× 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 #20 - 2018-07-01 11:59:21 to 11:59:29; Start Time 1530446361.7140186

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 16.010000010 () 0x00003e132a49A9C0F066cE2Fe2aB48B886B340cF/926 1416000 0x8aa0130f24324dabaaaa85856fe4348f22eae2a8ede36ddf8604423b0a29e17a
2.916 147.100000000 (160.73815214513286) 0x00003e132a49A9C0F066cE2Fe2aB48B886B340cF/926 1416000 0xf8da81422cf4f826efaaad395fd54d7c02038c6a04dd0e3a237136800fcd3bd0
3.059 161.810000002 (9.523809525042738) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11318 377773 0x41144ee83eb03606d3593efde1c00ac7a545775349117f95b5689612e6b5132b
3.159 167.900000000 (3.6941554687228524) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/14756 308281 0xfb272659db01b62a5b920dbf8d23db87ff1760f6a7177f9c88510880041772a4
3.537 195.790100004 (15.337288534218146) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11318 377773 0xba19931dd4c00bb0ccab1423e28f2e36c9ce903f76285c4f7786b3811ad74699
3.639 196.790100004 (0.5094500435730697) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/14756 308281 0x6c665a9c3288c9c42e2b79ce8dc2856154c97f72984742dfeccd13b6836aad5d
3.847 236.906021006 (18.499552593911726) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11318 377773 0x787ff659d9ac20bd9fd4884b642a068466f7770c55a7d8bf66edf3ad8ee1f74f
3.957 237.906021006 (0.42121930849206507) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/14756 308281 0x5a9e36fbaca312ec45c953aea3516aedb55a6bf3e17db07cb926b496e124fb6b
4.125 260.596623107 (9.103503208643572) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11318 377773 0x4f40f058456b9378b0898d4a315677ec0336d187c7a778cb9742bf2708ec03f0
4.222 273.591924156 (4.86543603960942) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/14756 308281 0x93611e61bc4e2dde6e5d22d83633d8b1fac3fe1b3d9c25a292e977ccb80aa435
4.393 286.656285418 (4.663776175896695) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11318 377773 0xc628af900c2c83376d0b8910cefa686cea2b1a11900365dc9a1e4bf2136a0a13
4.516 314.630712779 (9.304850244519914) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/14756 308281 0x558b780a8e128aa1b49332449dbadf63ab87a0d46be59ad24e62f73f04843a05
7.353 161.957100001 (-64.06945737342068) 0x00003e132a49A9C0F066cE2Fe2aB48B886B340cF/926 1416000 0xb6ddf2dcb8a04bee1b9c2594736c490559a32213bd422ee4ad15280800557642
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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.