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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.
  • 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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