As a final exam for the Data Analysis session at EM Lyon Business School, my role was to assist the PSG scouting team in making decisions for the next transfer window. Multiple datasets about players (FIFA ratings, clubs, contract expiration, etc.) were provided in order to process them in the clearest and most coherent way for the scouting team. We had to create one dashboard comparing positions and a second one comparing players. The final goal was to create the perfect tool for them, but also to recommend a few interesting profiles. Some of the data provided were not 100% reliable (e.g., season stats generated by AI, incomplete player lists, etc.).
The scouting team works with large volumes of data and wants clear information that matches their criteria and PSG’s strategic sporting direction: continuing to build a young team with versatile players who are technically strong. My goal was to transform these large Excel datasets into a clear dashboard with key information that would be readable, easily comparable, and relevant.
In the first dashboard, the goal was to provide an overview of performance, age, and market value, helping identify high-potential profiles aligned with PSG’s long-term strategy. An age filter allows the selection of young players if needed (matching PSG’s recruitment philosophy), while contract information highlights market opportunities ahead of upcoming transfer windows. After identifying interesting market opportunities in the first dashboard, the scouting team can go deeper using the second dashboard. This second dashboard is designed to deep dive into a single player profile, allowing scouts to move from market and positional exploration to individual evaluation. It provides a clear understanding of who the player is, with a structured analysis of performance, value, and contract situation. As we did not have detailed performance data, I also used FIFA ratings to create a spider chart, providing an overview of each player’s skills and technical profile. The market value evolution shows whether the player’s value is increasing, and I calculated success rates in shots, passes, and tackles to assess technical consistency. For example, in PSG’s core playing style, passing success rate is a key metric.
It was really interesting to select and structure data in alignment with the club’s strategy and criteria, while also considering the scouting team’s need for comfort and readability. What made the project slightly less relevant was the fact that the data were incomplete and not fully reliable, but it allowed me to develop strong logic, methodology, and a strategic approach to prioritizing and designing data. Regarding the tool, even though I discovered Power BI only a few weeks before the exam, I was highly motivated to learn more and aim to become highly proficient with it.
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