× 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 #753 - 2018-10-04 21:22:38 to 21:22:49; Start Time 1538688158.2902143

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 5.000000000 () 0x9D22a13c36E10B9630dDa6A3A3C0aF2076AB88D5/2784 850000 0x2cbf76d410709ab09424a50679360107d7ca9ae763074c6080d51c3a7c1409f8
0.255 5.000000001 (1.9999999998e-08) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/18554 445177 0x47284008fc7d163cef6f700aebba8ef5e7d681b9047e00e432ba01e5adeca2d9
0.580 5.000000001 (0.0) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/6372 429985 0xe0e663f4812edaa406d67d2e4996e6110e45973ce409b379d1340c1d293a3a34
1.668 8.100000000 (47.32824425592914) 0x377e2A31a95D8b02d564BE7cE834a7bd4a57d6F1/2756 60015 0x3200de1eadc1713835d03045802c12fa9cf19db1e21cef831c1374b6a39dcc38
3.731 5.000000001 (-47.32824425592914) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/19562 445049 0x2b1548ea60df4229fcbc5dca9d61fc3e36bca9e234fea02ab88c908442fa2361
4.558 5.000000001 (0.0) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/17676 430113 0x70f571aa57188481a5d455bebcc53b059d1cfa7da0ab065332276519766d4154
5.176 5.000000001 (0.0) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/7494 445177 0x7036a96427df6473070ef70821930387adb1e21583534b23af3839e44083145d
5.851 140.000000000 (186.20689654906064) 0xcba1766e19f32d79E3BA7A2764f04a7042324F0A/14 80000 0xec3e9455a57add5cbe96413b1968950187c7667e5e16f20a4d4af30e4f5280fe
8.034 11.000000001 (-170.86092714986185) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/7494 445177 0xbca43c6bb8971fc78280becbcab854ae2606d81e7b9810cf7b93695b9c4bec06
8.339 11.000000001 (0.0) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/18554 445177 0x672e9c0f0420094e50d2b615427b43bee97f8142f51ec38896f468605db935f4
8.645 11.000000001 (0.0) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/19562 445049 0x47dfa6f26e9ab5d120c263647162575cded2c0e6e8edf365fc6847944ba329d6
8.760 11.000000001 (0.0) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/17676 430113 0x414e9ac600e1e3bb7c4b1d811b492ba91ced8e22e41babc532465643ff2d893f
8.892 11.000000001 (0.0) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/6372 429985 0x96a247cc6077afe957f63ae81039c380414ecc95f80e41a5bfa9b110a67df166
11.548 3.000000000 (-114.28571429183674) 0x21Bec7dC23A8eE2573403C5935DE74d68C89F06e/577 218252 0xaa334271db0992db2e0d8acc53a5bd065c4d0838bee55baddeb3d4798d307b01
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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.