Traditional scouting tells you what a player looks like today. CADIG is built to answer the next question: how likely is that player to improve, translate across levels, and create future organizational value?
A scouting grade is a snapshot. CADIG layers the snapshot with development context, league difficulty, age, historical outcome data and projected future value. The purpose is to create a repeatable decision framework for teams, leagues, families and organizations.
Live/video scouting of skating, processing, skill, compete, decision-making, role and context.
Adjust performance for age, league strength, role and competition so unlike environments can be compared.
Estimate development velocity, risk, future role and projected pWAR rather than relying on current production alone.
Compare the projection against historical draft outcomes to understand what similar trajectories became.
This demonstration shows the structure CADIG would use for a verified prospect. Numerical values below are illustrative model outputs until the specific player dataset is validated.
A positive value gap means CADIG's model and scouting process are identifying a prospect as more valuable than the current market or consensus ranking suggests. That is where scouting becomes decision advantage.
A prospect should not be viewed as a fixed object. CADIG tracks whether development is accelerating, flattening or regressing relative to age and competition.
Age-adjusted scoring and primary involvement after league normalization.
Quality of competition, role and minutes relative to peers.
Rate of improvement across seasons and rising levels of competition.
Skating, processing, puck skill, defense, compete, role and translatability.
Deployment, special teams, matchup burden and opportunity quality.
Physical projection, volatility, sample size and uncertainty.
The analytics layer does not replace scouting. It gives the scout context. CADIG combines the numbers with details that are difficult to capture cleanly in public datasets:
CADIG's historical archive provides a calibration layer. The point is not to say a prospect “is the next” star. It is to understand the range of outcomes produced by similar value profiles and draft-position gaps.
| DRAFT | PLAYER | CAREER WAR | DRAFT MOVEMENT | LESSON |
|---|---|---|---|---|
| 2017 | Cale Makar | 81.3 | +3 | Elite development can exceed already-high draft expectations. |
| 2017 | Jason Robertson | 59.1 | +37 | Later selections can create first-tier value when development accelerates. |
| 2016 | Adam Fox | 67.2 | +63 | Draft slot does not fully capture translatable processing and puck-moving value. |
| 2015 | Sebastian Aho | 84.3 | +31 | Production, intelligence and development context can identify undervalued upside. |
| 2014 | Brayden Point | 79.2 | +76 | Historical outperformance demonstrates why growth projection matters. |
The CADIG question is not simply “Where is the player ranked?” It is “What explains the ranking, how is the player changing, and where could that development create value?”
Scouting evaluation, model components, development trajectory, risk and future-value summary.
Historical outcome ranges, draft-slot movement and context for what similar development profiles became.
A repeatable framework designed to help prioritize viewings, targets, follow-up scouting and organizational fit.
CADIG's differentiation is the combination of traditional hockey evaluation with a structured intelligence layer. The scout remains central. Analytics provide context, comparison and projection. Together, they create a clearer picture of both the player today and the player that may exist tomorrow.
