🎓 Learning SkillCorner Open Data
May 5, 2026 · View on GitHub
Welcome to the SkillCorner Open Data tutorials. We've organized our tutorials into logical learning paths to help you navigate from foundational data concepts to advanced analytical workflows.
💡 Note: You can find reusable Python modules for data loading, processing, and visualization used across these tutorials in the src/ directory.
🏗️ Path 01: Getting Started with SkillCorner Data
Focused on the foundational performance data (aggregates) and how to derive immediate insights.
| Tutorial | Description |
|---|---|
| Data Normalization Basics | Understanding key principles on filtering, P90/P60 normalization and thresholds. |
| Visualization with SkillCorner | Master key visuals for SkillCorner data using our proprietary library. |
| Multiple Metrics & Z-Scores | Learn how to use z-scores to handle multiple metrics at the same time and build archetypes. |
| Building Striker Archetypes | Combine multiple datasets to build tactical profiles for specific roles. |
🧠 Path 02: Working with Game Intelligence & Dynamic Events
Deep dive into the contextual data layers that define the narrative of a match.
| Tutorial | Description |
|---|---|
| Part 1: Aggregating Dynamic Events | Aggregate and process SkillCorner dynamic event. |
| Part 2: Aggregating Phases of Play | Aggregating phases of play at team level. |
| Part 3: Off-ball Runs & Pitch Viz | Visualizing runs and positioning on the pitch using dynamic event level data. |
| Part 4: Merging Events & Tracking | Synchronizing dynamic event data with continuous tracking streams. |
| Part 5: Animated 2D Video | Generating animated 2D visualizations from tracking and event data. |
| Part 6: Build Your Own Metric (Cutbacks Example) | Designing custom metrics to detect and evaluate cutback opportunities. |
📍 Path 03: Basics of Tracking
The core of SkillCorner: working with raw X/Y coordinates and spatial data.
| Tutorial | Description |
|---|---|
| Tracking Core Tutorial | Loading raw JSONL tracking data and visualizing fundamental positioning. |
| Kloppy Integration | Using the industry-standard Kloppy library for data standardization. |
🎨 Path 04: Visualization
| Tutorial | Description |
|---|---|
| Sectioned Summary Table | Create a comprehensive table comparing players across multiple metric categories. |
| OffBall Runs Radar | Create a standard offball run radar. |