What biases are in YOUR scouting tools?
Every AI has blind spots. We can tell you exactly where yours are.
After fingerprinting 5 frontier models across 45,300 evaluations, we know how to find systematic biases in AI talent assessment systems.
The problem
Your club uses AI for scouting, shortlisting, or analytics. But you don't know:
- ×Which player types it systematically undervalues
- ×When its recommendations are most likely wrong
- ×What league or position biases it carries
- ×If it's missing opportunities competitors might find
These blind spots are predictable. They can be mapped. We've proven it.
What we do
Fingerprint your system
We run your AI through our 12-dimension behavioral test. Same methodology we used on GPT, Claude, Grok, and Gemini.
Identify systematic biases
Where does it over-adjust? Under-adjust? Which contexts trigger predictable errors? We map the patterns.
Test against real outcomes
Using World Cup 2026 results (104 matches, known outcomes), we validate if the predicted biases actually hurt accuracy.
Deliver actionable report
When to trust it, when to override it, which player profiles to double-check. Specific, testable recommendations.
How fingerprinting helps
Example: Home Advantage Calibration (PC02)
We discovered Claude Sonnet 4.6 systematically over-adjusts for home advantage by 8-12 percentage points in host nation matches.
Real Impact:
- • If your system uses Claude, it's undervaluing away teams in host contexts
- • This bias cost 4.2% accuracy in our World Cup test
- • Knowing this, you can correct for it or use a different model in those scenarios
This is one dimension. We test 12 dimensions + 5 calibration tests = 17 potential bias vectors.
Who this is for
Professional clubs
Using AI for player valuation, shortlisting, or scouting reports. Want to know the blind spots.
Player agencies
Need to understand how AI tools are evaluating your clients and where biases work for or against them.
Analytics vendors
Building AI-powered tools for the football industry. Want independent validation and bias testing.
What you get
Behavioral fingerprint report
12 dimensions tested, scored, and compared against frontier model baselines
Prediction calibration analysis
Where your system is over/under-confident. Which contexts reduce accuracy.
Real-world validation
Testing against World Cup 2026 outcomes to prove impact of identified biases
Actionable recommendations
When to trust, when to override, which contexts to double-check. Specific to your workflow.
Executive presentation
60-minute walkthrough for your technical and scouting leadership
Request an audit
Tell us about your AI system and we'll respond with a proposal within 48 hours.