× 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 #153 - 2018-07-31 14:00:16 to 14:00:22; Start Time 1533045616.1125548

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 61.000000000 () 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/432 300000 0x25307c78178ea3bf92557e301ac33215cb3cb96d0d1310c87e30d56586479500
0.562 61.000000001 (1.6393442622816448e-09) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11786 354129 0xff32720ef1aa38d66d367c2e62e86d90f062ce1d5c79195628a7ee43038ded19
1.116 70.150000000 (13.953488370461658) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/432 300000 0x3488de57b56402a8c7cc6ed8d8eb5bfe1b09a6df3f7bc0e82292a31ea482e686
1.689 77.165000002 (9.523809526395494) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11786 354129 0x93669f4ad99306ef1c6cf4019534d904a7f733cd319e90c1d74c1f47f237ed12
2.239 80.672500000 (4.444444441853876) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/432 300000 0xbc4b4f3355b598114d6b97d4a7f093ae3a718b328d065d3f2aec4692ffad879e
2.820 93.369650004 (14.590890774112111) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11786 354129 0xa0424ec8d3eec438bbe3052278f25ae7e1b16fd05e290203a55e65091d7fe316
3.381 93.369650005 (1.0710118330226198e-09) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/432 300000 0x1daa5898e5ffa50ed85d8f5fa7950407c57d176dca0c97770ae8d29bce8eb7be
3.941 112.977276506 (19.00452488683397) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11786 354129 0xd49e784f2bf267c91ee0743be80ba0d6f56ec3c3a03681e1ab6696020e6f716c
4.496 112.977276507 (8.851337462914203e-10) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/432 300000 0xf838613b70a1aed75576c77d22ec1aef8d804bda574f0380bd568f3709465c27
5.062 136.702504573 (19.00452488653712) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11786 354129 0xf231a42a16e8e4ad942b9834de11ab410f91b05abcdd722b9801a27d626a7e2a
5.632 136.702504574 (7.315154927994286e-10) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/432 300000 0xeda4c4e74c83527d8e8c3578da4b0e59a9bf36225171de30a93d269201cc0401
6.186 165.410030535 (19.004524887153416) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11786 354129 0x6fde19c4c8ea380dbee44c9d159e62b9fd342a5802d1d3c5cbc416ea4f4cd937
6.743 165.410030536 (6.045582585062046e-10) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/432 300000 0xcf09b54cb475d1172c54e889c1f2b0962a7523c13168f103f0167ee659352bf6
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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.