× 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 #284 - 2018-08-20 08:06:48 to 08:06:53; Start Time 1534752408.9114842

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 907.517988467 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/4124 184532 0x76a477506a0e8e14f9e5d218edbcabbbfdae57f2ca0a82e95abab100e846f996
0.288 823.554313519 (-9.700770424397795) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4494 225757 0x6ccdcdb55a14f06798263c50faf61b613b31c38a32af93c2411b14c11b800ad1
0.394 1400.042332982 (51.85185185183187) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4494 225757 0x8b4da0f2286566df68a9c2a6582f9cec39d9eade7984ad96da1c6cee19e2a4ef
0.406 1394.967126987 (-0.3631619905183641) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4494 226240 0xe5193b11b3f6200e1db27ec044a5edccfc836c6bad491335ac487b40ee72a651
0.615 1397.757061240 (0.19980019973051677) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4124 214536 0xf4899976e5b00510b060b2997d76d0092f0557cbf35af609ac13febe4d489c8f
0.671 1579.465479201 (12.206572769940436) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4124 214536 0xb0f294f1a16db4f5a5a0ce7011c81a01b6073814a1adddf61cda44b21d8af693
0.866 2375.134094544 (40.24016088129515) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4494 226033 0x346bcbe722b4c98c4d57839c9996de32dc40ad2898a3e6d3b19230bef2a35a2a
0.884 2379.884362733 (0.19980019979650215) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4124 214536 0x6396b235b5f7552f04fd52286d7e1d282218a9b92182faa46bbea3d33c0581cf
1.188 4043.311166481 (51.794369210228666) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4124 214536 0x69fa3731aa582c8e9950faaa5f84c9377c90b6af6abe49a7ec7d88b63bc691f7
1.288 4035.240685111 (-0.19980019979470928) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4494 226115 0xb4e98eb0f042ab27732ce4bd896bed6cab68447d5addc9c3e0525349d14cec07
1.403 6867.180441872 (51.95065800113109) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4124 214536 0xce20e4390983c9bd87acb79f0a5a987a4bb327cfd283ecbf755e7dac46d6d66b
1.582 6853.473494883 (-0.1998001997890453) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4494 226240 0x261b84345405281ff4b6cae34e9316c1de12a71ff870dc541fd72bb84c845975
1.645 6859.909164688 (0.09385969843856642) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4494 226115 0x2d71f8fbe936a4a0f4ecdee211898756b3be2a198e958f6c3dd0b4e2ea4d6e66
1.851 7773.635756382 (12.488109977759082) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4494 226033 0x75e48dcc767ced77ac08ea559586eec9cdf210bd630fb6a47da16fc91ca3fa37 6180313 6180313 / 0
4.227 50.200692020 (-197.4334488022048) 0x41880a6C0BF00b0C159a8154d66B676a995D2383/17506 550000 0xe2d626815ab35637e67ac803fde6276f88b8c3041570887bef01d017f0955dd3 6180313 6180313 / 7
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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.