× 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 #737 - 2018-10-02 03:28:22 to 03:28:30; Start Time 1538450902.1047184

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 0.000000000 () 0x00a329c0648769A73afAc7F9381E08FB43dBEA72/7407 60000 0x8a880553278deda1ab63565f8f4d0234a359382fb20fe9d7be2af6970903b173
2.724 8.036080000 (200.0) 0x41880a6C0BF00b0C159a8154d66B676a995D2383/17966 300000 0x9489219c138fba30bafa2cc9abb50e8c14034d3ccdc6cb4d98ee9e09025e4d7b
3.120 21.840000000 (92.4078393149302) 0xbB3a8c227bFBcB0fF70802Dee83246B507C57DcB/12081 240463 0x5c3c3f303c79b0d6ea719894392969dde8575af079d4ef54ffe598d912a83ee6 6437243 6437243 / 25
3.241 12.466174849 (-54.64803460169644) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12978 250000 0xcf66c8c17f3f209bf8798cba7e29830c1479bc283132b3446436528bf9b8c16a
3.292 12.466174848 (-8.021706835921089e-09) 0xF3A3fD06A875A09d7Cd5fd18FDBE22493fC86aBD/7656 368884 0xcada57999a240084e2bd3e7b2f8b4dde87e799cd2466d7a57b9ec1879091f696
3.660 12.207718560 (-2.094977746124176) 0xF3A3fD06A875A09d7Cd5fd18FDBE22493fC86aBD/7656 368884 0x81fef6f30061e39bffaa39ef92fbb3c3750fc517d592e4c9d2a66d7be3e7a12b
4.893 0.000000000 (-200.0) 0xB7605ddc0327406A7aC225B9dE87865E22aC5927/9338 60000 0xbf0cb8ac7bcb78ab415af4e2857bf44151689d7ecccffca16c5442d147c598cf
4.894 0.000000000 (0.0) 0xB7605ddc0327406A7aC225B9dE87865E22aC5927/9339 60000 0xa534f4a4f2b182f497f7d03b653a08a983cee5f757f4c007669f87f21922ba47
4.896 0.000000000 (0.0) 0xB7605ddc0327406A7aC225B9dE87865E22aC5927/9340 60000 0x7c89e65cdc3bdc98a8267f1c390fb4c8216b04ab2a08658023fcc92d89765c2b
5.466 12.207718561 (200.0) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12978 250000 0x9f3473ed49b6a834cfa326b863412c98e17012bee76ce55f19abbcc8a52b1d7e
6.058 21.840000000 (56.581068254207835) 0xbB3a8c227bFBcB0fF70802Dee83246B507C57DcB/12081 240367 0x2c0b254a78726961453b02474ba2edf6cd21199cf67adb0bdcf3e91714d191f0
8.014 134.000000000 (143.94250513347023) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/50282 121000 0x598ebaaba5ddefc025c2d0c1986ca23f994e87fd6fea7002579d8ebeecec81d2
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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.