English Women’s Football League Analysis
During our dashboard week with the English Women’s Football (EWF) Database, I focused on analyzing factors driving stadium attendance and identifying strong-performing teams from the second division (Championship) that earned promotion to the first division (Super League).
The dashboard features a line chart displaying yearly data on total and average attendance from 2011 to the 2023-24 season, alongside the number of matches per season. Attendance saw a significant rise from 2020, not only due to league expansion but also because big teams began hosting rivalry matches in larger stadiums.
Increasing Attendance with Big teams
One highlight with the dataset was the number of attendance increased with better performance of teams. For instance, Arsenal has been performing quite good for the last 10 years and showed the highest number in the attendance. On the other hand, Manchester United showed relatively higher number in the Average attendance because they held the matches in the bigger stadium more than other teams.
Rival Matches in Bigger Stadiums
Holding women’s games in the bigger stadium is financially risky for the clubs. But when there is chance such as big games against their rivals, people are ready to come and watch.
Most matches with the highest attendance on Sundays, Average Peaks on Fridays
Although most games were held on Sundays, Friday had the highest average attendance. Clubs could consider scheduling more big matches on Fridays if feasible.
Rise of Aston Villa Women
Aston Villa stayed in the Championship from 2014 until earning promotion to the Super League in the 2019-20 season, while Birmingham City, in contrast, dropped to the Championship in 2018-19. At the same time, Chelsea and Arsenal have dominated the Super league for the past decade, consistently competing at the highest level.
If you’re interested, check out my dashboard at the link below:
Author: Don Kang
Experienced Engineer with a diverse background in academic research and engineering seeking for a career transition to data and AI engineer. Skilled in Python, SQL, Alteryx, Tableau, Power BI and cloud platforms such as, GCP, AWS and AZURE. Detail-oriented and quick to adapt to evolving business needs and industry trends, with a passion for leveraging data to drive decision making and innovation.