× 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 #1586 - 2018-11-26 09:27:21 to 09:27:28; Start Time 1543224441.6077518

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 11.000000000 () 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/330 500000 0x339e6b0a25fde1c97581f0b8f027be5835ebc2025c4935afe8aa4599699aef99
0.357 10.000000000 (-9.523809523809524) 0x8177573B5557e3a2213d4aEc44abe7BaEF6D737D/3780 850000 0x063b8b9ace7b242d2c87b4f1a8c595e69d0e9865eef57858bf0ad02a2c363420 6775409 6775409 / 210
0.357 10.000000000 (-9.523809523809524) 0x8177573B5557e3a2213d4aEc44abe7BaEF6D737D/3780 850000 0x063b8b9ace7b242d2c87b4f1a8c595e69d0e9865eef57858bf0ad02a2c363420 6775409 6775409 / 210
0.460 38.139974464 (116.90897129595952) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/330 500000 0x37189e9bfc0b8cfb559cde796d5fecd5dd455de33573250457afac53a8e3d914
0.803 38.139974464 (0.0) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/385 500000 0xe8fd2557224e7d7a32d9fe9d085ad0eaf4c7039ff799805281a6875e85e15409
0.987 43.098171144 (12.206572769213327) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/330 500000 0x3763188fc295f6e923923253cbb3f8b222fb7da56180e76f87b7d8ab936057c5
1.220 48.700933392 (12.206572768480148) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/330 42000 0x991a26aa7ae807288ab9ed924965712caf240d3b4dd1895b7482a6ff20d8fa29
1.236 43.098171144 (-12.206572768480148) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/385 500000 0x5680fd4e9e480fcb38fe9d43a21ccf86812421a994f02be46147cb2053f0d7b4
1.351 11.000000000 (-118.66638174721362) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/385 500000 0x6ef4fcc8355f30938ce6dd675f4e1e54ef45388443bc55ca16e2e2ba1a829008
1.406 48.700933392 (126.29930974262447) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/385 42000 0x279fea5d414f6cd192552fe531d069df0fa5db29c79f80c563cae95b5c8f26a0
1.716 12.429999999 (-118.66638175146853) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/330 500000 0x9b8ca372188b94cb06fd413c15778602c4763dd08066bf1289f07289cfdd8a26
2.105 12.429999999 (0.0) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/385 500000 0x50620988a3807563c4936a370e2b111276d88b0bf9b011024436f01869a48e6a
5.065 60.100000000 (131.44905556778502) 0xEcC996953e976A305ee585A9C7BBbcc85D1C467b/6509 150000 0xc5c008c51342e97f323d8645afc86096f3a11d8b491251049a6eee5abe6bda81
5.381 134.000000000 (76.14631633178773) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/56744 121000 0xa8161519bceae37433b7ce8d504f4b2eaaaaf8eb7f0b28919619e93c1cb91eb5
6.844 50.000000000 (-91.30434782608695) 0x274F3c32C90517975e29Dfc209a23f315c1e5Fc7/121438 96562 0x129d3bd73db01c26c2fc885be3a9f7c13fb93694d736e09e40c9fadade7b7781
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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.