Football Data Analysis Repository

This repository contains various codes and tutorials focused on football event data analysis, using Python and popular data science libraries. Whether you're a data scientist, analyst, or football enthusiast, this repository will help you manipulate, analyze, and visualize football data effectively.

Contents

📊 From Data to Social Media

This notebook is part of the QuantStats Data Analysis in Football Course
🎯 Objective: Learn how to turn raw event data into meaningful, shareable visualizations for social media.

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💡 What You'll Learn

• How to filter and prepare football event data
• How to create captivating visualizations with mplsoccer
• How to highlight key actions like defensive events or goals
• How to export and save your visuals
• ⚠️ Final Challenge: Build your own defensive heatmap for Ali Abdi!

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🏆 Challenge

📌 Create a heatmap of Ali Abdi’s defensive actions and post your result on LinkedIn with the hashtag
#BentelliChallenge and tag me (@Sara Bentelli) — I’ll feature selected works!

Need help? Check the notebook for guidance and examples.

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📁 Files Included

• From_Data_to_Social_Media.ipynb: The full tutorial notebook used in the live demo session
• ali_abdi.csv: The dataset for the visualizations
• From_Data_to_Social_Media.pdf: The PDF guide that explains the methodology

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🛠 Libraries Used

• pandas
• mplsoccer
• matplotlib
• seaborn
• highlight_text
• FontManager, PIL, cmasher...

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🚀 Let's Build Together

Don’t hesitate to fork, play with the code, and share your creations.
This is just the beginning — bring your own creativity, and let’s push the limits of football analysis together!

1️⃣ Data Manipulation Techniques

• Concatenation & Splitting: Efficient methods for merging and breaking down large datasets.
• Grouping & Filtering: Advanced techniques to group data and retrieve meaningful subsets, crucial for focusing on specific players, teams, or match events.

2️⃣ Visualization of Event Data (Heatmap Styles)

📌 Tutorial: "Different Heatmap Styles for Football Event Data"

• Kernel Density Estimation (KDE): Visualizing player activity areas on the pitch with smooth density plots.
• Hexbin Plot: A grid-based heatmap providing detailed spatial insights.
• Custom Colormaps: Explore how to create visually appealing colormaps for better data storytelling.

👉 Explore the tutorial here: Heatmap Styles Notebook

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4️⃣ Convex Hull Styles for Pass Clustering

📌 New Tutorial: "Using Convex Hulls to Analyze Passing Structures"

• Implements Convex Hull visualization to outline passing clusters.
• Helps in understanding positional structures and the spread of passes within clusters.
• Applied to Final Third Entry Passes, highlighting passing lanes and patterns.

📷 Check out the implementation here.

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3️⃣ Clustering Passes by Corridor

📌 New Tutorial: "Clustering Final Third Entry Passes in Football"
This tutorial focuses on segmenting passes into three vertical corridors (Left, Center, Right) and applying K-Means clustering to identify passing patterns.

• 🔹 Filters Argentina's passes that start in their own half and enter the final third.
• 🔹 Uses K-Means clustering to classify passes into different clusters within each corridor.
• 🔹 Visualizes passing trends with color-coded clusters and structured pitch separation.

📷 See the visualization in action and explore the code here.

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🎯 How to Use goal-plot to Visualize Penalty Shots

goal-plot is my first Python package – simple and easy to use! It lets you draw a clean 2D goal and plot events like penalties on it.

This tutorial shows you how to visualize penalty shots using your own data.

👉 Explore the tutorial here: draw_goal Notebook

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🧩 Installation

pip install goal-plot

🚀 How to Use This Repository?

Simply clone the repository and explore the Jupyter notebooks:

git clone https://github.com/sara1621/Football-Data-Analysis.git
cd Football-Data-Analysis