Context & Goal
When choosing a dataset, I decided to go with football (soccer) data because it combines my passion with a great opportunity to analyze real performance questions. This dataset covers matches from 2019-2025, including match statistics, betting odds, and ELO ratings (team strength). My goal was to get to the bottom of how shots, goals, and team strength interact to shape a match’s outcome.
Approach & Methods
• Forecasting offensive trends (shots vs goals).
• Scatter plots analyzing shots and performance.
• ELO rating distribution and team strength.
• Quadrant analysis of volume vs. accuracy.
• Conclusion with key takeaways.
Key Findings
• Shots per match have steadily increased, showing a shift toward more attacking playstyles.
• Goals have remained stable; efficiency and finishing are still more important than volume of shots.
• Stronger teams (higher ELO) consistently outperform weaker ones.
Reflection & My Next Steps
• This project was very helpful to my development as a data analyst. It strengthened my ability to tell a clear story with data, using Tableau to move beyond just charts and create a narrative with important context, findings, and takeaways.
• I learned the importance of design consistency (colors, fonts, layout, etc.) when building dashboards, this makes insights easir to understand and more professional.
• The main limitation of this dataset was that it focused on match-level statistics, this means that I wasn’t able to explore deeper player patterns. Given a more granular dataset, I could perform a richer, more in-depth analysis.
• Beyond this project, my next step is completing the Google Data Analytics Professional Certificate, with the capstone project being next in line. This upcoming project will allow me to apply the storytelling knowledge I acquired to a business case study while also demonstrating Excel, SQL, R, and structured analytics methods.
Interactive Dashboard
