About DraftResearch.com

We built the dataset.
Then we built
the platform.

DraftResearch.com is a hockey analytics platform built on 61 draft classes of NHL outcomes across 47 feeder leagues. Interactive tools for analysts, broadcast-ready intelligence for media, and data-driven consulting for front offices – all from one dataset.

Methodology The Dataset Contact
The Work

One dataset, three products.

Every NHL draft pick across 61 draft classes has a CarProd tier assignment. The Career Production model classifies players using career games played, percentile rankings within draft position, and historical benchmarking across 47 feeder leagues.

The Player gives analysts interactive tools – The ReDraft Room, a Trade Calculator, CarProd Tables, and Prospect Probabilities. The Broadcaster delivers enriched player intelligence for 554K+ players with milestone tracking, hometown narratives, and auto-generated storyline snippets. Consulting Services brings that same dataset to NHL front offices with org depth charts, scout performance audits, and prospect tracking.

Nothing here is opinion. The model scores players based on comparable historical outcomes. When we say a player had a low probability score, we mean that historically, players with that profile convert at a low rate. We never speculate beyond what the data supports.

We mapped 30K+ scouting staff records linking every GM, scout, and coaching hire to the draft picks they made. Scouts and front office staff are treated with recognition and respect – never evaluation or criticism of individuals.

Our Principles
01
Data only. No opinions.
Every claim is a fact from the dataset. We never speculate on what a player could become. We report what historical probability says about players with comparable profiles.
02
Scouts are people, not targets.
The scouts and front office staff who appear in our analysis are treated with respect. Historical record and recognition – never evaluation or criticism of individuals.
03
Precision over volume.
16 content series on a disciplined weekly cadence. Every post is reviewed before it publishes. We write when we have something the data actually supports saying.
04
Built to use, not just read.
Interactive tools, not static reports. The ReDraft Room, probability models, and trade analysis that let you explore the data yourself – the same dataset that powers our consulting work.
The Dataset

What we built from.

61 draft classes of NHL outcomes, hand-assembled across 47 feeder leagues and all 32 organisations – with career game tracking through the 2025—26 season.

Last updated
March 4, 2026
61
Draft Classes
Full data from the 1964—2025 drafts. Career outcome tracking through the 2025—26 NHL regular season.
47
Feeder Leagues
OHL, WHL, QMJHL, SHL, Liiga, KHL, NCAA, USHL, BCHL, and 38 additional leagues across Europe and North America.
12,883
Draft Picks
Every pick across all 61 draft classes with steal/bust flags, pick-before/after context, and career GP tracking.
554K+
Players Enriched
Player bio, nationality, hometown, milestone tracking, draft context, and auto-generated broadcaster narrative snippets.
3,400+
Scouts & Staff
Unique GMs, scouts, coaches, and front office staff tracked across 32 NHL franchises with EP-matched profiles.
30K+
Staff Records
Every GM, scout, and coaching hire across 32 NHL franchises since 2008. Draft attribution linking staff to picks.
32
Organisations
All 32 NHL franchises with complete scouting staff records, CarProd tier analysis, and team draft tendency reports.
Methodology

How the model works.

The probability score is a single number – the model's estimate that a player reaches 200 NHL regular season games from their draft year. Here's how it's built.

See the Ladder
Step 01
League tier assignment
Each of the 47 feeder leagues is assigned a historical conversion tier based on historical NHL conversion rates at equivalent draft positions. The SHL and Liiga rank at the top. The KHL is treated separately due to age and circumstance factors that differ from development leagues.
Tiers recalibrate annually with the addition of each new completed draft year.
Step 02
Age-adjusted production score
A player's production is adjusted for age within their birth year cohort. A 17-year-old producing at a level typical of 19-year-olds in the same league receives a higher adjusted score than the raw numbers suggest. This is one of the model's most predictive variables.
Birth year cohort windows: Sep 15 — Sep 14 (matching NHL eligibility rules).
Step 03
Historical comparables
For each prospect, the model identifies all historical players with similar league tier, age-adjusted score, and position. The proportion of those players who reached 200 NHL games is the raw probability estimate before further adjustment.
Minimum comparable pool: 30 players. Pools below this threshold receive a confidence flag.
Step 04
Position and draft year calibration
Position carries different conversion baselines – historically, centres and defence have higher conversion rates than wingers at equivalent scores. Draft year cohort size also affects probability, as larger draft classes produce more competition for roster spots.
Step 05
Weekly update cycle
Scores recalculate every Tuesday using the most recent available game data from all 47 leagues. Significant statistical shifts – a major scoring streak, a sudden drop in ice time – are reflected within one update cycle.
The model does not incorporate injury information, trade data, or any non-game-performance inputs.
Note
What this is not
A probability score is not a ranking. Two players with the same score are equally likely to make the NHL – the model makes no preference between them. A score of 40 is not "bad" – it means roughly 40% of historically comparable players reached 200 games. Many excellent NHL careers began with scores in that range.
Contact

Get in touch.

Questions about the model, Pro access, media, or consultancy work.

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