× 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 #1076 - 2018-10-18 16:07:40 to 16:07:48; Start Time 1539878860.0993805

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 3.000000000 () 0x8bC3da587DeF887B5C822105729ee1D6aF05A5ca/183718 98352 0x47217fdfc6c1b6534434649d66d7455588027027316866e36ac2b67f4ae23c3a
2.639 5.057923460 (51.07825782202205) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 191957 0xfab4c83ca9e4692163f6428ebcc89dc5c42cf1f0edcecd899b6585074466bf2b
2.929 5.057923461 (1.9770959521422754e-08) 0xF3A3fD06A875A09d7Cd5fd18FDBE22493fC86aBD/7854 412815 0xf09852279b590c7a3d5b7b091a65aef4fe61af3ae0f20677f3cef57b35d70114
3.075 5.715453509 (12.20657273631074) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 191957 0x10879bfcfd199b11b72b55a515917f98380d5e04a3c236f418a6e458659dda91
3.269 5.057923462 (-12.206572716613428) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/13324 250000 0x984bdf61861f3b5360330f87bf047486c4588fd47cf3e8195b205d8e39b1e090
4.832 25.000000000 (132.69097955626432) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0x6d964e6307f9bed3ee88f439589c689d9ee286f8075e659e40e6d29ddda27c98
4.959 25.050000000 (0.1998001998001998) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0x0b91a29c7a8b3a6567bcacfc2c85db9dbed56f83ccfedf2e953c4445cc373051
5.208 28.807500000 (13.953488372093023) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0x769d3752abc24af5404d9565e09f40d37abc3fac3cebe0b68b9e8470846f6556
5.264 28.750000000 (-0.1998001998001998) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0x67e3c3aa6bdd24220c5efecc3ba778623e4564535b7ed9e13de4303fd496224f
5.510 38.097918750 (27.967718142309405) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0xdc53c78fc109b8e137700192e8199b31834976810206d7f953d55213385e0128
5.601 33.128625000 (-13.953488372093023) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0x5f08e182308056c3a4eca178f6390c64a1d0394400484b197523c753e4db4c73
5.630 33.062500000 (-0.1998001998001998) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0x12dc732d935d2e57325d4f4c0b82466a4f8ee36ac7c6a2cf41329d8ca16aca8e
5.737 50.384497547 (41.5161672826963) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0x4ade5e18d3a6f041e72162c2a2d0200829836b9a8eea888ce9b99dedcb19dab3
5.765 57.942172180 (13.95348837372461) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0x9586791cc18826373ce69e1890971a5fe29589ea6a079fd04ddb73b9d9aee03e
5.811 43.812606562 (-27.771797634832694) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0xaca12a5102391ec9ccaa40035241356b771eb942bdfb3131f4e4100656c2fe6b
6.024 66.633498008 (41.32493678224074) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0xfb2ddf921bfa5973449fcb4d4a4dd4f3f8e62a578315e965a814808756a6396b
6.040 38.021875000 (-54.67779089720102) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0x87d3c37117a9379bc18fbddd61531f0a6593fd8096fe716767b475618f94e2a2
6.081 57.826519142 (41.324936780180785) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0xffbe95d38eb0023edc6fba88a5e8ef6ac92a5d4bc0a49141033541e02447a04f
6.190 66.500497014 (13.953488373140525) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0xc9d8aa12f723ef8d618c7224f349e3a87123dd654284d8921cfaaad239c45148
6.209 43.725156250 (-41.32493678118846) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0xd68bec3787f144b43dd2eefabf38a8cb85423d90b9a0c340f2ba826f23ab4fbb
6.438 76.628522710 (54.67779089816699) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0x3c9a381c10827b740a523ff664679e2dd91cf356eff51fa1b57e3f83da56d9cc
6.584 50.283929688 (-41.51616728574881) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0x1c28dca213e2bca9a030312f9d57c748757d62a937d2fdc6210356fe961d3f5d
6.641 76.475571567 (41.324936781363164) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0x2fd30ab3c8eb9da0298550c5c903ed81c3be3cac5deb2b008afe6aeb74710839
6.760 116.309784909 (41.32493677958263) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 22000 0x818f6c98af6dc90b87f825898376c56d9ac8a7fd3223d721012904bfbefa6ce4
6.843 88.122801117 (-27.575822758916864) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0xcb157b9754c78ad5f3ce149d156583433f680579d0792ae16162d2885486529b
7.098 87.946907303 (-0.19980019911252375) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0x6c5b0ec928e53ade62a5c354c2a301cd4d6be9b52b9716b18118bd19fa1f602b
7.118 101.341221285 (14.15230218811423) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1156 227470 0xca95735448a77256c28267da5dcf3aa925dfb947e003f0c10a96954aee733842 6538975 6538975 / 1
8.184 101.138943399 (-0.1998001990127619) 0xb8D76f4BC2518F8eb508bf0Ccca76f8F9DD57a3f/1167 300000 0x2170bcc044d04a2973c769fc19c0bc60b49c74ed09f30490a444f3cc3c40e904
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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.