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
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.
Extended Analysis
soccer_feature_engineering_extended.py adds:
| 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 |
Tactical Archetypes Discovered (k=4)
| 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 |
Data Source
SkillCorner Open Data — 9 matches of Australian A-League 2024/2025, MIT License.
Requirements
- Python 3.8+
- pandas, numpy
- scikit-learn (for extended clustering)
- matplotlib, seaborn (for extended visualizations)