Scouting Experience
Live evaluation, video, player traits and projection remain the foundation. Analytical work is designed to sharpen — not substitute for — hockey judgment.
Justin’s role at CADIG focuses on building the analytical infrastructure around traditional scouting.
His work connects player evaluation with league intelligence, development pathways, career outcomes, market segmentation and structured data systems.
The goal is to turn hockey information into repeatable, decision-ready intelligence that complements the group’s scouting expertise.
Justin’s work expands the range and depth of traditional scouting through data, analytics, historical player-development research and cross-league intelligence. The objective is not to replace the scout’s eye — it is to give scouts another layer of evidence that helps determine where to look, which players deserve additional attention and which signals may be appearing before reputation catches up with ability.
Live evaluation, video, player traits and projection remain the foundation. Analytical work is designed to sharpen — not substitute for — hockey judgment.
Production, age curves, role, usage, trends and historical comparables are structured to identify players whose underlying profile may be stronger than conventional totals suggest.
Competition strength, development pathways, advancement rates and geography provide context so production in the CHL, NCAA, Europe and smaller development environments is not treated as interchangeable.
Career histories help answer a more useful question: what happened next to players with similar age, position, production, environment and scouting characteristics?
Hockey history contains extraordinary players who were not obvious future superstars at the earliest stages of their development. Pavel Datsyuk and Nicklas Lidström illustrate the question that drives this work: what information could have helped an organization recognize exceptional NHL potential earlier?
CADIG is not claiming that an algorithm can simply identify “the next Datsyuk” or “the next Lidström.” The goal is to build an intelligence system capable of flagging unusual development signals, undervalued leagues and regions, atypical career trajectories, transferable skills and players whose underlying profile warrants deeper scouting attention.
Which leagues, clubs and regions repeatedly produce players who outperform expectations?
Which statistical and scouting characteristics appear before major advancement?
Who is performing unusually well after adjusting for age, league strength, role and opportunity?
Which prospects resemble successful historical players at the same developmental age?
Where does CADIG’s scouting evaluation disagree with the market — and does the historical evidence support another look?
Which development pathways create professional value despite receiving comparatively little attention?
A repeatable infrastructure for expanding coverage, identifying overlooked talent, adding context to projection and improving the probability of recognizing exceptional players earlier.
Graduate education in sports management and hands-on work in sports operations, analytics, scouting research and business intelligence, with technical experience across SQL, Python, Power BI, Tableau and R.
Justin structures hockey questions into measurable units — player, league, pathway, market and outcome — then uses scouting context and data together to identify patterns and support better decisions.
League classification, pathway scoring, Advancement Delta and Power BI-ready data architecture.
VIEW SIJHL →Agency and individual-agent market segmentation.
VIEW MARKET WORK →