About Draft Research

We built the model.
Then we built
the publication.

Draft Research is a data-driven hockey analytics platform. We apply a probability model to 10 years of NHL draft outcomes across 36 feeder leagues — and publish everything it tells us, free and openly.

Methodology → The Dataset → Contact →
The Work

The data behind the decision.

Every NHL draft pick in the last 10 years has a probability score. That score is built from historical conversion rates across 36 feeder leagues — how often players with that profile actually make the NHL, measured by reaching 200 career regular season games.

We built that model. Then we built a content system to publish what it tells us: weekly probability rankings, model vs. actual draft comparisons, league conversion analyses, scout staff records, and historical class reviews.

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.

Draft Research covers the scouts and front office staff behind these decisions with the same rigour. The Scout Roll Call and Territory Report series exist to recognise the people behind each class — not evaluate them.

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
Free first, Pro for depth.
All 16 content series are free and public. Pro unlocks the raw model — full probability scores for every prospect, team tendency reports, and tools for serious analysts.
The Dataset

What we built from.

10 years of NHL draft outcomes, hand-assembled across 36 feeder leagues and all 32 organisations — with career game tracking through the 2025–26 season.

Last updated
March 4, 2026
10
Draft Years
Full data from the 2014–2023 drafts. Career outcome tracking through the 2025–26 NHL regular season.
36
Feeder Leagues
OHL, WHL, QMJHL, SHL, Liiga, KHL, NCAA, USHL, BCHL, and 27 additional leagues across Europe and North America.
7k+
Draft Picks
Every pick from rounds 1–7 across all 10 draft years, with career GP tracked for outcome analysis.
32
Organisations
All 32 NHL franchises with complete scouting staff records — by territory and pick — per draft year.
200
GP Threshold
The benchmark for "making the NHL." 200 career regular season games represents an established, recurring NHL player.
500+
Scout Records
Individual scout and front office staff records per team, per draft year, with territory assignment and picks attributed.
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 36 feeder leagues is assigned a historical conversion tier based on 10-year 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 36 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.

hello@draftresearch.com
@draftresearch on X u/draftresearch on Reddit @draftresearchHQ on Instagram @DraftResearch on YouTube