× 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 #1119 - 2018-10-21 09:34:30 to 09:34:33; Start Time 1540114470.6013937

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 540.095733948 () 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/24816 22000 0xcd58babecc604faf6de9989ba289e5bef40ab5714d01f1e8930caaad181b641b
1.275 10.000000000 (-192.72853841041038) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8433 373783 0x437cbfc8f1858d28a144f2a2432cac47a7bed2d953d1d2dd9681126cc52d630a
1.760 409.206748898 (190.45816888560333) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8433 22000 0x778034321a558c5943ea931b933a531d70f1e8c49e64dedf8aa0240c8b23f2af
1.957 10.000000000 (-190.45816888560333) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20605 373975 0x064bdfe71747c508e2f07ad848909442d42f4bd494ee4a7ef19eeb983d88db27
2.148 10.000000000 (0.0) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18409 374167 0x2b116d3173716376d9d88db2c68535ac81ed1115740ef129168333551aca6175
2.163 409.206748898 (190.45816888560333) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20605 22000 0xe3f1bf493ffe36cefdcfff4c6829a62ffe17d233af6f4b93c71496f242ae0e72
2.593 409.206748898 (0.0) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18409 22000 0x5c77839b890b4aae86384be6c9a305fc2f5cd426cbe042dae199625dd4dc33b2
2.716 10.000000000 (-190.45816888560333) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/8184 373783 0x356bcd0202360269ae93770ed5e4b71c4ca8f3cc4034f3a0d26a352ff997adbe
3.285 409.206748898 (190.45816888560333) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/8184 22000 0x2d178924f95474fbe811ffb6011b6fedba7992102f022e26e52c35208d108528
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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.