| # Soccer Feature Engineering Hackathon — 33 Match-Level Features |
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| This repository contains a complete, reproducible feature engineering pipeline for the **Kaggle Soccer Feature Engineering Hackathon**, built from SkillCorner Open Data. |
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| ## Output |
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| - **`features.csv`** — 18 rows (9 matches × 2 teams), 35 columns: |
| - `match_id`, `team_id` |
| - **33 engineered features** across 5 behavioral dimensions |
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| All values are raw aggregated counts or cumulative distances — no percentages, no ratios. |
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| ## Feature Architecture |
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| | 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 |
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| ```bash |
| git clone https://github.com/SkillCorner/opendata.git |
| pip install pandas numpy |
| python soccer_feature_engineering.py |
| ``` |
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| The script dynamically discovers all `*_dynamic_events.csv` files via `glob` — no hardcoded match IDs required. |
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| ## Extended Analysis |
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| `soccer_feature_engineering_extended.py` adds: |
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| | Output | Description | |
| |--------|-------------| |
| | `features_first_half.csv` | All 33 features computed on 1st-half events only | |
| | `features_second_half.csv` | All 33 features computed on 2nd-half events only | |
| | `features_halves_diff.csv` | 2nd half minus 1st half — reveals tactical fatigue & in-game adjustments | |
| | `behavioral_fingerprint.csv` | 16-dim fingerprint per team-match (phase profile + pressing + runs + structure) | |
| | `cluster_labels.csv` | KMeans cluster assignments (k=4) on standardized fingerprint | |
| | `archetype_profiles.csv` | Centroid profiles for each discovered tactical archetype | |
| | `similarity_matrix.csv` | Pairwise cosine similarity between all 18 team-match fingerprints | |
| | `analysis.png` | Radar chart grid of the 4 archetypes | |
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| ## Tactical Archetypes Discovered (k=4) |
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| | Cluster | Signature | Characteristics | |
| |---------|-----------|-----------------| |
| | **0** — "Resilient Block" | Low build-up (71.6), modest pressing, balanced all-around | Conservative teams that stay compact; low risk, low reward | |
| | **1** — "Aggressive Dominator" | High build-up (85.0), massive pressing chains (231.7), elite off-ball runs (115.7) | High-press, high-tempo teams that generate territory and options | |
| | **2** — "Transition Specialist" | Low build-up (45.3), high quick_breaks (10.3), low runs (8.3) | Relies on direct play and transitions; minimal patient possession | |
| | **3** — "Organized Builder" | Highest build-up (90.3), balanced pressing, disciplined runs | Patient positional play with systematic defensive line pressure | |
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| ## Data Source |
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| [SkillCorner Open Data](https://github.com/SkillCorner/opendata) — 9 matches of Australian A-League 2024/2025, MIT License. |
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| ## Requirements |
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| - Python 3.8+ |
| - pandas, numpy |
| - scikit-learn (for extended clustering) |
| - matplotlib, seaborn (for extended visualizations) |
