Contact

Email

Write to nolanmcneil@gmail.com. That address reaches the person who runs ShirtMath directly. Expect a reply within two business days; if a question needs a measurement checked against a source, it may take longer and you will be told so rather than left waiting.

What to include, by type of message

A measurement looks wrong

This is the most useful message anyone can send. Include the brand and style, the size, which point of measure looks wrong (chest, length, sleeve), and the number you measured. If the garment is in your hands, say whether you measured it flat with the seams smoothed — that is the standard the whole database uses, and it is described in full on the methodology page. Corrections are checked against the source and the record is updated or removed.

A brand or retailer enquiry

If you publish garment measurements and want them matched properly, or you want to talk about the fit API, say which catalogue platform you run and roughly how many products you carry. The ShirtMath for Brands page describes what the product does today; it is pre-launch and has no customers yet.

Press and research

The published-specification data behind the size charts can be quoted, and the provenance of every number is stated on the page it appears on. If you need the underlying table or want to confirm a figure before publishing, ask — that is a request that gets answered quickly.

Privacy, data and deletion

Requests to export or delete an account's data are handled from the account settings inside the app, and the privacy policy describes exactly what each does. If something there does not work, email is the fallback and the request will be actioned manually.

What ShirtMath is not

ShirtMath does not sell garments, take orders, process payments for garments, or handle returns. Every purchase happens on the retailer's own site under that retailer's terms, so an order problem has to go to the retailer who took the money. If a ShirtMath match sent you to the wrong product, that is worth reporting here, because it means something in the data is wrong.