Welcome to pyTSPA’s documentation!

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) and predictive models using logistic regression.

• Visualization: Graphical representation of match data, statistical summaries, and prediction results using bar charts, scatter plots, and pie charts.

Datasets

The datasets used for testing and demonstration purposes are primarily sourced from public football datasets. For a comprehensive collection of football datasets, visit:

https://www.football-data.co.uk/data.php

Installation

pip project: https://pypi.org/project/pyTSPA-toolbox/

git repository: https://github.com/vargaheni05/pyTSPA-toolbox

For comprehensive installation instructions and usage guidelines, please refer to the documentation: https://pytspa-toolbox.readthedocs.io/en/latest/usage.html

Requirements

Python Requirements:

Python >= 3.10

pandas >= 2.2.2

numpy >= 1.26.4

matplotlib >= 3.9.2

seaborn >= 0.13.2

scikit-learn >= 1.5.2

All the python requirements are installed when the toolbox is installed, so there is no need for any additional commands.

Documentation

https://pytspa-toolbox.readthedocs.io/en/latest/

Contents:

• Usage
  • Installation
  • Example
• Data Handling
  • Load match data
  • Clean match data
  • Data profiling
• Performance Metrics
  • Match result statistics
  • Team performance statistics
  • All teams
  • Statistics for all teams
  • Win percentage
  • Win percentage for all teams
  • Pythagorean expectation
  • Pythagorean expectation for all teams
  • Logistic regression prediction
  • Match outcome prediction
  • Season half prediction
• Visualization Module
  • Result distribution plot
  • Team results plot
  • League points table
  • Goal difference distribution
  • Win percentage comparison
  • Pythagorean expectation plot

Tutorials:

• pyTSPA Example Usage

Correspondence

Henrietta Varga PPCU-ITK, Budapest, Hungary

varga.henrietta.julianna@hallgato.ppke.hu

Marcell Szögi PPCU-ITK, Budapest, Hungary

szogi.marcell@hallgato.ppke.hu

Benedek Kardos PPCU-ITK, Budapest, Hungary

kardos.benedek.zoltan@hallgato.ppke.hu