Understanding Soccer Through Data Science

Data science is revolutionizing the world of sports by uncovering hidden patterns and providing profound insights that enhance performance, strategy, and decision-making. This project, "Understanding Soccer Through Data Science," exemplifies the transformative power of data analytics in sports. By leveraging Graph Neural Networks (GNNs), this project delves deep into the intricate passing dynamics within soccer teams. 

A key innovation of this project is the development of a novel metric called PassNetScore, which aims to contextualize and provide meaningful insights into passing networks—a popular application of graph network theory in soccer. Utilizing the Statsbomb Event Data, which captures every event during a soccer match, including passes, shots, fouls, and substitutions, this project constructs detailed passing network graphs. Each player is represented as a node, and each pass as an edge, creating a comprehensive representation of team interactions on the pitch. The project harnesses the power of Spektral, a Python library for graph deep learning, to build and analyze these graphs. Key node features include players' average positions, total passes and expected threat of passes, while edges encapsulate the passing interactions and pass counts. 

The project explores two distinct models to calculate PassNetScore through predicting match outcomes. The first model is a basic GNN that employs a binary adjacency matrix to represent the presence or absence of passes between players. This model captures the fundamental structure of passing networks, highlighting key players and connections within the team. There are three variations of this model, each building on the binary model by adding new features to nodes or edges. The second model integrates GNN with Long Short-Term Memory (LSTM) networks to account for temporal dependencies in passing sequences. This advanced model provides deeper insights into how passing patterns evolve over time and how these dynamics impact match outcomes. To evaluate the effectiveness of these models, a suite of graph theory metrics is employed. These metrics illuminate the dynamics of team play and the influence of individual players, offering a comprehensive assessment of the PassNet Score metric. 

Through this innovative approach, the project demonstrates the powerful application of GNNs in sports analytics and offers a novel metric for evaluating passing networks based on match outcomes. This project paves the way for new strategies and insights that could revolutionize how teams analyze and improve their gameplay, showcasing the profound impact of data science in sports.