How ScoutWizz ensures the SWS scoring system is fair and objective.
This document sets out the principles that guide Scoutwizz when designing and deploying data analytics models and SWS score calculations, with the aim of preventing discrimination and ensuring fair opportunities for all athletes.
Scoutwizz performance analytics and recommendation algorithms rely exclusively on sporting and performance-related metrics (KPIs). They do not use personal characteristics that may form a basis for discrimination, such as:
The SWS score and related indicators are derived from measurable statistical data (e.g. attacking, passing, defensive and overall metrics) that reflect on-field performance. The criteria, weights and formulas are designed to capture sporting effectiveness rather than personal identity or background.
Scoutwizz will periodically review analytical outputs to detect potential indirect bias — for example, systematically lower ratings for particular groups unrelated to on-field performance. If bias is detected, the underlying data, features and weighting schemes will be re-evaluated and adjusted in consultation with domain experts (scouts, coaches, analysts) to improve fairness.
Scoutwizz provides users with a high-level explanation of the structure of SWS scores, including the main performance categories and examples of contributing actions. This supports transparency and user understanding while protecting proprietary details of the models.
These principles are applied in line with applicable anti-discrimination and data protection laws, as well as emerging ethical guidelines on AI and analytics in sports. The document is reviewed regularly to reflect regulatory developments and best practices in ethical sports analytics.