DMX is a single composite z-score evaluating NFL Draft prospects at the moment of the draft. It blends three orthogonal dimensions of pre-draft information into one number per player. D1 (top decile) is the elite tier; D10 is the bottom. The composite is computed per position with position-specific weights.
Position-specific weights are not published — they're the result of 25 years of iterative refinement and are central to what makes DMX distinct from simply summing combine metrics. See the Methodology tab for what this means in practice.
DPX tracks dynasty value during an NFL career — orthogonal to DMX, which only predicts pre-draft. Built on three observable career components updated every season:
DPX is published as two outputs per player-season: DPX-Next (next-season projection decile) and DPX-Long (3-year forward projection decile). Both modified by contract status and draft capital decay.
| Position | Target Metric | Intercept | Slope | R² | N | Cohort | Notes |
|---|---|---|---|---|---|---|---|
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Why tier R² runs 2× higher than VBD R²: Career outcomes cluster into discrete archetypes (Star / Starter / Contributor / Bust) much more cleanly than they distribute along continuous fantasy points. Predicting "what archetype" a prospect becomes is a more learnable problem than predicting their exact career point total. Both regressions are honest; the tier framework is more useful for dynasty roster decisions.
Each point is one prospect with a complete career. The diagonal line is perfect calibration. Points above the line outperformed prediction; below underperformed. For deeper residual analysis — including ranked overperformer/underperformer leaderboards by position — see the Analytics > Residual Analysis tab.
- Athleticism (ATH). Combine and pro-day measurables: 40-yard dash, vertical jump, broad jump, 3-cone, shuttle, bench press, height-adjusted weight. Each metric z-scored within position cohort, then composite-weighted into four sub-components (Speed, Agility, Lower Power, Strength).
- Draft Position (DPOS). Pick number transformed to z-score within position cohort. UDFAs assigned pick 300. This single dimension is the strongest predictor at every position — the NFL is collectively pretty good at evaluating prospects.
- Age-Weighted Production (AWP). Final college season's per-game production normalized by age and competition tier (FBS Power 5 / G5 / FCS). For IDP, replaced with havoc share to handle scheme variance.
- Composite assembly. Per-position weights blend the three components. Weights are calibrated by maximum-likelihood fit against career outcomes on a 25-year training set, refit roughly every 3 years. Specific weight values are not published.
Raw DMX is a continuous z-score. The decile assignment (D1–D10) is computed per (position, draft-year) cohort using NTILE(10) OVER (PARTITION BY position, draft_year ORDER BY dmx DESC). This means D1 is always relative to that year's class — a D1 in a weak class is not the same prospect as a D1 in a stacked class. The raw DMX score is more reliable for cross-class comparison.
- Landing spot. DMX is computed at the moment of the draft itself, so the prospect's team, coaching staff, depth chart situation, and offensive scheme are not encoded.
- Injury history. Pre-draft injuries are visible to NFL scouts and reflected in DPOS, but not modeled as a separate signal.
- Character / interview signals. Off-field information is signal-poor and noise-rich; we leave it to the NFL's evaluation expressed through DPOS.
- Position-versatility flags. A WR/RB hybrid is scored at his primary position. Some signal is lost.
Combine testing standards, college pass-rate context, and draft strategy have all changed materially since 1996. The DMX components are z-scored within draft year to partially absorb this, but the position-weight calibration is fit on long historical windows. A 1998 elite WR profile and a 2024 elite WR profile may look subtly different.
Comprehensive defensive college statistics are unavailable in any free, terms-of-service-clean source before 2004. Draft classes 2004 (n=117 prospects) and earlier have AWP set to NULL rather than zero or imputed. These prospects still receive an ATH+DPOS-only DMX, but with lower confidence. The Draft Board surfaces this explicitly.
AWP component-of-competition adjustments exist but FCS / Division II prospects with limited combine attendance can have noisy ATH. Notable historical examples of dominant FCS prospects (Adrian Peterson at Georgia Southern, the Walter Payton Award winners) are correctly captured, but smaller-school prospects deserve extra qualitative due diligence.
"5-year VBD" sums points-above-replacement across years 1–5 post-draft, with a hard window. This treats players with concentrated peaks differently from those with sustained mid-tier seasons. The peak_3yr_vbd_avg regression (also published in the table above) is an alternative formulation that rewards peak performance over total accumulation.
Career tiers (Star / Starter / Contributor / Bust) are derived from VBD percentiles within position cohort. The boundary thresholds are deterministic but somewhat arbitrary — a player who falls 2% below the Starter threshold is classified Contributor even though their actual career might feel "Starter-adjacent". Use the continuous residual values alongside the tier label.
Trade-Secret Disclosure
Three categories of model internals are not published: (1) DMX position-component weights, (2) IDP DMX position-component weights, and (3) DPX layer weights and dynasty modifier coefficients. These are the result of 25 years of iterative refinement on a curated proprietary dataset and constitute the platform's commercial differentiation. Per Anthropic's trade-secret guidance, exposing only outputs (scores, deciles, rankings, regression coefficients on those outputs) is the appropriate disclosure level. Every value visible in the table above is publicly readable via Supabase. Every value not visible is locked down behind row-level security with anon access revoked.
If you're an independent analyst who wants to validate the model: the regressions above are reproducible. Pull DMX scores for completed-career cohorts, compute career VBD or tier yourself, run OLS, and you should land on coefficients within rounding of what's published here. If you can't, that's a model bug we want to know about.