silly-kicks/ghost-gk-v1
Tabular Regression • Updated
AI & ML interests
Soccer analytics library — multi-provider SPADL + tracking conversion, VAEP/xT valuation, and spatial features (pitch control, cover shadows, player influence)
Recent Activity
Organization Card
(Ministry of) Silly Kicks
Classify and value on-ball football actions using SPADL and VAEP.
Open-source Python library for soccer analytics — converts raw event and tracking data from multiple providers into a unified SPADL representation, then values every on-ball action using VAEP (Valuing Actions by Estimating Probabilities). The Hugging Face Hub hosts pre-trained model weights that ship with the library.
- PyPI:
pip install silly-kicks - GitHub: karsten-s-nielsen/silly-kicks
- License: MIT
Models
| Model | Architecture | Description |
|---|---|---|
| ghost-gk-v1 | RFCDE (HistGBR leaf co-occurrence + 2D KDE) | Conditional density estimation for league-average goalkeeper positioning. 26 goal-relative features, 60×64 probability grid output. Two variants: "default" (approx. 9 MB, bundled in wheel) and "full" (approx. 91 MB, downloaded from Hub). |
What silly-kicks Does
Event data converters (spadl/): StatsBomb, Opta, Wyscout, Sportec, Metrica, Gradient Sports, SkillCorner, plus a Kloppy gateway. All return standardized SPADL DataFrames with guaranteed columns and dtypes.
Tracking data (tracking/): 20-column long-form schema with native adapters for Sportec, Gradient Sports, and Kloppy gateway (Metrica, SkillCorner). 30+ tracking-derived feature families including pitch control, pressure, defensive line geometry, off-ball runs, team shape, cover shadows, DAS, OBSO, PAUSA, and ghost-GK positioning.
Action valuation (vaep/): Binary classifiers (P(scores), P(concedes)) with XGBoost/CatBoost/LightGBM backends. Hybrid-VAEP removes result leakage. Tracking-aware features integrate seamlessly via composition.
Academic Foundations
| Module | Foundation |
|---|---|
| SPADL + VAEP | Decroos et al., "Actions Speak Louder than Goals" (2019) |
| Pitch Control | Spearman, "Physics-Based Modeling of Pass Probabilities" (2017) |
| Ghost-GK | Le et al. (2017, ghosting); Dutta et al. (2024, NFL RFCDE); Pospisil & Lee (2018, RFCDE) |
| Pressure | Andrienko et al. (2017, oval zones); Bekkers (2024, π-model) |
| OBSO | Spearman (2018, off-ball scoring opportunity) |
| DAS | Bischofberger & Baca (2026, accessible space) |
Links
- Related project: (Right! Luxury!) Lakehouse — full soccer analytics platform built on Databricks, consuming silly-kicks as its core analytics engine
datasets 0
None public yet