English Women’s Football Report: Chelsea vs. Arsenal Rivalry Analysis
This is the second dashboard of the week, and this time, the dataset is about English Women’s Football. The task was to build a Power BI report.
As someone who genuinely enjoys sports, especially football, I had a lot of fun working on this report. Despite my interest, finding meaningful insights and crafting a compelling story was challenging.
After careful consideration, I decided to focus on an analysis of the London Rivals: Chelsea vs. Arsenal. The decision was based on the well-known rivalry between these two clubs and the fact that both have been part of the league since its inception, facing each other many times over the years.
Chelsea vs. Arsenal – Games
The overall comparison shows a tightly contested rivalry. However, when broken down by season, a clear pattern emerges: Arsenal dominated the early years, while Chelsea has taken the upper hand in more recent seasons.
Chelsea vs. Arsenal – League Performance
This analysis aimed to determine whether one club has clearly dominated the rivalry over time. The initial comparison focused on head-to-head games, examining which club had more wins and scored more goals.
The league performance analysis revealed a similar pattern. Arsenal had stronger performances in the earlier years of the league, but Chelsea has been the more successful team recently, winning the league in the last five seasons and consistently finishing above Arsenal in the standings over the past decade.
Conclusion
Working on this report was a perfect combination of my passion for sports and data analysis. I truly enjoyed building this dashboard, and it has inspired me to consider exploring more sports-related data projects in the future.
Click to view the report: Chelsea vs Arsenal Report
Author: Augusto Terra
Before joining the Data Schools, Augusto was a PE teacher and Football coach. A few years after enjoying working with data at his master's degree, Augusto decided this was the path that he was willing to pursue. This led him to join The Data School; he is keen to work with different data and find business solutions. He also enjoys being with his friends and playing sports (Football).