AI USE CASE
AI Video Player Performance Analysis
Automatically extract player movement, tactical, and performance metrics from game footage.
What it is
Computer vision and deep learning models analyze match recordings to track player positions, speed, tactical patterns, and physical load in near real-time.
Data you need
Historical and live match video footage with consistent camera angles, ideally supplemented by GPS or tracking data for model calibration.
Required systems
- data warehouse
Why it works
- Standardise camera placement and video resolution across all training and match venues before deployment.
- Involve coaches and analysts in defining the KPIs and visualisations so outputs match real decision-making needs.
- Run a parallel pilot season where AI outputs are compared side-by-side with expert analysis to build trust.
- Connect the analytics platform to existing squad management or injury-tracking software for actionable workflows.
How this goes wrong
- Inconsistent or low-quality camera setups produce unreliable tracking data and degrade model accuracy.
- Coaching staff distrust automated metrics and revert to traditional manual review, abandoning the tool.
- Model trained on one sport or league fails to generalise when applied to different play styles or camera angles.
- Lack of integration with existing performance management workflows means insights are never acted upon.
When NOT to do this
Don't pursue this if your club or federation lacks standardised multi-camera video infrastructure and a dedicated analyst to interpret outputs, raw model outputs without contextual interpretation rarely change coaching decisions.
Example vendors
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