× 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.
*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 - 2018-11-01 06:56:41 to 06:56:52; Start Time 1541055401.4512599

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 9.600240000 () 0x00dEe1F836998bcc736022f314dF906588d44808/2443 1600000 0xe835ec21aa5e855978d96360cd6cdcefa6cf7b089a7dfb788ddf1684982385f2
1.725 14.000000000 (37.285722518076085) 0xa8282A3D5C9699A3E5e8D387c8E2f51fc8110389/2974 250000 0x7c300f727089b33d2ddc4a65a4cf5e60aeec6a0d6a58867fbe524b1a0c1e4c81
3.299 5.000000001 (-94.7368420897507) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/27306 482484 0x2d2a0436af277cc280e94ef024956cfaac0bfc5359e36aa2177f95572bed7c33
3.526 5.000000000 (-1.9999999998e-08) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/9135 754494 0xf32c890a671b4a1d588c1d4cda912be0eb0cb4661564f82ef980870a256c66ce
3.767 9.600240002 (63.0159504414974) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/27306 482484 0xae33928e23c196a08c9c059adfd15c1864022e362407aecfd214799331c748e4
4.183 5.000000000 (-63.0159504414974) 0x3bd189a9b1c11684b08f6464866dFa431362A06e/2523 1300000 0x8f87ba2a3fb8c18ea8d2335e91f70348b25d18171416eed749531903eb33242e 6622223 6622223 / 127
4.183 5.000000000 (-63.0159504414974) 0x3bd189a9b1c11684b08f6464866dFa431362A06e/2523 1300000 0x8f87ba2a3fb8c18ea8d2335e91f70348b25d18171416eed749531903eb33242e 6622223 6622223 / 127
5.071 134.000000000 (185.6115107913669) 0xd6cB6744B7f2Da784c5aFd6B023D957188522198/36065 121000 0xcc423deef65564cb8d2e21094ad0c197310452423fefcc02c107e4e836444fcd
5.082 5.000000001 (-185.61151078859274) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20927 482484 0x03d4012b694fd3d13c1f158fc14a253e198c4535e80083afa2165b10e19f927a
7.934 9.600240002 (63.01595042348291) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20927 482484 0x2e091b0e20af5a7847827185da35022fe656176226979a3cb282409144047d1e
8.372 5.000000001 (-63.01595042348291) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18643 482484 0xb1d7af0454e964077bf259c6e21bb737ea1bc2aa6ccde9d71fe16308b5d9638f
11.241 14.000000000 (94.7368420897507) 0xa8282A3D5C9699A3E5e8D387c8E2f51fc8110389/2975 250000 0x7553289de66f5099e8eadcad58df5eebc7e3a044dbabcff724f5b30e838fb51c
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This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. .
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.