Reading the Numbers
Why Projections Have Ranges
5 min read
TL;DR
A projection is a distribution reported as a number. The single figure is the middle of a range, and for most players that range is wide enough that the point estimate is the least interesting part of it.
The number is a summary
When a projection says a running back will score 210 fantasy points, it does not mean 210. It means something closer to: across the plausible versions of this season, the middle outcome is around 210, and the realistic band runs from perhaps 140 to 290.
Reporting only the middle throws away the part that matters most for any actual decision — how wide the band is, and whether it is lopsided.
Floor, ceiling, and why they differ by player
Two players with identical projections can have completely different shapes:
- A volume-dependent veteran on a clear depth chart has a narrow band. He will get his touches; the question is only efficiency.
- A role-dependent young player has a wide, lopsided band. If he wins the job the ceiling is enormous; if he doesn't, the floor is near zero.
The projection cannot tell these apart. The range can.
What our numbers actually are
Worth being precise about, because the word "projection" gets used loosely across the industry.
The figures in our NFL Draft Kit are not the output of a projection model. They are last season's per-game PPR production, regressed toward the positional mean according to how many games the player actually appeared in, with floor and ceiling derived from that player's own week-to-week variance. Rookies have no prior season, so they are explicitly null rather than guessed.
That is a useful baseline and we think an honest one, but it is a description of what already happened, adjusted for sample size — not a forecast of what will happen. Anywhere you see those numbers on this site, that is what they are.
Keep these
- A projection is the middle of a range, not a prediction of a number
- Identical projections can hide very different floors and ceilings
- Our Draft Kit figures are regressed prior-season production, not model forecasts
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How Scoring Format Changes Player Value
