pca_football_profiling
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.1%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: CezaryKlimczuk
- Language: Jupyter Notebook
- Default Branch: master
- Size: 26.1 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
Online supplement to Explainable PCA Decomposition of Player Features for Football Profiling
This repo contains the Supplement Material and full research code for "Stable Stylistic Axes in Football: Interpretable Principal-Component Decomposition of Player Data from the Top-Five European Leagues"
Setup guide
Follow these steps to set up your project with Python package manager - uv:
- Install uv
Choose one of the following installation methods. For more installation options, refer to the uv installation documentation.
Standalone Installer:
For macOS and Linux:
sh curl -LsSf https://astral.sh/uv/install.sh | shFor Windows:
powershell powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Using pip:
sh pip install uvUsing pipx:
sh pipx install uv
- Clone the Repository
Replace <repository_url> with your repository's URL:
sh
git clone <repository_url>
cd <repository_name>
- Synchronize Dependencies
In the project directory, run:
sh
uv sync
This command will install all necessary dependencies as specified in pyproject.toml and uv.lock files.
- All set! You should be able to run all notebooks without any issues
Citation (CITATION.cff)
cff-version: 2.0.0
message: "If you use this code, please cite:"
title: "Stable Stylistic Axes in Football: Interpretable Principal-Component Decomposition of Player Data from the Top-Five European Leagues"
version: "1.0.0"
doi: "10.5281/zenodo.16235038"
date-released: 2025-07-20
license: MIT
authors:
- family-names: Klimczuk
given-names: Cezary
orcid: 0009-0004-1924-9700
keywords:
- principal component analysis
- dimensionality reduction
- football profiling
- explainable latent components
- football analytics
- robustness
repository-code: "https://github.com/CezaryKlimczuk/pca_football_profiling"
GitHub Events
Total
- Release event: 1
- Push event: 2
- Public event: 1
- Create event: 1
Last Year
- Release event: 1
- Push event: 2
- Public event: 1
- Create event: 1
Dependencies
- ipykernel >=6.29.5
- numpy <=1.25
- pandas ==2.0.0
- scikit-learn <1.3
- scipy >=1.15.3
- seaborn >=0.13.2
- appnope 0.1.4
- asttokens 3.0.0
- cffi 1.17.1
- colorama 0.4.6
- comm 0.2.2
- contourpy 1.3.2
- cycler 0.12.1
- debugpy 1.8.14
- decorator 5.2.1
- exceptiongroup 1.3.0
- executing 2.2.0
- fonttools 4.58.4
- ipykernel 6.29.5
- ipython 8.37.0
- jedi 0.19.2
- joblib 1.5.1
- jupyter-client 8.6.3
- jupyter-core 5.8.1
- kiwisolver 1.4.8
- matplotlib 3.10.3
- matplotlib-inline 0.1.7
- nest-asyncio 1.6.0
- numpy 1.25.0
- packaging 25.0
- pandas 2.0.0
- parso 0.8.4
- pca-football-profiling 0.1.0
- pexpect 4.9.0
- pillow 11.2.1
- platformdirs 4.3.8
- prompt-toolkit 3.0.51
- psutil 7.0.0
- ptyprocess 0.7.0
- pure-eval 0.2.3
- pycparser 2.22
- pygments 2.19.1
- pyparsing 3.2.3
- python-dateutil 2.9.0.post0
- pytz 2025.2
- pywin32 310
- pyzmq 27.0.0
- scikit-learn 1.2.2
- scipy 1.15.3
- seaborn 0.13.2
- six 1.17.0
- stack-data 0.6.3
- threadpoolctl 3.6.0
- tornado 6.5.1
- traitlets 5.14.3
- typing-extensions 4.14.0
- tzdata 2025.2
- wcwidth 0.2.13