This repository will contain all sorts of basic visualisation to step into the world of football analytics

11/09/2020 - Complete directory for Pundits Review web application. https://www.punditsreview.com/

Development of the scraping process used to collect data for the Pundits Review website - https://www.punditsreview.com/

A Data Analysis/Visualisation project on Analysis of FC Barcelone with the help of free datasets available in statsbomb, using Python.

Useful resources created alongside the Pundits Review website - https://www.punditsreview.com/

This repo contains football analytics scripts for the 2021 NFL Big Data Bowl.

Beautiful visualizations of league tables using Bezier Curves.

MCHEZOPESA FOOTBALL PREDICTION: A prediction result of a game between team 1 and team 2, based on who's home and who's away, and on whether or not the game is friendly

Poland football league Ekstraklasa statistical analysis. Season 2020/2021

A python package that is a wrapper for Plotly to generate football tracking and event data plots

This contains the analysis of football players and teams. It also has Tableau Workbooks of some of the player analysis. If you cannot access the workbooks, check out the link below.

The written report and jupyter notebook (HTML format) for the investigate a data set project. The selected database is the European Soccer Database hosted on Kaggle.

A couple of functions to create customized passing networks with event data by Statsbomb and tracking data by Metrica Sport

Jupyter notebooks of the github.com/Friends-of-Tracking-FoTD/LaurieOnTracking

Flatiron School Capstone project. Trying to find out how well players' on-field performance metrics can be used to predict their transfer values.

Football analysis using python to solve the problem statement: Find Out the Best Replacement for Harry Kane at Tottenham?

A Data Visualization Project that shows all of Lionel Messi's Shots and Goals under different managers during his time at Barcelona.

Collection of references, datasets and software libraries for soccer analytics.

This project aims to rate football players using data and statistics recorded from the last match they participated in. Much of the code included in this project can be used for other purposes when working with the Wyscout data set. For example minutes_played.py, fitting_functions.py and KPI_functions.py.