× 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 #664 - 2018-09-24 15:22:34 to 15:23:22; Start Time 1537802554.5975604

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 5.011298744 () 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11265 800000 0x8934ad1837ef64174a4679d7f02aa3298234d43bcbc3eb02ecbd770c44af28c1
0.224 250.000000000 (192.13948751497364) 0x25fd66B11f61C78dc9f58100a4a15684b1080617/13 21000 0x29ecccd684e75c5ad6c3980254920e629f622d6ff2bfbbfe748aea74aa3b34b0
1.990 6.900000001 (-189.25652004519557) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/6043 22000 0x80630cfa8d38b42974fad0b84b2bf94e457dd3c9f4fcfc558a720b57262c8266
2.996 11.067215738 (46.38688372795077) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/17095 22000 0xb17f2532efbed6a39be1feba5a3e57e22abc93488be667eedb3f9ccf62ac9039
3.786 31.938755468 (97.063450235897) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11264 800000 0xa3fb999409f2edeabbcae5bb43ccca242df3dc466af15c9ecb1f5092902e8679
3.850 31.938755468 (0.0) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11265 800000 0xf1fae6a78e482d87b295b2477306c644cb59ef4b900eb1dc80013918f0486bca
3.856 31.736704296 (-0.6346280741398997) 0x3F2a954e94f4FC65c9f71c049aA5d63eBb7b19b7/6522 800000 0x9b7a213390ce4b754514fa378531945ffd35cc3f2ec11197a835c76f1c56ca21
3.901 6.000000000 (-136.40144138781102) 0x3F2a954e94f4FC65c9f71c049aA5d63eBb7b19b7/6523 800000 0x25136a0218b2f1230ca17ee5199d23e0dadc6403f675832333df286b843c84cc
3.930 6.000000000 (0.0) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11266 800000 0x72af5e2bfac092888191b83a959b5c1cd0c3c5025b29850c80125a96ae101724
47.946 12.292232016 (68.79676586756891) 0x3F2a954e94f4FC65c9f71c049aA5d63eBb7b19b7/6523 800000 0x37bf928dcbd309e76c85cd9297e3ebdf94ca09602da69294204c6eac101fab96
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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.