Showcasing Football Data Visualizations

This project highlights my ability to interpret and present football data through detailed visualizations, including passing networks, shot maps, and expected goals (xG) analyses. By using advanced analytics tools, these visualizations provide valuable insights into team strategies, player performance, and key areas for improvement.

UEFA Women’s EURO 2022 Final Passing Map

• This image presents the passing networks of England and Germany during the UEFA Women’s EURO 2022 Final. The visualization highlights how both teams structured their play:
• England’s Passing Network: Displays a dense network with frequent connections between key players like Williamson, Walsh, and Hemp, indicating a cohesive and central passing strategy.
• Germany’s Passing Network: Shows a more dispersed network, with connections among players like Rauch, Däbritz, and Hegering, suggesting a strategy focusing on wing play and wider distribution.

Shot/Goal Map

The shot and goal map are an integral part of the probability of scoring visualization. It details the locations of all shots taken during a match, differentiating between those that resulted in goals and those that did not. This map is essential for analyzing shooting efficiency and identifying key areas where teams can improve their finishing.

Probability of Scoring

This heatmap illustrates the probability of scoring from different areas on the pitch. The darker regions near the goal indicate higher scoring probabilities, emphasizing the importance of close-range opportunities. This visualization helps in understanding the most effective shooting positions and areas to focus on for both defensive and offensive strategies.

StatsBomb Freeze Frame

• This freeze frame visualizes key moments in a match, highlighting the positions of players during a critical event, such as a goal or a defensive maneuver:
• Red Dots: Represent players of one team.
• Blue Dots: Represent players of the opposing team.
• The lines and their thickness denote the direction and strength of a pass or movement, providing insight into the tactical decisions made during the match.

xG Shot Map Using StatsBomb Data

This visualization employs StatsBomb data to map expected goals (xG) for each shot taken in a match. The xG value estimates the likelihood of a shot resulting in a goal based on factors like shot location, angle, and type. This map helps in evaluating player performance and team strategy, highlighting high probability scoring opportunities and areas needing tactical adjustments.

Conclusion

These visualizations demonstrate my ability to use advanced football analytics tools to create meaningful and insightful representations of match data. Whether it’s analyzing passing networks, goal probabilities, shot maps, or expected goals, these skills are essential for enhancing understanding and strategy in football analysis.