Upload README.md

README.md

@@ -1 +1,41 @@
-See /app/README.md
+# Soccer Feature Engineering Hackathon — 33 Match-Level Features
+
+This repository contains a complete, reproducible feature engineering pipeline for the **Kaggle Soccer Feature Engineering Hackathon**, built from SkillCorner Open Data.
+
+## Output
+
+- **`features.csv`** — 18 rows (9 matches × 2 teams), 35 columns:
+  - `match_id`, `team_id`
+  - **33 engineered features** across 5 behavioral dimensions
+
+All values are raw aggregated counts or cumulative distances — no percentages, no ratios.
+
+## Feature Architecture
+
+| Dimension | Features | Description |
+|-----------|----------|-------------|
+| **Attacking Structure** | `att1`–`att5` | Passes/carries into final third, opponents bypassed, last-line breaks, advanced possession |
+| **Build-Up Profile** | `att6`–`att10` | Phase-type volume vector (build_up, direct, set_play, quick_break, transition) |
+| **Possession Quality** | `att11`–`att20` | Tempo, threat creation, defensive-line displacement (metres), passing options |
+| **Pressing & Defensive Shape** | `def1`–`def7` | Pressing volume, counter-press, recovery press, chain architecture, danger stopped |
+| **Off-Ball Movement Intelligence** | `run1`–`run5` + `att_give_and_go_initiated` | Line-breaking, line-pushing, behind, overlap, attacking-third runs, give-and-go |
+
+## How to Run
+
+```bash
+git clone https://github.com/SkillCorner/opendata.git
+pip install pandas numpy
+python soccer_feature_engineering.py
+```
+
+The script dynamically discovers all `*_dynamic_events.csv` files via `glob` — no hardcoded match IDs required.
+
+## Data Source
+
+[SkillCorner Open Data](https://github.com/SkillCorner/opendata) — 9 matches of Australian A-League 2024/2025, MIT License.
+
+## Requirements
+
+- Python 3.8+
+- pandas
+- numpy