Football talent intelligence
Find the player before the market does.
Search by how a player plays, not only where they play. Filter the market by role, output and statistical profile, then see exactly why each name came back.
Example breakdown
Ball-Winning Midfielder profile
91
DNA Match
The vertical marker on each bar is the percentile this role expects. Matching means sitting close to it, not sitting as high as possible.
How the workflow runs
Discover, understand, compare, shortlist.
Discover
Filter by age, role, competition, minutes and statistical profile until the list is short enough to work with.
Understand
Open a player and see the metrics behind every score, the peer group, and what the data does not cover.
Compare
Put two to four players side by side on the metrics the role actually depends on.
Shortlist
Save players to a watchlist with notes, and keep the search so it can be rerun as data updates.
Search tools
Four ways into the same dataset.
Each one answers a different recruitment question. They share the same normalised data, the same peer groups and the same explanation of how a number was produced.
Player Finder
The full database with role, age, competition, minutes and score filters, sortable on any score.
Which players fit these constraints?
DNA Finder
Match the market against a role profile and see the metric-by-metric fit behind each result.
Who plays the way this role needs?
Kanté Finder
A dedicated view for ball-winning midfielders: recovery volume, pressing and duel work.
Who wins the ball back at this level?
Compare
Two to four players on the same axes, with the gaps and strengths made explicit.
Which of these is the better fit?
Explainability
Every number says where it came from.
A score with no context is a guess with a decimal point. Each result in ScoutDNA carries the metrics behind it, the peer group it was measured against, the size of that group, and how much of the data was actually available.
- Data. What the source supplied, with the season and competition it applies to.
- Model output. Scores and match percentages, each traceable to a model version.
- Uncertainty. Minutes played, metric coverage and peer group size, stated rather than hidden.
How is this calculated?
- Peer group
- Midfielders, Demo Premier Division, 2025/26 (214 players)
- Normalisation
- Per 90 minutes, then percentile within the peer group
- Minimum sample
- 900 minutes for this profile
- Metric coverage
- 9 of 10 metrics supplied by the source
- Model version
- DNA model 1.0.0
Statistical similarity is not a prediction of career outcome. Further live and video scouting is recommended.
Current coverage
What is loaded in this installation right now. These are counts from the database, not projections.
- Players
- 1,231
- Competitions
- 4
- Seasons
- 2
- Role profiles
- 7