The Data For This Dashboard
The second dataset for Dashboard Week centers around the English Women’s Football League. The data contains information around the different matches teams competed in, along with their final standing at the end of each football season. Luckily, the data was already cleaned and processed by the person who created the dataset meaning I could jump right into creating my Power Bi report.
Introducing My Topic and First Dashboard
The “question” I wanted to answer with my report was whether or not home field advantage can be seen within data, displaying an aggregated look complimenting a more granular look divided by seasons, divisions and tiers within the league. The measures I’ve used to display home field advantage is the number of wins/losses/draws the teams achieved when they were a home team vs when they were an away team, and the number of goals scored as well.
Insights
Looking at a solely aggregated look, home field advantage can be seen quite strongly, but as you start to drill down to individual seasons or divisions, some examples can be seen in which this trend is not seen. To dive deeper into this, my next dashboard focuses on the teams themselves, to see if any might deviate from the trend as well.
Team Analysis
Insights
This report serves to highlight the number of teams that don’t follow the trend of having home field advantage. Considering teams are thought to be better at their home field, 12 teams display a larger number of away team wins than home team wins.
To serve as extra detail on an individual team bases, I’ve added a tool-tip to display the wins over the seasons to display if teams are consistent over time. The next dashboard follows as a drill through from the right bar chart, going into details for an individual team over the course of their season run.
Drilling Through To Individual Teams
The report finishes with a general look over time (seasons) for the teams, looking at total wins, total goals for, and total goals against. This can be used to see if any interesting trends can be found for the teams. This could help provide some context to the teams who don’t follow the home field advantage trend, to hopefully find some insight into what might explain their deviance from the expected result.
Conclusion
This dataset was very interesting to work with as a whole. I found the question very interesting, but I also found it quite difficult to get meaningful insights to explain this question. Nonetheless, I had a lot of fun making this report and can’t wait for tomorrows dataset.
Thanks for reading, if you would like to check out my dashboard feel free to check it out here.
