# TSX Sports AI — Football Performance Analytics

Canonical URL: https://alectorlab.com/work/tsx-sports-ai-football-analytics
Raw Markdown: https://alectorlab.com/work/tsx-sports-ai-football-analytics/raw
Industry: Sports AI / Football
Capabilities: Computer Vision, AI Product Engineering
Owner: Alector Lab – Enabling Technologies Ltd (direct)
Provenance: Alector Lab project record; outcome labels are descriptive rather than independent performance measurements.
External Reference: https://tsxsports-ai.com/

## Summary
A football intelligence product that turns fixed sideline footage into player tracking, scan-frequency measures, decision analysis, and coaching KPIs.

## Operational Problem
Many clubs already record matches but still depend on manual tagging or broadcast-oriented tools. That leaves useful player behaviour, awareness, pressure, and decision context buried inside video.

## Operating Context
TSX Sports AI is designed around fixed sideline cameras that pan and zoom with play. The product positions analysis as a repeatable coaching workflow rather than a specialist data-science exercise.

## Engineering Approach
The pipeline follows players through camera movement, calibrates the pitch into real-world coordinates, reconstructs match events, and computes player and team measures including scan frequency and composite performance indicators.

## System Architecture
1. Ingestion: existing fixed sideline match footage
2. Perception: player tracking through camera pans
3. Calibration: pitch homography and real-world positioning
4. Intelligence: events, pressure, scan frequency, and composite KPIs

## Implementation Details
Clubs upload footage, follow analysis progress, and receive match views built for coaching review. The product also supports event extraction and broader pitch coverage through multiple camera angles where available.

## Governance-Reviewed Outcomes
- **No tagging (Automated workflow):** From uploaded footage to coaching insight
- **Sideline (Camera-first design):** Built for the fixed-pivot footage many clubs already record

## Technology Stack
- Computer Vision
- Player Tracking
- Pitch Calibration
- Video Analytics

## Engineering Lessons Learned
- A useful club product has to match the way teams actually film, not assume broadcast cameras or wearable tracking data.
- Performance measures become more credible when coaches can trace them back to visible events and player movement.