× 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.
*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 #1594 - 2019-03-07 12:28:22 to 12:28:33; Start Time 1551961702.3057003

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 30.375632107 () 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 190386 0x9acd3cfe14b87c152d058fbda92e3d0b8621776f82e7c95b0efd0e73370179ff
0.150 20.000000000 (-41.193059711734094) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/54954 343516 0x15dbddc3727d839d0cd304223122e3aa7ab4849502ed70c659ff799ab1c66e4c
0.359 30.375632109 (41.193059718039) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/54954 343516 0x0831c4664f04866f9ffebd238becdf4ccdb94e5d71701ff32211c355324c164e
0.478 34.202961752 (11.853245523549198) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0xb0ec2e1940bc93c0952efbf6ff6d40cac3c60380b3722ae1205cb0d4f3f323f2
0.894 38.512534932 (11.853245529568829) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x9a496dd8669b3541a962f459c776ad9541df66e17d74725d5b7bbb94c98ff6f8
0.927 43.365114333 (11.853245530521889) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0xfa1fcbcac78f8c2d0056e52821165d751d527871af6981e676b9197c066689b9
1.857 48.829118738 (11.853245529559528) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x66ee80976d08270216afa16d13d9a7d48758a3a3b943d575810f946d40d22260 7322161 7322161 / 3
2.109 69.709835483 (35.230134907501714) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x763fafa73e52831fb2e0ee700c542145d9fa2b92291bea407643bf8c91cebffc
2.620 54.981587698 (-23.623513805950743) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x78766d8e4e5fc35d627ff71770577f9c6bcef8ebfc42fcf41b7035a593d2ea15
3.094 99.519739219 (57.654069916080964) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x0832554dcca6b945f2a17246ff36374045d63104a9076a019de81fbc81c51b58
3.485 61.909267747 (-46.596918582199386) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0xf55c2ac4f0b7a2a7c5e4dfe90df3007493e65bc8cefb6681df19cae5e8bdcedb
3.568 112.059226360 (57.654069916998964) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x4563831f2160af8886f2872bcc1d37b3ca530c4819f12a4cd270a4b4b210c361
3.575 88.383427371 (-23.6235138063714) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x11bccb518b2d43f42c51f98e8ac00fc4cff2ac4fc432d1eb2e53f30ba1c55862
3.642 78.493274753 (-11.85324553052467) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x9574f010d2ceca2fab16f21379d36d37e6bcd4d343eb4982ab7bdfdcead1b2ff
5.069 126.178688881 (46.59691858262753) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x7d08917e7b8f6bddd819efa4e7ca1dace9d18ff89dcd0a8f92d843ca68eeaf89
5.489 257.167660151 (68.33975155926979) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x7b8364adc2a98bf9758276b6ec01d6b00801b6088d1aa10b3cd063a101dba119
5.824 159.978931343 (-46.5969185843859) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x11d02cf06f80851853f93777ed2f67aaed9c9957aa96e9c855d5a90c9cfa65d9
5.935 202.833447555 (23.62351380742055) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0xad66654dddcd6c0314bc96a8b067bf6ad980954a0890d7317f80cd1b8a3a05e0
5.942 465.487803215 (78.60122818401638) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x6be4fa051de25d7dfeff4ce407c452c5fd14aba18d26f27a03ff829700e00533
6.005 413.399469996 (-11.853245531408401) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x6c96bab1287b0598b17bb05dc0fbc64f34a36e3d5beccbeb1cd9f4bf92669774
6.285 289.570785330 (-35.23013491049486) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x947cae7dc4467595fc4e9544f8b682f5ba3dc7c608fa9e40020cbead8686dbc5
6.763 180.136276692 (-46.59691858449185) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x2a5e361b2c03da2925661af463413bade17e7ace1cb631ae6db9e8f324943dad
7.283 326.056704281 (57.654069919544504) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0xfd70e52b18ed5cb3a756892ca1dd01a1595a60de01319a36bdab11f9703716c4
8.308 664.543596550 (68.3397515597458) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x33ab9a943fd983636d4e44e5ea79bec9ae49289c047f5ce93f748ce35b2687c6
8.387 228.390461946 (-97.68988660565327) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x4ddddcf659ce53e3386afebb49f593af582e152e95a883289806da00bc242241
9.836 142.077203680 (-46.59691858405599) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x34348a87212c2ac28a3a7dcb60e52515a3c07b3af55fb898e0c2ba04af2c2ba7
10.291 524.139266420 (114.6960724890671) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x24397e8c5d8d52da512178ce1c907f8bf67e6527aecbd4646a9065d9b4f97c9d
11.496 590.180813988 (11.853245531359244) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/1898 225835 0x78100412277f57feb1dab5d98efa6adcb184ee196fd75347fb5b45c2f6450056
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This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. .
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.