Nobody knows, because nobody's published it. This is where we're building the answer — the methodology, the data access that makes it possible, and where to check back once results exist.
This page doesn't have the numbers yet. We're telling you that up front rather than padding it with placeholder statistics, because the honest version of this page is more useful than a finished-looking one built on guesses.
What we can tell you is what we're measuring, why we're one of the only vendors in this market positioned to measure it, and the methodology we're committing to before a single result comes in. That last part matters. Writing the counting rules down first is what keeps a benchmark from quietly bending toward whatever answer looks best once you're staring at it.
Everyone in this category has an opinion on how often a seller should reprice. Almost nobody has data on how often sellers actually do. Our own guide on how often you should reprice your TCGplayer cards walks through how to reason your way to your own cadence, and it's a reasonable answer for an individual store. But "reasonable" isn't the same as "here's what stores your size actually do, and here's what happens to their sell-through and their floor usage when they do it." That second answer requires real behavioral data across real stores, and as far as we've found in competitive research, nobody selling repricing tools today (not SortSwift, not TCG Sync, not MassPrice, not Storepass) has published anything like it.
Hoard operates a public TCG price API and MCP server covering more than 20 games, which puts us in the position of seeing market-side pricing data most repricing vendors never touch. On top of that, we have our own sellers' inventory, pricing, order, and refund history — anonymised, but real, across live stores actually running repricing rules day to day. That combination, market-side coverage plus seller-side behavior, is genuinely unusual for a vendor in this specific market. It's what makes a benchmark like this possible for us and not for a tool that only ever sees the price it wrote.
The study is not complete. This is the methodology as planned, not a set of findings:
All three are built from anonymised first-sync snapshots (the state of a store's inventory the moment it connects, before any Hoard rule has touched it) plus ongoing behavior across live stores after that point.
We're publishing the counting rules before we look at the numbers. If the result says something unflattering about repricing cadence, or about Hoard's own assumptions, we're publishing that too.
Most of this content cluster targets a specific search query. This page doesn't, really — it's built to be the thing other pages, and other people writing about repricing, can cite instead of guessing. Right now, a claim like "most sellers reprice weekly" or "floor usage is rare" has no source behind it anywhere in this market. Once this study runs, it will. Until then, we'd rather point you to the TCGplayer repricing guide for the decisions you can make today, and come back here when there's something real to report.
We'll update this page directly once the first results are in, rather than publish a separate announcement and leave this one stale. If you're citing repricing behavior for anything public — a talk, an article, a comparison — this is the page to watch, not the one to cite yet.
One adjacent, narrower measurement is already live: not seller behavior, but market behavior — how often prices themselves move enough to matter. That's a different question with a different answer already published in catch a price spike before it corrects. It's a preview of the discipline this page will hold to once the seller-behavior numbers are ready: counting rules first, results second, published either way.
A study measuring how often TCGplayer sellers actually reprice, built from anonymised data across live Hoard stores rather than opinion. Nobody in this market has published behavioral data like it before.
Not yet. This page publishes the methodology first — what's being measured and how — before any numbers, and we'll publish the result directly to this page even if it says something unflattering about repricing cadence or about Hoard's own assumptions.
Three things: reprice frequency by store size and tier, the share of listings sitting at a price floor at any given time, and average time-to-react when a card's market price jumps or drops meaningfully.
Hoard operates a public TCG price API and MCP server covering more than 20 games, on top of its own sellers' inventory, pricing, order, and refund history. That combination — market-side coverage plus seller-side behavior — is unusual for a vendor in this market, which is what makes the study possible.
Connect your TCGplayer store and see how your own repricing cadence and floor usage compare to what you'd expect — no benchmark required.
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