Is 2.5% best? Is 10% too much?

Interesting discussions on Twitter made me look at the data.

This Tweet prompted me to dig deeper.

https://twitter.com/CapetainTrippy/status/1499973462907142144?s=20&t=UXe7tuaE6p1pCL5MadSzdQ

Then I researched other discussions and found interesting insights.

If the fee is too high, is that bad for the market and slows down trading? Or does it give more funds back to the project to re-invest into more growth? From a business and entrepreneurial standpoint, higher % fee makes sense but only if you know what to do with it and it’s a legit project.

If the fee is too low, it might be a good signal for the community as not a cash-grab but the team might be hurting themselves if their funds are not increasing. Low fee projects would need some other advantage like innovation, momentum, being early, connections, etc, to set a low % and still succeed.

Those are just a few discussion points, there are many more. By the way, any project can have success if they set it at 0% or 25%, so just use this data for fun if you are creating or supporting a project to have a higher chance and “predict” what their volume or floor might do.

Ultimately I pulled the data and very quickly did some sloppy analyses to see if there are general trends.

950+ collections were included. I’m lazy so the main metrics I looked at are a bit biased since they represent a snapshot on March 5th, 2022. Whichever floor prices, age of projects, volume last 30 days (in ETH), and the current royalties % set on this day (creators may have changed this setting), is what the pattern supports. I didn’t exclude uncommon projects (1 item in collection, etc.)

Also a bias to mention is that I didn’t run proper statistical analyses (even though that’s why I did daily years ago for my Ph.D.). In otherwords, ignore 100% of everything in this article haha! This is simply to spot major patterns or not, and simplify showing it visually. I split up the data into main categories of common % fee’s that are set for secondary sales.

Summary: The middle category seems to be the best. That’s where the fee is between 5.01% and 7.49%. Basically everything in between, but excluding 5% and 7.5%.

That doesn’t mean you should set your % in some weird number like 5.55% fees. Although some of you weirdos probably will :) The 2nd and 3rd category are best after that, which do include 5% and 7.5%.

Overall I would say the sweet spot does seem to be in the area of 5%-7.5%. There are many exceptions, off course, with some doing extremely well at 2.5% as BAYC did, and setting at 10% is on the higher end with some tradeoffs for project that go that route.

If the project needs extra funds to re-invest into the team, community, marketing, dev, the project itself, etc, higher percentage makes sense. If there is more experience or innovative advantage, a lower % might make more sense.

Credit to interesting Twitter posts or comments that made me think how to approach this analysis and article, even simple tweets gave me ideas to test in the data… (Thanks NFT peeps BobbyBananasNFT, DiscoverXnft, Rroiie, BoredApe1000, Gee__Gazza, NathanHeadPhoto, and HunterOrrell for creating old and recent discussions on the topic). I’m sure they know more about this if the data makes sense or not. There is more data/table I can share if anyone asks, but keeping this a simple one-image post.

Again, lots of exceptions to the pattern here but it’s cool anyway.

Thanks for reading!

Yev

@Doctoryev

The idea is to normalize it by other factors (volume, supply to owners %, actual floor price, etc) and have a relative comparison to other projects.

The floor price in the adjusted metric is essentially one that is undervalued, simply based on math relative to other projects that have similar other types of factors. This does not account for social metrics or actual quality of the project, but the other factors may be an indirect proxy for that.

SHORT SUMMARY without watching video: Look at the live table I put on Dune analytics site. Sort by the new last column (FloorPredictorMetric). The higher the better. The first several pages will be blank until you get to recent projects. Look for projects that are within your acceptable Floor range and have decent size volume.

Here’s a video explaining how I’m looking at it. Try it and see if it helps you narrow down to some projects. Then hopefully within a few days (or weeks?) you’ll see it spike relative to other projects. Or atleast not drop as much compared to other ones.

Watch video on the LOOM screen recording here for slightly higher quality.

Let me know on Twitter if you find good projects this way. Also if there are factors you think are vital to include in the analyses or another strategy to filter out collections.

Use the live table with data from the video directly on Dune analytics here.

Thanks,

Yev Marusenko

https://twitter.com/Doctoryev

I’ll keep digging for more.

In summary:

1. Having a large social media following (includes Discord, Twitter, and a few others) does NOT guarantee you will have a higher floor price. We see that because many projects with huge followings have tiny floor prices.

2. Having a large social media following DOES increase the chance of having a higher floor price. We see that because in general as the number of followers increase, the range and maximum increases too. Makes sense. Good to keep in mind for project leaders and if you are following particular projects.

Next, I investigated if any factors affect the pattern above. One of the things that stood out is that the pattern of positive correlation between social vs. floor price is weaker (or non-existent) at high Supply % (that’s number of Owners divided by number of Items). I wonder if that means that at 70% supply is when the project starts getting saturated.

Hopefully that’s interesting.

I also found some thresholds (not shown above) highlighting if Twitter or Discord is more important. I’ll discuss that later, and when projects skyrocket in certain conditions when the Twitter social size reaches specific tipping points.

Let me know on Twitter if you’re observing these patterns at the individual project level and if these broad scale insights are relevant.

Thanks!

Yev

Creating this post entry as a placeholder but will be adding the patterns very soon. Hoping to share helpful insights.

Some of the data will go here on my profile in Dune Analytics.

Some of the discussions will go here on my Twitter.

Stay tuned.

Thanks,

Yev

IDEAS HOW TO LEVERAGE THESE TOOLS

People buy NFTs for many different reasons.

These tools will help you find insights to be early in unique projects and/or to catch trends. Depending on how you do research, your decision process will improve and accelerate to get the NFT as early as possible.

Aside from having interesting data, pay attention to different thresholds and tipping points which will allow you to discover NFT collections even before they start spiking in volume or floor price. I’ll discuss pattern recognition topics in future content. Analytics here and @DoctorYev.

30+ tools is a lot. Set your expectations how you or your team should use them, and why. I suggest mixing it up with both of the following strategies:

1. Learn 2-3 tools really well. This will help you spot patterns quicker rather than in hindsight and will improve your intuition by mastering just a handful of tools. Try to get atleast one premium/paid tool (any of these should pay for themselves to either help with your investment or easily save you some time).

2. Use 10+ of these tools as a generalist. They all innovate in different ways and you’ll always find unique metrics and functionalities released. Also, identical features but a simple UX/UI difference can make a big impact, for instance, ability to filter or select different time ranges may help spot patterns that you normally would not.

For example, I just used a bunch of these tools (during analytical experimentation) to get CoolMonkes NFT for 0.15 ETH and now the floor is around 0.8 ETH.

On another note, interact with the communities and/or socials for these tools. The users in some of them are very friendly and share best ways to fully leverage the tools.

I have more experience in DEX/CEX/DeFi/coins than NFTs but loving the creativity and impending utility of NFTs, hoping to integrate across the industries through analytics and technology. This was my attempt to make a resource list focused specifically on NFTs and researching of NFTs (patterns, trading, alerts, analytics, data). There’s also an entire world of automation but that’s a topic for another day.

Dig in below.

HERE’S THE NFT TOOL LIST

Scroll down to see all tools. In addition, the Google Sheet contains 10+ additional metrics and info for each tool.

The pretty graphic earlier is just to catch attention but the actual full list is inside the sheet here.

https://docs.google.com/spreadsheets/d/1bQejMwqpYmMFaiwNXo8ObBlpdglk2usS27yUEx1qIsI/edit?usp=sharing

The list is made by Yev Marusenko, Ph.D. | yevm.eth https://twitter.com/Doctoryev

To suggest other tools or edits DM/tweet to Yev.

More analyses on https://dune.xyz/yev/NFT-analytics

Follow Yev on twitter for unique analyses, ways how to improve NFT tools, and beta access to new features in some of these tools.

[Updated Jan 27, 2022.]

NAME | WEBSITE | Social following

*** = favorites

traitsniper | https://www.traitsniper.com/ | 16400 ***

chilly.tools | https://chilly.tools/ | 405

rarity sniper | https://raritysniper.com/ | 517200

txstreet | https://txstreet.com/ | 4450

flipsfinance | https://www.flips.finance/ | 3100 ***

Nansen | https://www.nansen.ai/ | 123600 ***

LunarCrush | https://lunarcrush.com/ | 310800

Santiment | https://santiment.net/ | 129700

Whalepeek | https://whalepeek.com/ | 10100

NFTGO | https://nftgo.io/ | 31900 ***

IcyTools | https://icy.tools/ | 36400

DappRadar | https://dappradar.com/ | 171800

NFT-stats.com | https://www.nft-stats.com/ | 7300

nonfungible.com | https://nonfungible.com/ | 57700

cryptoslam | https://cryptoslam.io/ | 24800

rarity.tools | https://rarity.tools/ | 82400

nftbank.ai | https://nftbank.ai/ | 10500

moby.gg | https://moby.gg/ | 7900

WGMI.io | https://wgmi.io/ | 12700 ***

luckytrader | https://luckytrader.com/ | 11900 ***

metarankings | https://upcomingnft.live/ | 550

ranknft.io | https://ranknft.io/ | 760

rarityranks.io | https://rarityranks.io/ | 5800

nftscoring | https://nftscoring.com/ | 4300 ***

seafloor | https://seafloor.io/ | 250

nftinit | https://nftinit.com/ | 1840

nfthelper | https://georgemike.com/ | 455

momentranks | https://momentranks.com/ | 31100

supersea | https://nonfungible.tools/supersea | 1100

niftyriver | https://www.niftyriver.io/ | 1560

nftexp.io | https://nftexp.io/ | 2000

rarity.guide | https://rarity.guide/ | 5000

exodia | https://exodia.io/ | 4700

ninjalerts | https://www.ninjalerts.com | 27,000

raritysniffer | https://raritysniffer.com/ | 23900 ***

HypeReveal | https://hypereveal.xyz | 6700 ***

There you have it. The 35+ tools are categorized in more detail in the Google Sheet here with additional helpful info (Twitter links, if it’s for Beginners or Advanced, etc.)

The favorites are designated with a *** above.

Some people find out about these tools and try to keep them secret to keep an advantage. I prefer collaboration and open-sourced decentralization, so please share away. Tag any important tools I missed or cool projects that you’ve found via these tools.

Thanks,

Yev

[Updated Mar 27, 2022.]