× 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 #1605 - 2018-11-26 18:28:25 to 18:28:37; Start Time 1543256905.0499394

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 11.000000000 () 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/808 500000 0x880a8aad36bcdfb7ef0bcfa62a9365d46da537a838aa588f39f1b42ba5f8aa92
0.155 20.000000000 (58.06451612903226) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/32620 404371 0x4317cd9a31c54e6a1f0ca7807fc3ef3b04772569345b5cc9371118957e010e53 6777655 6777655 / 33
1.534 52.009688746 (88.90383864567968) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/808 500000 0xbdecccceec3dd96f01242d237ebd70f842f788b9e17bec8143d7c6d9e126f1f5
1.597 125.000000000 (82.470413649207) 0x7CF767d7E2545a784E8B1F4E207B52D07BE15666/4 150000 0xef8a9f9d3dd7d319f481e4406ac01ca36ae93760599966a01267aa3b2b046c7e
4.229 58.770948282 (-72.07782550740312) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/808 42000 0x3247cdf51cfc8024263c4802f82852445183e219e15a50565046155e1755bd18
5.378 11.000000000 (-136.93650282326544) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/570 500000 0x4e0422f0c6303e4a72e21cda7947d72ae9071361df10ce9da79c0a2a965db122
6.580 52.009688746 (130.16946936943373) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/570 500000 0xd2169d2b3bd679a276d98589a2484ce11ed6c5f8dc6aeb3ca7be59f9a9baec97
7.042 20.000000000 (-88.90383864567968) 0xbADFfCB79Efe57e954fd79621b29B0c2DC170171/4401 144723 0xaa51507b1b6edbc6ea2a026d33c87fd040741ea8a58105f47a87d89ecd24c599
12.169 58.770948282 (98.43971445716257) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/570 42000 0xf7b5db78551a72565b4eb310e401787b6ea4351b3ae9ec3ba65aaa4262c2aa35
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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.