The Most Popular .ETH Names in the ENS Short Name Auction
It’s been three weeks since we announced short name auctions in collaboration with OpenSea.
As you can see on the website, 5 and 6 character .ETH names are ending very soon followed by 3 and 4 character names. Many people are more likely waiting for the final moment (note the end date for an auction varies depending on the name you are bidding on as end dates are staggered to avoid Ethereum network surge).
Having said that, there are over 3000 names with a bid, and I will spotlight some of the trends in the following order:
- Top 500 websites
- Personal names (male/female/last)
- Total Popularity
- Non alphabetical names
Top 500 websites
Before the short name auction, we had a 6 week period in which any owner of a DNS name could claim the equivalent .ETH name (eg: https://aragon.org was able to reserve aragon.eth). We had 344 submissions under short name reservations of which 194 were approved. All remaining 3–6 character .ETH names are available for auction (after which they will become available for instant registration).
To see how many popular names are currently under auction, I downloaded popular websites from https://moz.com/top500 , removed some duplicated domains (eg: google had 45 of their subdomains such as docs.google.com , play.google.com on the list) and filtered for 3–6 character names.
The top 10 popular domains by web traffic are as follows:
Out of 173 names short names, we identified the following 44 are being bid on in our auction (sorted by the number of bids and highest bids)
╔══════╦═════════╦════════╦═════╦═════════╦══════╦════════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬════════╬═════╬═════════╬══════╬════════╣
║ 8 ║ 1.05 ║ amazon ║ ║ 3.0 ║ 6 ║ google ║
║ 7 ║ 0.05 ║ medium ║ ║ 1.05 ║ 8 ║ amazon ║
║ 7 ║ 0.04 ║ cisco ║ ║ 0.8 ║ 1 ║ mail ║
║ 6 ║ 3.0 ║ google ║ ║ 0.5 ║ 4 ║ disney ║
║ 6 ║ 0.25 ║ yahoo ║ ║ 0.25 ║ 6 ║ yahoo ║
║ 6 ║ 0.05 ║ orange ║ ║ 0.11 ║ 5 ║ skype ║
║ 6 ║ 0.04 ║ office ║ ║ 0.11 ║ 4 ║ nvidia ║
║ 5 ║ 0.11 ║ skype ║ ║ 0.1 ║ 4 ║ airbnb ║
║ 5 ║ 0.05 ║ intel ║ ║ 0.06 ║ 2 ║ github ║
║ 5 ║ 0.04 ║ adobe ║ ║ 0.05 ║ 6 ║ orange ║
║ 5 ║ 0.03 ║ change ║ ║ 0.05 ║ 7 ║ medium ║
║ 4 ║ 0.03 ║ house ║ ║ 0.05 ║ 5 ║ intel ║
║ 4 ║ 0.1 ║ airbnb ║ ║ 0.05 ║ 2 ║ nokia ║
║ 4 ║ 0.03 ║ target ║ ║ 0.04 ║ 6 ║ office ║
║ 4 ║ 0.04 ║ oracle ║ ║ 0.04 ║ 2 ║ gmail ║
║ 4 ║ 0.5 ║ disney ║ ║ 0.04 ║ 4 ║ oracle ║
║ 4 ║ 0.11 ║ nvidia ║ ║ 0.04 ║ 5 ║ adobe ║
║ 3 ║ 0.03 ║ metro ║ ║ 0.04 ║ 7 ║ cisco ║
║ 3 ║ 0.03 ║ forbes ║ ║ 0.03 ║ 1 ║ mirror ║
║ 3 ║ 0.03 ║ people ║ ║ 0.03 ║ 5 ║ change ║
║ 3 ║ 0.03 ║ twitch ║ ║ 0.03 ║ 4 ║ house ║
║ 3 ║ 0.03 ║ terra ║ ║ 0.03 ║ 4 ║ target ║
║ 3 ║ 0.03 ║ unesco ║ ║ 0.03 ║ 3 ║ metro ║
║ 3 ║ 0.03 ║ alexa ║ ║ 0.03 ║ 3 ║ forbes ║
║ 2 ║ 0.05 ║ nokia ║ ║ 0.03 ║ 3 ║ people ║
║ 2 ║ 0.04 ║ gmail ║ ║ 0.03 ║ 3 ║ twitch ║
║ 2 ║ 0.03 ║ naver ║ ║ 0.03 ║ 3 ║ terra ║
║ 2 ║ 0.03 ║ europa ║ ║ 0.03 ║ 3 ║ unesco ║
║ 2 ║ 0.03 ║ sakura ║ ║ 0.03 ║ 3 ║ alexa ║
║ 2 ║ 0.06 ║ github ║ ║ 0.03 ║ 2 ║ naver ║
║ 1 ║ 0.8 ║ mail ║ ║ 0.03 ║ 2 ║ europa ║
║ 1 ║ 0.03 ║ vimeo ║ ║ 0.03 ║ 2 ║ sakura ║
║ 1 ║ 0.03 ║ webmd ║ ║ 0.03 ║ 1 ║ vimeo ║
║ 1 ║ 0.03 ║ flickr ║ ║ 0.03 ║ 1 ║ webmd ║
║ 1 ║ 0.03 ║ mirror ║ ║ 0.03 ║ 1 ║ flickr ║
╚══════╩═════════╩════════╩═════╩═════════╩══════╩════════╝2. Personal names
Personal names were a contentious point in the reservation process, and we decided to generally not allow them to be reserved, hence they are open for the auction.
I categorized names into three categories: a) last name, b) male name, c) female names, and extracted names from the following sites.
- https://data.world/uscensusbureau/frequently-occurring-surnames-from-the-census-2000(65,000 names)
- https://github.com/hadley/data-baby-names (258,000 names)
These include pretty much all the kinds of names so it matches up lots of generic names such as “China” (popular names back in the 1990s) and even “Ether” (popular names back in 1880–1910). I further filtered down to 1000 names of which 600~700 names matched. I first placed popular names in general (which have the number of bids next to them), followed by top 10 in number of bids and highest bids.
a: Last Name (149 matches out of 625)
By popularity in general
ENS auction
╔══════╦═════════╦════════╦═════╦═════════╦══════╦════════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬════════╬═════╬═════════╬══════╬════════╣
║ 9 ║ 1.6 ║ love ║ ║ 2.0 ║ 3 ║ daniel ║
║ 8 ║ 0.08 ║ joseph ║ ║ 1.6 ║ 9 ║ love ║
║ 7 ║ 0.03 ║ white ║ ║ 0.8 ║ 1 ║ bell ║
║ 7 ║ 0.11 ║ james ║ ║ 0.8 ║ 1 ║ cain ║
║ 7 ║ 0.04 ║ black ║ ║ 0.8 ║ 1 ║ rich ║
║ 6 ║ 0.03 ║ chang ║ ║ 0.8 ║ 1 ║ dean ║
║ 6 ║ 0.05 ║ jones ║ ║ 0.8 ║ 1 ║ owen ║
║ 6 ║ 0.06 ║ gamble ║ ║ 0.3 ║ 3 ║ chase ║
║ 6 ║ 0.03 ║ green ║ ║ 0.3 ║ 1 ║ church ║
║ 6 ║ 0.04 ║ clark ║ ║ 0.25 ║ 1 ║ smith ║
╚══════╩═════════╩════════╩═════╩═════════╩══════╩════════╝b: Male first name (189 matches out of 737)
By popularity in general
ENS auction
╔══════╦═════════╦════════╦═════╦═════════╦══════╦════════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬════════╬═════╬═════════╬══════╬════════╣
║ 9 ║ 0.23 ║ andrew ║ ║ 3.2 ║ 1 ║ tim ║
║ 8 ║ 0.08 ║ joseph ║ ║ 2.0 ║ 3 ║ daniel ║
║ 8 ║ 0.14 ║ israel ║ ║ 1.1 ║ 6 ║ peter ║
║ 8 ║ 0.04 ║ alpha ║ ║ 1.01 ║ 1 ║ phil ║
║ 7 ║ 0.11 ║ james ║ ║ 1.0 ║ 5 ║ andres ║
║ 7 ║ 0.15 ║ robert ║ ║ 1.0 ║ 1 ║ jose ║
║ 6 ║ 0.05 ║ jones ║ ║ 0.81 ║ 1 ║ alex ║
║ 6 ║ 0.03 ║ green ║ ║ 0.8 ║ 1 ║ jeff ║
║ 6 ║ 0.04 ║ clark ║ ║ 0.8 ║ 1 ║ jack ║
║ 6 ║ 0.03 ║ lawyer ║ ║ 0.8 ║ 1 ║ dave ║
╚══════╩═════════╩════════╩═════╩═════════╩══════╩════════╝c: Female first name (111 matches out of 642)
By popularity in general
ENS auction
╔══════╦═════════╦════════╦═════╦═════════╦══════╦════════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬════════╬═════╬═════════╬══════╬════════╣
║ 11 ║ 0.07 ║ chanel ║ ║ 3.2 ║ 1 ║ amy ║
║ 7 ║ 0.05 ║ summer ║ ║ 0.8 ║ 1 ║ angel ║
║ 7 ║ 0.07 ║ london ║ ║ 0.8 ║ 1 ║ sage ║
║ 7 ║ 0.21 ║ paris ║ ║ 0.8 ║ 1 ║ lucy ║
║ 5 ║ 0.06 ║ india ║ ║ 0.6 ║ 4 ║ sofia ║
║ 5 ║ 0.04 ║ grace ║ ║ 0.21 ║ 7 ║ paris ║
║ 5 ║ 0.03 ║ megan ║ ║ 0.15 ║ 4 ║ chloe ║
║ 4 ║ 0.03 ║ parker ║ ║ 0.12 ║ 2 ║ sophie ║
║ 4 ║ 0.06 ║ april ║ ║ 0.1 ║ 1 ║ hadley ║
║ 4 ║ 0.05 ║ maria ║ ║ 0.08 ║ 3 ║ dylan ║
╚══════╩═════════╩════════╩═════╩═════════╩══════╩════════╝It is interesting to see the gender contrast that popular male names more bids than female names (and we don’t know if bidders meant to bid female names for names such as “london”, “paris”, “summer”, and “april.”
For last names, “Kim” (one of the most popular names in Korea) has not been bid on (probably due to the fact that 3 characters are expensive).
Which leads to the third topic…
3. Total Popularity
Total: 3011
╔══════╦═════════╦════════╦═════╦═════════╦══════╦════════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬════════╬═════╬═════════╬══════╬════════╣
║ 18 ║ 0.18 ║ invest ║ ║ 10.0 ║ 6 ║ hawaii ║
║ 15 ║ 1.5 ║ crypto ║ ║ 8.0 ║ 6 ║ suzuki ║
║ 15 ║ 0.12 ║ libra ║ ║ 5.0 ║ 3 ║ osaka ║
║ 14 ║ 1.2 ║ casino ║ ║ 4.0 ║ 2 ║ btc ║
║ 14 ║ 0.21 ║ insure ║ ║ 3.5 ║ 1 ║ nft ║
║ 14 ║ 0.42 ║ coffee ║ ║ 3.3 ║ 1 ║ oro ║
║ 13 ║ 0.5 ║ ether ║ ║ 3.3 ║ 1 ║ tax ║
║ 13 ║ 0.25 ║ hotels ║ ║ 3.3 ║ 1 ║ car ║
║ 12 ║ 0.61 ║ music ║ ║ 3.21 ║ 1 ║ dao ║
║ 12 ║ 0.21 ║ games ║ ║ 3.2 ║ 1 ║ god ║
╚══════╩═════════╩════════╩═════╩═════════╩══════╩════════╝4 chars: 81
╔══════╦═════════╦══════╦═════╦═════════╦══════╦══════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬══════╬═════╬═════════╬══════╬══════╣
║ 9 ║ 1.6 ║ love ║ ║ 2.5 ║ 1 ║ bren ║
║ 9 ║ 1.6 ║ game ║ ║ 1.6 ║ 9 ║ love ║
║ 5 ║ 1.4 ║ hodl ║ ║ 1.6 ║ 9 ║ game ║
║ 5 ║ 1.26 ║ defi ║ ║ 1.5 ║ 4 ║ porn ║
║ 5 ║ 1.01 ║ bank ║ ║ 1.5 ║ 1 ║ ronb ║
║ 4 ║ 1.5 ║ porn ║ ║ 1.4 ║ 5 ║ hodl ║
║ 2 ║ 1.0 ║ lend ║ ║ 1.37 ║ 1 ║ dydx ║
║ 2 ║ 1.0 ║ nfts ║ ║ 1.26 ║ 5 ║ defi ║
║ 2 ║ 1.0 ║ iota ║ ║ 1.01 ║ 1 ║ phil ║
║ 2 ║ 1.0 ║ free ║ ║ 1.01 ║ 5 ║ bank ║
╚══════╩═════════╩══════╩═════╩═════════╩══════╩══════╝3 chars:20
╔══════╦═════════╦══════╦═════╦═════════╦══════╦══════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬══════╬═════╬═════════╬══════╬══════╣
║ 2 ║ 4.0 ║ btc ║ ║ 4.0 ║ 2 ║ btc ║
║ 1 ║ 3.5 ║ nft ║ ║ 3.5 ║ 1 ║ nft ║
║ 1 ║ 3.3 ║ oro ║ ║ 3.3 ║ 1 ║ car ║
║ 1 ║ 3.3 ║ tax ║ ║ 3.3 ║ 1 ║ oro ║
║ 1 ║ 3.3 ║ car ║ ║ 3.3 ║ 1 ║ tax ║
║ 1 ║ 3.21 ║ dao ║ ║ 3.21 ║ 1 ║ dao ║
║ 1 ║ 3.2 ║ tim ║ ║ 3.2 ║ 1 ║ bnb ║
║ 1 ║ 3.2 ║ snx ║ ║ 3.2 ║ 1 ║ usa ║
║ 1 ║ 3.2 ║ dkp ║ ║ 3.2 ║ 1 ║ god ║
║ 1 ║ 3.2 ║ 888 ║ ║ 3.2 ║ 1 ║ eos ║
╚══════╩═════════╩══════╩═════╩═════════╩══════╩══════╝As you can see, most popular names by the number of bids are in 5-6 character range. In fact, there aren’t multiple bids on 3 character names apart from “btc.eth” (other names like eth.eth and ens.eth are reserved).
A bit of warning about the highest bids though. Just because they bid for the price, it does not mean they have a money for that. Opensea shows warning box for such highest bids.
Check out brown.eth , one of the highest bid. The bidder seems bidding 10 ETH into many different names (hence total of 104 eth), though he only has 10ETH in total.
When I initially analysed, I didn’t take these into consideration.
When Nick pointed that out and fixed the script, the total bids went down from 3481 to 3011, almost 400 less entries even though the second query was run almost 12 hours afterward. You can check before/after the modification.
BEFORE
╔══════╦═════════╦════════╦═════╦═════════╦══════╦════════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬════════╬═════╬═════════╬══════╬════════╣
║ 11 ║ 0.07 ║ chanel ║ ║ 7.77 ║ 4 ║ maria ║
║ 7 ║ 1.0 ║ summer ║ ║ 7.0 ║ 7 ║ london ║
║ 7 ║ 1.0 ║ paris ║ ║ 5.0 ║ 4 ║ parker ║
║ 7 ║ 7.0 ║ london ║ ║ 3.2 ║ 1 ║ amy ║
║ 5 ║ 0.04 ║ grace ║ ║ 3.0 ║ 2 ║ naomi ║
║ 4 ║ 0.03 ║ logan ║ ║ 1.2 ║ 3 ║ kenya ║
║ 4 ║ 0.0525 ║ india ║ ║ 1.12 ║ 4 ║ sofia ║
║ 4 ║ 0.03 ║ megan ║ ║ 1.0 ║ 3 ║ harper ║
║ 4 ║ 1.0 ║ hannah ║ ║ 1.0 ║ 7 ║ summer ║
║ 4 ║ 0.037 ║ armani ║ ║ 1.0 ║ 2 ║ aisha ║
╚══════╩═════════╩════════╩═════╩═════════╩══════╩════════╝AFTER
╔══════╦═════════╦════════╦═════╦═════════╦══════╦════════╗
║ bids ║ highest ║ name ║ ___ ║ highest ║ bids ║ name ║
╠══════╬═════════╬════════╬═════╬═════════╬══════╬════════╣
║ 11 ║ 0.07 ║ chanel ║ ║ 3.2 ║ 1 ║ amy ║
║ 7 ║ 0.05 ║ summer ║ ║ 0.8 ║ 1 ║ angel ║
║ 7 ║ 0.07 ║ london ║ ║ 0.8 ║ 1 ║ sage ║
║ 7 ║ 0.21 ║ paris ║ ║ 0.8 ║ 1 ║ lucy ║
║ 5 ║ 0.06 ║ india ║ ║ 0.6 ║ 4 ║ sofia ║
║ 5 ║ 0.04 ║ grace ║ ║ 0.21 ║ 7 ║ paris ║
║ 5 ║ 0.03 ║ megan ║ ║ 0.15 ║ 4 ║ chloe ║
║ 4 ║ 0.03 ║ parker ║ ║ 0.12 ║ 2 ║ sophie ║
║ 4 ║ 0.06 ║ april ║ ║ 0.1 ║ 1 ║ hadley ║
║ 4 ║ 0.05 ║ maria ║ ║ 0.08 ║ 3 ║ dylan ║
╚══════╩═════════╩════════╩═════╩═════════╩══════╩════════╝
There was significant difference on highest bids. For female category, maria.eth went from 7.77 ETH all the way down to 0.05 ETH!
4. Non alphabetical names
I derived all the data from the log of a script Nick Johnson has been developing. While traversing the output of the log (which emits the number of bids per each name), I noticed a few warning messages which indicates non-alphabetical numbers. 中国**银行 are both some sort of bank in China , 早稲田大学 is a university in Tokyo and 以太坊 means Ethereum in Chinese (which I noticed a lot while I was at Devcon2 in Shanghai). I was expecting a lot more emojis, but so far only ⭐⭐⭐⭐⭐ and ❤❤❤❤❤ have bids.
INFO:root:Processing 1 bids on [CJK: 以太坊支付].eth ⚠️...
INFO:root:Processing 1 bids on voil[LATIN: à].eth ⚠️...
INFO:root:Processing 1 bids on cr[LATIN: é]dit.eth ⚠️...
INFO:root:Processing 1 bids on [HEAVY: ❤❤❤❤❤].eth ⚠️...
INFO:root:Processing 1 bids on [CJK: 早稲田大学].eth ⚠️...
INFO:root:Processing 1 bids on c[LATIN: é]line.eth ⚠️...
INFO:root:Processing 1 bids on chlo[LATIN: é].eth ⚠️...
INFO:root:Processing 1 bids on [WHITE: ⭐⭐⭐⭐⭐].eth ⚠️...
INFO:root:Processing 1 bids on [LATIN: é]tudes.eth ⚠️...
INFO:root:Processing 1 bids on gar[LATIN: ç]on.eth ⚠️...
INFO:root:Processing 1 bids on herm[LATIN: è]s.eth ⚠️...
INFO:root:Processing 1 bids on caf[LATIN: é]s.eth ⚠️...
INFO:root:Processing 1 bids on h[LATIN: ô]tel.eth ⚠️...
INFO:root:Processing 1 bids on [CJK: 中国人民银行].eth ⚠️...
INFO:root:Processing 1 bids on [CJK: 中国建设银行].eth ⚠️...
INFO:root:Processing 1 bids on [CJK: 中国工商银行].eth ⚠️...
INFO:root:Processing 1 bids on [CJK: 中国交通银行].eth ⚠️...5. What else can you bid on?
So far I went through list of names where there are more likely one or more legitimate owners (whether bidders are the legitimate owners or not is a different story).
However, there will be lots of generic names like countries, cities, animals, flowers, foods, etc.
RicMoo , one of winners at ETHNewYork (which I wrote about it here), is a massive ENS fan boy and he told me that he owns bunch so that he can come up with new services with new names. takoyaki.eth (Octopus balls which is one of famous Osaka streetfood) is in fact a great name!
I am sure that Richard is shoehorning his snipping tool to capture his favorite short names.
To compete against him, I will give you a bit of tool. The script I used to create this blogpost and the full bidding dataset!
What kind of creative Web3 dapps and services will you create with cool short .ETH names?
