As part of Dashboard Week at Data School, I took on the challenge of creating a performance analysis dashboard using player recovery data. My initial plan was to compare multiple players’ recovery metrics across matches and training days in order to identify patterns. However, once I dove into the data, I quickly realised it only contained recovery stats for a single player. That forced a quick pivot in direction. Instead, I decided to focus solely on this player’s recovery trends over time.
I wish I’d invested more time in data prep upfront. I hadn’t connected the recovery metrics with actual match dates, which made it difficult to identify meaningful trends or contextual spikes.
In hindsight, I should have spent the first hour mapping out what the data could realistically tell me and identifying where other datasets (like match schedules) were needed. Even a simple data dictionary or date table would’ve highlighted these issues earlier on.
As a result, I spent about an hour trying to write a dynamic calculation that return the last 5 days for which data was recorded for each metric. This was definitely time I could have spent elsewhere had I realised the issue at hand earlier. After this, I moved on to building out the charts and incorporating filters and metric switches before aligning the dashboard lay out
As a proof of concept, I still think the dashboard came together reasonably well. It allowed me to visualise the recovery metrics I had, and the interactivity made it easy to switch between different weeks and seasons. That said, one feature I would’ve loved to include — and that I think would’ve added real context — was a legend or marker system indicating pre-match, post-match, and actual match days on the timeline. This would have helped frame the recovery data more clearly against the demands of the player’s schedule. Unfortunately, with time running out, I wasn’t able to implement it, but it’s definitely something I’d prioritise in future.
I also experimented with starting a second dashboard on physical performance metrics, but time was tight. Arguably, the time would have been better spent adding the finishing touches to the player recovery dashboard.
Reflecting on the whole process, I now realise the biggest miss was not crafting a clear user story from the start. Without a focused question or purpose, my final dashboard — while visually decent — lacked a strong narrative and direction when it came to presentation.
Key takeaway: always begin with a clear user story and ensure the data prep phase sets up your analysis for success.
You can find the dashboard by clicking on the link below:
https://app.powerbi.com/groups/fb53ac9e-6f3b-41ca-8525-f3d41ff26326/reports/cebee2df-62cb-4324-9d96-042e3931e557/b4e607b672001ae786e6?experience=power-bi