u/Fresh-Obligation6053

I've been building a waitlist tracker for luxury watches (unghosted.io) and Tudor is one of the brands I'm least sure about. I have 24 reports so far and most of them are walk-in or under a month. Black Bay 58, Pelagos, even the Black Bay Pro seem to be sitting in cases at a lot of ADs.

But I keep hearing the Black Bay Chrono and certain GMT configs are harder to get in some regions. Is that still true or has availability opened up across the board?

If you bought a Tudor at an AD recently I'd appreciate a quick submission on the site: what you bought, how long you waited, your region, and any purchase history. "Walked in and bought it" is just as useful as "waited 6 months" because it tells people what's actually available.

I've been working on a waitlist tracker for luxury watches (unghosted.io). The problem with buying any allocation-based watch right now is you're making decisions with almost zero information. You don't know if the wait is 3 months or 3 years, whether purchase history matters at your specific boutique, or whether you'd have better luck somewhere else. Most people guess based on a couple Reddit comments or whatever their SA tells them.

I've got 580+ reports across 8 brands but my Vacheron data is the thinnest. The Overseas 4500V in steel is obviously the tough one, but beyond that I don't have a clear picture of what's happening with Traditionnelle, Fiftysix, Historiques, the 222, etc.

If you bought a Vacheron at a boutique or AD, I'd appreciate a quick submission: what you bought, how long you waited, your region, and any purchase history. Even "walked in and bought it same day" is useful because that tells people which models don't have a wait.

www.unghosted.io

Everything is anonymous, no boutique names or personal info collected.

I've been building a community waitlist tracker for luxury watches at unghosted.io. After covering Rolex, Patek, Tudor, Vacheron, and FP Journe, I expanded the AP section with the full Royal Oak and Code 11.59 catalog.

The problem is simple: AP boutiques don't give timelines. You express interest, your SA submits your profile, and you wait. Maybe months, maybe years, maybe never. The only way to calibrate expectations is to hear from people who actually went through the process.

That's what this does. Buyers submit what they bought, how long they waited, what region they're in, and whether they had purchase history. Anyone can search and filter the data.

What the data is showing so far:

• Steel Royal Oak as a first piece is almost impossible. Nearly every report confirms this.
• One prior AP purchase (especially an Offshore or Code 11.59) opens the door. A buyer in Hong Kong got a blue ROC 9 months after purchasing a Taupe ROO.
• Code 11.59 and certain Offshore configs have walk-in or short-wait availability.
• Region matters. Hong Kong and some European boutiques appear more flexible than major US cities for first-time buyers.

What's in the tracker for AP:

• Royal Oak (62 references)
• Code 11.59 (29 references)
• Royal Oak Offshore
• Full model catalog with ref numbers

I need more data points from this community. If you bought an AP at a boutique, I'd appreciate a quick submission: what you bought, how long you waited, your region, and whether you had prior purchases.

Link: www.unghosted.io

The data is anonymous, no AD names, no personal info. Just the facts that help the next person set realistic expectations.

Been tracking AD wait times across a few brands and wanted to share whats coming in for Patek. Built a tool called unghosted.io where collectors submit their wait times anonymously.

Still early on the Patek data but some interesting patterns so far. Nautilus waits are all over the place depending on purchase history. Aquanaut seems slightly more accessible but still measured in years for most people. Calatrava and Complications are actually trading below retail on the grey market which is interesting.

The purchase history correlation is pretty clear in the scatter plots. Not exactly shocking but seeing it mapped out with real data hits different than just hearing "build a relationship with your AD."

If youve bought from an AD recently would love for you to add your data. Everything is anonymous, takes about 30 seconds. The more Patek data we get the more useful the comparisons become.

Curious what wait times you guys are seeing out there.

So I'm a collector who's been on a couple waitlists for a while now. I work with multiple ADs in my area but I cant justify paying grey market prices for something I should be able to get at retail if I'm patient enough. The problem is theres literally no way to know if your wait is normal or if you're getting strung along.

So I built a tracker for it. Its called unghosted.io - people submit their wait times anonymously and the data gets aggregated so you can actually see whats going on.

Got about 540 reports so far from 62 countries. Covers Rolex, Patek, AP, Tudor, Vacheron, FP Journe, Rexhep Rexhepi, and A. Lange & Sohne.

Some interesting stuff from the data: Datejusts and Explorers are averaging under 3 months. Submariners have gotten way easier to get than people think. Steel Daytonas are still 2-5+ years which suprises nobody. AP Royal Oaks seem to be running 1-3 months if you have at least one prior purchase. And FP Journe is basically impossible for new clients on anything desirable.

You can filter by brand, theres scatter plots showing wait time vs purchase history, and a calculator to estimate your own wait. Free, no account or anything.

Would love to hear what other data points would make this actually useful for you guys. Still early so open to feedback.

Started with no moderation at all. People submit data through a form, it goes straight to the database, shows up on the site. Worked fine until I realized anyone could spam garbage entries. Retrofitted a status column (pending/published/flagged) after launch which was annoying because I had to backfill 400+ existing rows.

The outlier detection was a fun one. API route queries existing published entries for the same category, calculates the most common value, and if the new submission is way outside that range it gets auto-flagged instead of going to pending. Simple but catches the obvious junk.

Duplicate check is just a query before insert: same category + same key fields + submitted within last 24 hours = rejected. Nothing fancy.

Biggest win was removing auth entirely. No login, no email required, just a form with dropdowns and a submit button. Conversion went way up. I use RLS to let anyone insert but only show published entries on reads.

One painful lesson: Plotly.js full bundle is enormous. Mobile Safari was throwing script errors on about 10% of loads. Switched to plotly.js-basic-dist-min and wrapped the whole thing in an error boundary. Should have done that from the start.

Anyone else doing community data collection on Supabase? Curious how others handle moderation at scale.

8 months ago someone analyzed 600+ comments from the AD Wait Time Megathread and posted the results here. The mod refused to pin it because the data was incomplete, had survivorship bias, didn't track purchase history, and had no regional filtering. Every comment pointed out the same problems.

Those are real problems. I built something that fixes them.

unghosted.io is a community waitlist tracker where buyers submit structured data: what watch, how long they waited, what region, and whether they had purchase history with the AD. Everyone can search and filter the results.

328 reports so far across 8 brands. Some things the data already shows:

• GMT market is shifting fast since the Pepsi got discontinued. Batman and Sprite are absorbing that demand.
• Regional variation is massive. Same watch, same purchase history, completely different outcomes depending on city.

I also wrote a full breakdown of current wait times by model: unghosted.io/rolex-waitlist-times

The whole point is that this gets more useful as more people submit. If you got the call (or didn't), 30 seconds of your time helps the next person know what to expect.

Not selling anything. No ads. Just data.