× 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 #1608 - 2019-03-12 04:41:20 to 04:41:26; Start Time 1552365680.659855

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 63.000000000 () 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0x5aeba43fb1cc19c9de608bcda1da0d21918392d74e420e5644ece5ceaa303809
0.150 72.450000000 (13.953488372093023) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0x74b733745a90953b9ba75e5853b6c80a650be7df664d5f3c0e093960dc98ef36
0.517 69.300000002 (-4.444444441559867) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0x920423b3a225cea45e94cf43ad899cf87ce2105beb4f28a935c6f7ab22329041
0.916 122.769177308 (55.67699935497787) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0x0644440832fd34538a1de0845e549b26352bdecb6380b02c24f4c525d3a222de
1.131 122.769177309 (8.145366955479015e-10) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0xdef70055217eb4fd7df43a5daf5a9f6f8b4f52bf1f9b9dd815e64e127983aab1
1.230 101.462130007 (-19.004524887305635) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0x15e9f12c31f48a451339ec1675d22bdd80498022f8c950d5779692f9254f3d13
1.382 148.550704543 (37.66892577395483) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0x1a45cbe0d5ae962aa29d259910654e2e68c2a642b1333214c9e6f9e423f9a370
1.397 179.746352498 (19.004524887412604) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0xb4a0f15831818f71fa8dec4ff0fc0fcf4d875451093db9b469a13c55fe8cab32
1.694 148.550704544 (-19.004524886745514) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0x3a92532e9c0c590f8bb5917b1fbef81b419ee832a17eddda77b573fdaf079359
2.009 263.166634695 (55.676999352444554) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0x6217554644bf150ec831dbe0b5a358eb80f33bb6d4952ba2e87ba4b4e007bd19
2.050 263.166634694 (-3.7998737992027805e-10) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0x891897f292831e85e0c958edc5e37195722ee11ada07ed2ba10a66ec51e636a0
2.171 385.302269859 (37.66892577499612) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0x79363e5fda4dc60df45b030a7aee77fce2ec137a176f0d5333c20d63d1eea04e
2.396 217.493086523 (-55.67699935288053) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0xad3c6c7dee74f41d8d3d1513753be6ff8ce06754e0be67bd62843401c0e85cc5
2.442 217.493086524 (4.5978472970539716e-10) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0x6e77909547af5b5826323197d778a063a1596653a3c460d0da2be3cd009c5748
2.572 421.125828007 (63.772850083072576) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0x737a90ad3e7645681a7254ccc5e5c89f6d5d258ffc4d176c58662480820a4c9d
2.676 83.853000005 (-133.5789974917464) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0xa358a7975b7ae150bd636013652c3c04a458495311fbed2a8f07f91a2e792397
2.760 318.431627982 (116.62321235132083) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0xbe26ef4cbf48234126d862c221930fe18c91739264f2708137435feac3ab0411
2.874 83.853000004 (-116.62321235210788) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0x10caaeec4b6f214926e2afd791802588563d655869f031c340b0be5b5a95d67a
3.147 318.431627981 (116.62321235190063) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0xf96b8ab4e38049cd2f1b0e8e7cd22a9e2ab4738806abdb4f8b9b7a8aa3062122
3.161 179.746352499 (-55.67699935206899) 0xAf4b59e535df18942241f06A4Eb9bCb32F45f43C/656 185922 0x91e2655c59fffa13c5caa224e8be673fc89287a9ecd516c0227d8881858fc634
3.902 101.462130006 (-55.67699935339474) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0x4fc2f408d2e887bd8a4b1be216592daff5345b0bcd1747d84992924e9b252803
5.474 466.215746530 (128.50725089015117) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/14431 324129 0x5b677b2f7f017e946fd4bf0863c4456f2bc39022733584b80dce57fffeeebd5e
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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.