× 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 #1585 - 2018-11-26 09:19:18 to 09:19:27; Start Time 1543223958.9304383

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 11.000000000 () 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/323 500000 0x391f41756e83400d68ceaff8167bb2eaa78f1f9080a9904e54b7d70f65fe8c0c
0.629 38.139974464 (110.45986392965173) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/323 500000 0xf00625aee195d25b6e261d4e84a4b7a2433142a040ebf7d6e84eefa81ee8335f
0.920 43.098171144 (12.206572769213327) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/323 500000 0x3cb4aa5229c14bcfbbf4b645b8c3f581a6c331813e923cabce2b6e0f99300445
0.946 12.429999999 (-110.45986393472676) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/323 500000 0xed229bf134567413b9aaadd8506d222a391a1c50e327bfbe9c80606ced548164
2.622 125.000000000 (163.8215818988854) 0x7CF767d7E2545a784E8B1F4E207B52D07BE15666/2 150000 0xe7296afb4b6a53f19f375882f4cc9c8114640db74c5d4b1b2c32e715e01c82bb
2.875 48.700933392 (-87.85107266615763) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/323 42000 0x9e7fc9c49547c88f74d985731415eaedcf5321860432456a8dc46079fd68845b
3.828 11.000000000 (-126.29930974262447) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/383 500000 0x2bff2fb0c09a9b745b0c3ec9c76247da5242f90fc09ead6bcb81dd1be897ceac
4.525 6.000000000 (-58.82352941176471) 0x8bC3da587DeF887B5C822105729ee1D6aF05A5ca/221680 104802 0xaa842036f487fc7365948ac6d261f908798389d14a30e95b1dd157bead3d00bb
5.391 38.139974464 (145.6275172529289) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/383 500000 0x426647dd57cfb8e4d6c2e0fd5970088868e3f4e962ad5f8acb01c8d14b096e3f
6.204 43.098171144 (12.206572769213327) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/383 500000 0x94692ca8615c31bbd95c391fb6e163da546ab76def786eaeebf0c450a5429990
6.588 48.700933392 (12.206572768480148) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/383 42000 0xaf5cbddc9cb7e75dee3bcf87759278e463e8f52ae4f628bde8cc47499d310a1b
8.284 12.429999999 (-118.66638175146853) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/383 500000 0xd1f54ba2957b56fc8cb7aa289026f6335362396539ad1ac5ffb8ac457d34e233
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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.