56acef09ddb9f897bf8d440829f3791f64b3230a83819
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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.