Upload README.md
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README.md
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The script dynamically discovers all `*_dynamic_events.csv` files via `glob` — no hardcoded match IDs required.
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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+
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- pandas
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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 |
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|--------|-------------|
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| `features_first_half.csv` | All 33 features computed on 1st-half events only |
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| `features_second_half.csv` | All 33 features computed on 2nd-half events only |
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| `features_halves_diff.csv` | 2nd half minus 1st half — reveals tactical fatigue & in-game adjustments |
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| `behavioral_fingerprint.csv` | 16-dim fingerprint per team-match (phase profile + pressing + runs + structure) |
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| `cluster_labels.csv` | KMeans cluster assignments (k=4) on standardized fingerprint |
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| `archetype_profiles.csv` | Centroid profiles for each discovered tactical archetype |
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| `similarity_matrix.csv` | Pairwise cosine similarity between all 18 team-match fingerprints |
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| `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 |
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|---------|-----------|-----------------|
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| **0** — "Resilient Block" | Low build-up (71.6), modest pressing, balanced all-around | Conservative teams that stay compact; low risk, low reward |
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| **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 |
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| **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 |
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| **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+
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- pandas, numpy
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- scikit-learn (for extended clustering)
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- matplotlib, seaborn (for extended visualizations)
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