× 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 #746 - 2018-10-03 17:15:42 to 17:15:51; Start Time 1538586942.2730334

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 295.804884905 () 0xf666666666E8025c503209ac580a5aA4b39A090c/413 184212 0x019f462936245da1706b193acb4892117ba90a73382350dbf6f81de8b78d9549
0.722 307.637080301 (3.9215686273859935) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225617 0x4ef5713f0132e2d4b94247fe4968b05ce9d854356d73638652f92921ccea9474
0.884 307.637080301 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225994 0xdd4af523dd2734fad2f9459501019f7bbc74356e54da041e989624229a471dc1
1.131 307.637080301 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225550 0x11ccf0cd78d1a6b7f4078fa3bcc34a8542b67291ec1b94e40d9bbc4ea87eeb0d
1.229 522.983036511 (51.851851851727005) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225617 0x582fae5a23e15cef3cde253156189536d684e4cc37cff37ad570d8c6e6e0fbe2
1.236 307.637080301 (-51.851851851727005) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225907 0xaaf00c5a3985b137005ea503ab95183cc5ade8bd62110fbd2a2d7a0aac3ebec7
1.310 334.259519942 (8.294930875446811) 0xf666666666E8025c503209ac580a5aA4b39A090c/413 214214 0x08ed0a31a39fbe9210c3db5a1f4f392e68b326d834f2b062a8431998b6dba1f6
1.681 524.029002584 (44.220440483928606) 0xf666666666E8025c503209ac580a5aA4b39A090c/413 214214 0x0a9b1a6126a14e8a860713907066b991fa7e56b398cb4e679fc52cc358101e0d
1.881 889.071162068 (51.66543301251371) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225617 0x0b983ffb3359a75f69b0688811d40d2924a2038e66f11c3782cd7e28eaadf13e 6446860 6446860 / 0
2.433 889.071162068 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225550 0xd830348157a210ff630c1943125d9132b86b703547412de56fb1f53b6edb1948
2.858 522.983036511 (-51.851851851778406) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225994 0xb42ca21ed1a55dd7ba8c20660a12dc9b501b094cad64e3fe3c0b9ca5aeeec926
4.092 522.983036511 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225907 0x4e2afa28a0f6e960ef243d95018f05786ea558a8d3c68662872a574f4a3263e2
4.544 4.281250000 (-196.75210317897327) 0x8bC3da587DeF887B5C822105729ee1D6aF05A5ca/169588 98096 0xde907b9351c536f0670bfd0bd37bdabb492f4ff662d6889f79764d78830010e0
7.321 889.071162068 (198.0830633276785) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225907 0x45b26157f971051ae7ca32d4809c3689c013a8b1c4318fc1acf200577be33ac3
8.290 889.071162068 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225994 0x2d6ae4ee95e26010095943feaa755e0e998832af1ac6d390e25c9b1a9dbf1c70
8.885 522.983036511 (-51.851851851778406) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4790 225550 0x302e78bfb54e5ce94f6b99cdffcf1005118e97cc1380775dce1089b114428b6d
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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.