Row count is not market sample size. The cited board covers several sports and plenty of games, but each spread used here comes from one quoted book. That gives us coverage of events. It does not give us independent observations of market price.
The sample-size guide asks two separate questions: how many independent books posted, and how far apart their quotations landed. On this board the first answer blocks the second. One book cannot disagree with itself.
The baseball rows fix a label, not the sample
Houston at the Yankees showed the Yankees -1.5 . That row belongs to baseball, not the NFL, and the game is Astros at Yankees—not an unnamed Houston-at-New-York matchup.
Kansas City at Toronto showed Toronto -1.5 . Baltimore at St. Louis showed St. Louis -1.5 .
Those three rows are legitimate observations from the cited table. They still do not create a cross-book sample. More games can expand coverage while leaving the independent-book count unchanged.
The NFL board has the same limit
Pittsburgh at Buffalo showed Buffalo -1.5 . New England at Cleveland showed Cleveland +2.5 . San Francisco at Las Vegas showed Las Vegas -2.5 .
Washington at Baltimore showed Baltimore -3.5 in this cited snapshot . The Giants at the Jets showed the Jets +3 .
A screen full of NFL games can feel like a mature market. The sample question is narrower: how many independent books stand behind each displayed line at the captured moment? The answer in this module is one.
Book count and dispersion are not interchangeable
Book count measures independent quotations in the allowed feed. Dispersion measures the range among those quotations. Both can be useful, but only in that order.
With one book, a range calculation returns zero because the maximum and minimum are the same observation. That arithmetic is correct. The betting interpretation is not “perfect agreement.” It is “no comparison set.”
The interface should make that state explicit. Showing a bright zero without book count invites the reader to infer consensus. Showing “one book; dispersion unavailable” tells the truth and supplies the cure.
This is also why repeated captures of one game need care. A time series can show movement at one source. It cannot substitute for simultaneous independent prices unless the analysis is explicitly about that source’s path.
The college pairs are time evidence, not breadth
New Mexico State at Florida State appears at Florida State -31 and -31.5 . San José State at USC appears at USC -38 and -38.5 .
Sacramento State at Eastern Michigan appears at Eastern Michigan -9.5 and -10 . Akron at Wake Forest showed Wake Forest -23.5 .
Each pair can support a statement that the captured quotation changed. It cannot support a statement that two books disagreed unless the provenance identifies two independent books at the same decision window.
The wide values do not change that standard. A dramatic point spread attracts attention, but sample size comes from independent observations, not visual magnitude.
What the guide can still conclude
It can conclude that the event coverage recovered. It can conclude that the cited rows carry single-book quotations. It can conclude that cross-book dispersion is unavailable for those rows.
It cannot conclude that the market agreed, that the market was stable, or that no price discovery occurred elsewhere. Those claims require coverage the sample does not have.
The earlier rebuild remains useful as a description of the book-count rule. The closing-line value guide remains useful once a frozen entry and later market price exist. Neither authorizes a placeholder when the comparison price is missing.
The Receipts Drawer
The market-sample result for these rows is NO MARKET SAMPLE. The cure is concrete: capture another independent allowed book on the same event and decision window. Then calculate dispersion and preserve both source timestamps.
Until then, the odds page should show the thin feed, and the track record should grade only frozen, scoreable calls. A single resting spread is data. It is not a crowd.
