🎓 Learning SkillCorner Open Data
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. |