Interactive Football Scouting Web App for BotolaPro Moroccan League using Streamlit

The top teams in the English Premier League in the 2021-22 season, Manchester City and Liverpool FC, have gone ahead during this off season to add classic NO.9s to their already stellar attack options. Let's see how these two player stack up against each order using radar plot. Data source is FBref via Statsbomb.

Basic comparison pizza chart. To compare two players The PFA released their Team of the season for the 2021-22 Premier League season. Thiago Alcântara of Liverpool FC was included in the midfield three. Many fans questioned the PFA's decision for the inclusion ahead of the likes of Rodri of Manchester City and Declan Rice of Westham United. Let…

Mo Salah had an exceptional season winning the PFA awards, Golden boot and finished with 13 assists making him assist king of 2021-22 season. This is his shotmap

Discord bot with some fun functions

⚽️ Unveiling the FIFA World Cup 2022 Final! 🏆 Explore the epic clash between Argentina and France through data-driven visualizations. From shot maps to passing networks and heatmaps, this project combines football excitement with advanced analytics to reveal tactical insights and key moments from one of the greatest matches in history!

Built a detailed analysis of Manchester City F.C.'s Striker Erling Haaland's shots taken data in their Treble winning 2022-23 season in the premier league using Python, pandas, and mplsoccer. Visualized shot locations, xG, scoring patterns, and finishing efficiency with a custom-designed pitch map and insightful performance metrics.

Data Analysis Concepts + Python

This project visualizes the pass network from Barcelona's 2020 match against Valladolid. It processes match event data, focusing on successful passes before the first substitution. Players are represented as nodes, with arrows indicating passes. The visualization highlights team interactions and passing dynamics on the field.

Learning and exploring data analysis through real-world datasets using Python and StatsBomb APIs and mlpsoccer library

This repository is a collection of data visualizations that I made regarding the performance of football players in Europe

Data Visualization of FC Barcelona's Attacking Trio

This project uses the mplsoccer library to create a visual representation of a futsal pitch for use in sports data analysis, utilizing event data.

This is repository, in which I'll keep my solutions from the Soccermatics course, available here -->

Visualization of the performance metrics gathered in Unity using the Performance Testing Extension

FC Barcelona Reports: An interactive web application to analyze and visualize FC Barcelona's match data. Built with Streamlit, it scrapes match data from WhoScored, stores it in MongoDB, and presents insights through interactive visualizations like pass networks, shot maps, and player statistics.

"Interactive Euro 2024 Final Tactical Dashboard built with Python. Visualizes StatsBomb data (Pass Maps, Heatmaps, Carries) to analyze player performance and team structures, featuring a Yamal vs. Saka head-to-head tactical comparison."

"A data-driven tactical deep dive into Leicester City’s championship DNA. Analyzing N'Golo Kanté’s defensive resilience and Riyad Mahrez’s creative efficiency during their iconic 2016 clash vs. Bournemouth using StatsBomb data."

Football Analytics is a project that collects, analyzes, and visualizes performance data for football teams and players during the Serie A 2017/18 season, using database structures and machine learning models to provide insights into match events and player actions.

This project involves representing a blind football field, inspired by mpl.soccer. This field is similar to a futsal court, with specific adaptations for the sport. The repository includes a dataset for this sport and three examples of map generation.