Maven World Cup Challenge Winning Report

This project combined my love for data, visuals, and football all in one. It was a ton of fun to work on and I learned a great deal along the way. I thought I would break down my process for getting this together in case anyone was curious!

I spent a ton of time in the model and with the data. It wasn't particularly complex, but there were a lot of manipulations required to make them play nicely with the DAX formulas that drove the final visuals. You generally want to spend more time in the model than with the visuals, though, so I guess this forced me to get comfortable with the data and find insights during the build process.

The main measures that drove the report were based on the win and loss rates. I also used the CALCULATE function to write measures that filtered by the number of matches played (e.g., win record with a minimum of twenty matches played in the 2020s). This allowed me to filter outliers that were driven by small sample sizes and focus on the data that I felt more confident in.

Matches that went to penalties are considered 'draws' in this dataset. While this is the correct, stricter definition, I chose to adjust the data to reflect the reality that when a match is decided in penalties, it has the same effect on the team as a win earlier in the game. For example, France and Argentina going to penalties in the 2022 world cup final would have been considered a draw by the original definition, but we all know who won that one ;).

Before I wrote the measures, I started to think about what questions I would have of a world cup contender and built the report to answer those questions. Going from the top left chart downwards I wanted to show:

1. How has this team performed recently compared to the group it's currently in?
2. How does the team compare historically against all other competition?

I then thought about what challenges they might face and how far they might go based on their historical performances and their current state. From the top right chart:

1. Who do they lose to the most?
2. Who do they have the best record against?
3. How do they do against the best teams?

I wrote the measures and built the charts in and out of step with the rest of the process. Some didn’t change at all since the beginning, while others were added on the day of the submission. It was a messy process that left the cutting room floor full of charts and measures that just didn’t add enough to be included. I also designed tool tips, none of which made it to the final submission as it was a static image.

For the design, I focused on getting the point across to the reader. I used conditional formatting in Power BI to highlight what I was emphasizing and push everything else to the background. Anything not highlighted is primarily there for context, so the reader can skim over anything not highlighted and get to the primary point. I then added some more qualitative information in the 'Story Lines' section. This was all typed out about 40 minutes before I submitted the report. I would have sent it without that section, but it felt incomplete without mentioning some of those non-quant factors.

I was running out of time in the end and had to simplify the design. I had wanted to include some more interesting shadows and gradients, but I knew it would have taken too long so I cut down on the visual design. I designed everything with standard Power BI visuals except for the map of Argentina with the players- I used Photoshop and Illustrator for that. Funnily enough, I had wanted the players to be in full colour, but I barely know how to use Photoshop and had no idea how to set the mask up. I ended up keeping them in the 'Blue Scale' because it worked best with what I was able to do, and it turned out ok, I guess. The card backgrounds were just rectangles with shadows, grouped if they were related or telling the same story.