SkillCorner Analytics Platform

December 28, 2025 · View on GitHub

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SkillCorner Analytics Platform

An interactive web-based football analytics platform for comprehensive match and team performance analysis using SkillCorner tracking and event data.

Abstract

Introduction

This submission presents an interactive web-based analytics platform built with SvelteKit and TypeScript, transforming SkillCorner tracking and event data from the SkillCorner Open Data Repository into actionable insights through intuitive, synchronized visualizations.

Use Cases

Team Analysis: Interactive scatter plots for comparative team performance across matches. Dynamic metric selection on both axes with color-coded team identification and match filtering reveals performance.

Match Analysis: Frame-by-frame match replay with synchronized components: football field with player/ball tracking, phase-of-play detection showing ball movement ranges and pass networks, chronological events list with automatic scrolling, and cumulative performance charts (xThreat, player-targeted xThreat, xPass completion). Interactive timeline navigation with adjustable playback speed.

Player Analysis: Individual event tracking with detailed metrics including possession time, force backward actions, affected line breaks, and regain statistics.

Scouting: Cross-match performance aggregation enabling player comparison and recruitment insights through objective, quantifiable metrics.

Potential Audience

  • Performance Analysts: Post-match tactical analysis and opponent scouting
  • Coaching Staff: Training session planning and in-game strategy development
  • Recruitment Teams: Objective player evaluation across competitions
  • Football Enthusiasts: Educational tool for understanding modern analytics

Run Instructions

Prerequisites

  • Node.js (v18 or higher)
  • npm or pnpm

Installation

# Install dependencies
npm install

# Run development server
npm run dev

# Build for production
npm run build

# Preview production build
npm run preview

The application will be available at http://localhost:5173

Video URL

Video Demo 60s

URL to Web App / Website

Here the Skillcorner-PySport Web Application

Project Structure

src/
├── lib/
│   ├── components/
│   │   └── features/
│   │       ├── match-analysis/      # Match replay components
│   │       ├── team-analysis/       # Team comparison visualizations
│   │       └── player-analysis/     # Individual player metrics
│   ├── utils/
│   │   ├── match-analysis/          # Phase detection, coordinate transforms
│   │   ├── dataFilters.ts           # Data processing utilities
│   │   └── coordinateTransform.ts   # Spatial data transformations
│   └── types/                       # TypeScript type definitions
├── routes/
│   └── (app)/
│       ├── match-analysis/          # Match analysis page
│       ├── team-analysis/           # Team comparison page
│       └── player-analysis/         # Player metrics page
└── data/                            # SkillCorner dataset (from GitHub repo)

Development Notes

Project Context

This project was developed as part of the SkillCorner X PySport Analytics Cup (Analyst Track). The platform demonstrates how rich spatiotemporal football data can be transformed into accessible, interactive visualizations for tactical analysis.

Technical Challenges

During development, hardware failure resulted in partial code loss despite version control. Some components and code cleanup work had to be rebuilt from earlier commits, which may result in inconsistent code quality across different modules.

Design & Planned Features

An extensive design system was created in Figma with comprehensive wireframes and mockups. Several planned features from the original design were not implemented:

  • Match Analysis Enhancements:

    • Events leading to goals with interactive chart analysis
    • Individual player charts
    • Dynamic player statistics overlay synchronized with match playback
    • Real-time metric updates during timeline navigation

  • Additional Features:

    • Advanced filtering and search capabilities
    • Export functionality for analysis reports

Current Limitations

  • Code Quality: Some sections require refactoring and better documentation
  • Performance: Large datasets may cause slowdowns in certain visualizations
  • Testing: Limited automated test coverage

Development Process

As a Data Engineering student learning web development and football analytics, this project represents significant learning across multiple domains. Development utilized AI assistance (Claude Anthropic) for code implementation, debugging, and technical guidance. All design choices, feature specifications, and data analysis approaches were directed by myself.

Future Improvements

  • Code refactoring and comprehensive documentation
  • Performance optimizations for large datasets
  • Additional metric visualizations and analysis features
  • Enhanced error handling and edge case coverage
  • Unit and integration testing suite
  • Mobile-responsive design improvements

Data Attribution

All tracking and event data used in this project comes from the SkillCorner Open Data Repository. The data has been included in this repository for submission purposes as per Analytics Cup requirements.

License

This project was created for the SkillCorner X PySport Analytics Cup. Please refer to the competition rules for usage guidelines.

Acknowledgments

  • SkillCorner for providing high-quality tracking and event data
  • PySport for organizing the Analytics Cup
  • The football analytics community for inspiration and methodological frameworks