A toolbox for analyzing and visualizing team sports performance
Project description
pyTSPA - Toolbox for Analyzing and Visualizing Team Sports Performance
A toolbox for analyzing and visualizing team sports performance, with a particular focus on football match data.
The toolbox includes modules for data processing, statistical analysis, match outcome prediction, and result visualization.
Current Version: 0.1.1
Planned Release: May 2025
Introduction
pyTSPA was developed to support coaches, analysts, and sports scientists in extracting actionable insights from football match data.
Designed with a football-centric approach, the toolbox facilitates data handling, statistical analysis, match outcome prediction using machine learning, and comprehensive data visualization.
Description
The toolbox is divided into the following modules:
-
Data Handling:
- Data input/output, cleaning, and profiling (CSV, Excel).
-
Metrics:
- Calculation of match and team statistics (
Win Percentage,Pythagorean Expectation) - Predictive models using logistic regression for match outcome prediction.
- Calculation of match and team statistics (
-
Visualization:
- Graphical representation of match data, statistical summaries, and prediction results using bar charts, scatter plots, and pie charts.
For more information, visit the official documentation.
Installation
From PyPi
Install pyTSPA using pip
pip install pyTSPA-toolbox
From source
Clone the git repository
git clone https://github.com/vargaheni05/pyTSPA-toolbox.git
Change directory to the cloned repository
cd pyTSPA-toolbox
Install with pip
pip install .
Building the documentation
Install required packages
pip install -r docs/requirements.txt
Call Sphinx build command
sphinx-build -M html docs/source docs/build
On Windows you can also run the make.bat file
.\docs\make.bat html
The documentation should be available in the docs/build directory as html files
This includes the example codes as tutorials
Correspondence
Henrietta Varga (varga.henrietta.julianna@hallgato.ppke.hu)
Marcell Szögi (szogi.marcell@hallgato.ppke.hu)
Benedek Kardos (kardos.benedek.zoltan@hallgato.ppke.hu)
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