× 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.

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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 #360 - 2018-08-31 04:33:01 to 04:33:11; Start Time 1535689981.4802334

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 351.841000000 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/4166 129080 0x1c5b4cc3900b5fc9731e0cdb9254420b68ac314a91966d610ba927e5c67fe1a7
0.874 4860.640362224 (173.00011449058644) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4567 151393 0xfb31379c2d801b5b02d708cc878ef337db0fcce1cde6272c45ce2a1d3ca0513a
0.928 4870.361642948 (0.1998001997910013) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4166 144096 0x4183b8df6e34090d592ea8da1c2b01c6729b150a202bf2c89aa11d5fa17efde6
1.240 8263.088615780 (51.66543301258282) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4567 151393 0x31387c1235bbfd4db84787b7129bb21b0ccac2295bb28aef653a75934998f579
1.247 8279.614793011 (0.1998001997934362) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4166 144096 0xd991a17440176ce75c3f7f13cd1408a884b483d43441e9018f7c1b500cf566dc
1.788 14075.345148119 (51.85185185185384) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4166 144096 0x7c1b96270f5206f1fdbe3dad6bab7ab7e51a80e603e4bba50c3e25f59de41c99
1.855 8246.061471236 (-52.23043311104239) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4567 151559 0xc53f21751c41ec291e0e074e5833a85b74d8588a8c77834bdfd1e22ad54618d2
2.029 14047.250646826 (52.04421079171889) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4567 151393 0x8ba55150dff08a8ac7bce0494e5aee214d6a2223664db3573c142ea3114e8550 6244604 6244604 / 0
2.140 14018.304501101 (-0.20627524075281328) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4567 151559 0x2c12354940e77d7291a076b6d5de62dc026c76e0b7cee272e88ac4ec50ad5e44
10.331 8610.799384731 (-47.79248125468742) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4567 151665 0xe23ce9a73ad45633b970bbd3a7cb2e47d09bbbb55fce9b321ef5f1587c2ff1e4
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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.