Projected Points vs. Draft Cost: Efficiency Analysis
Efficiency analysis in fantasy drafts compares what a player is expected to produce — measured in projected points — against what drafters actually pay to acquire them, measured in draft position or auction dollars. The gap between those two numbers is where value is won or lost. This page explains how the analysis is structured, where it breaks down, and how it shapes real draft decisions across formats.
Definition and scope
Draft cost is the price of acquisition. In a snake draft, that cost is expressed as average draft position (ADP) — the average pick at which a player is selected across a population of real drafts. In an auction, cost is literal: a dollar amount bid against a fixed budget, typically $200 in standard leagues. Projected points are the model output — an estimate of how many fantasy points a player will score across a season, based on inputs like target share, snap rate, historical scoring, and matchup data.
Efficiency analysis, at its core, is a ratio: projected points per draft dollar, or projected points relative to the expected output at a given ADP. A player projected for 280 points who typically goes in round 4 is more efficient than a player projected for 290 points who goes in round 2, assuming the scoring system and positional context are held constant. The surplus value drafting framework formalizes exactly this logic.
The scope of the analysis extends across all major fantasy formats — season-long snake, auction, best ball, and dynasty — though the inputs and the definition of "cost" shift in each context.
How it works
The mechanics depend on three components:
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Projection baseline — A per-player point forecast for the full season or relevant scoring period. This can come from consensus aggregators (FantasyPros publishes composite projections updated through the preseason) or proprietary models built from play-by-play data.
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Cost benchmark — ADP data from a large sample of drafts, typically sourced from platforms like Underdog, NFFC, or the NFFC Main Event, which runs one of the largest high-stakes draft competitions in the country. For auctions, cost benchmarks are drawn from actual auction results rather than ADP.
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Value over replacement baseline — Raw projected points alone are insufficient. A player projected for 180 points at running back is less valuable than one projected for the same 180 at quarterback in a single-QB league, because the replacement-level quarterback (the last starter in a 12-team league) scores far more than the replacement-level running back. This is the core logic explained in the value over replacement player framework.
Once those components are assembled, the analysis flags players where projected points significantly exceed what ADP implies — these are candidates for early targeting. Players where ADP outpaces projected output become candidates to avoid or trade away post-draft.
Common scenarios
Scenario 1: The injured incumbent
A running back enters a draft recovering from a torn ACL. Fear suppresses ADP by 2–3 rounds below his healthy projection. If the projection accounts for a realistic return timeline and still shows surplus value, the risk-adjusted efficiency may still favor drafting him — though injury risk and draft value discounting requires its own weighting.
Scenario 2: The new offensive coordinator effect
A wide receiver changes teams and lands in a high-volume passing offense. Consensus ADP has not caught up to the new opportunity share. Projection models that incorporate opportunity share and draft value will flag the receiver as underpriced relative to his ADP. This is one of the most reliable structural inefficiencies in fantasy markets, as noted in the market inefficiencies in fantasy drafts analysis.
Scenario 3: The aging veteran
A tight end with three consecutive 900-yard seasons is drafted at the same cost as the prior year, but age-related decline models show a 15–20% expected output reduction. The cost has not adjusted; the efficiency ratio has quietly deteriorated. Aging curves and player value provide the scaffolding for these adjustments.
Decision boundaries
Efficiency analysis produces a signal — it does not produce a decision. The boundaries of when to act on an efficiency gap require several qualifications.
Projection confidence interval matters more than the point estimate. A player with a wide variance band (a receiver on a new team with an unproven quarterback) carries more risk than his median projection implies. Breakout probability models are one tool for estimating that variance.
Positional scarcity adjusts the raw efficiency number. A surplus at quarterback in a single-QB league is worth less than an equal-sized surplus at running back, where replacement-level output drops off sharply after pick 40. The positional scarcity metrics framework quantifies this adjustment.
Format context changes the optimal threshold for acting on a gap. In best-ball formats — where consistency matters less than ceiling — a wider variance player with high projected upside offers more value than the same player would in a head-to-head weekly matchup format. Best ball draft value covers those distinctions in depth.
The core tension in efficiency analysis is precision versus actionability. A model can identify that a player is theoretically 12% underpriced at his ADP, but if that gap is within the normal noise range of projection error, the signal is meaningless. The useful threshold — what separates a genuine inefficiency from statistical static — varies by position, format, and projection methodology. The draft value analytics overview provides context for how these tools fit together in a broader draft preparation framework.