Year-End Top 3 – ATP Men’s Tennis Rankings
Idea & Approach
The provided dataset contained information about individuals considered to be among the absolute elite in their respective sports.
The first Wimbledon final I remember watching live was probably in 1974 or 1975. I’m certain about the women’s final in 1977, featuring the Dutch player Betty Stöve. She reached not only the women’s singles final but also the women’s doubles and mixed doubles finals — and then lost all three 🙁. That was also the last year our family went on holiday to Dishoek in Zeeland.
Dataset
Looking at the dataset, I decided to focus on tennis players and wanted to create a compelling visualization showing the dynamics within the year-end top 3. I consider myself reasonably knowledgeable about tennis, and after creating a line visualization based on the existing data, two things stood out:
- The players included seemed somewhat arbitrary — it wasn’t possible to compile a consistent top 3 for every year, and Agassi was missing.
- To properly illustrate the dominance of Federer, Nadal, and Djokovic, it would make sense to extend the timeline through 2025, while the dataset only covered data up to 2018.
I also thought it would be nice to include an information card for each player with a photo, a fun fact, and a short summary of their achievements.
Data Enrichment
The missing information was filled in as follows:
- I looked up the missing top 3 players — AI provided initial answers, which I then verified on the official ATP website, as there had been some hallucinations.
- The images used are official ATP headshots, which I made semi-transparent in the demo. The exact copyright status is unclear. Using random free images found online would have resulted in an inconsistent presentation.
- The fun facts and summaries of achievements were generated using AI. I did verify the summaries manually, as there were occasional inaccuracies.
This hands-on effort reflects my preference for putting in a bit more time to achieve a result I’m satisfied with — and also explains why I decided to skip the women’s (WTA) dataset.
Result
In the final result, it’s possible to select different players and view their performance over time on the timeline. Each selected player is shown on a card containing key information, with the card’s border matching the color of that player’s performance line.
When you hover over a data point (dot) on the chart, you can see which player it represents — even if that player hasn’t been selected.
Example Includes
- A timeline displaying the performance lines of the selected players.
- The visualization is configured so that the data points are connected by smooth lines.
- The timeline only shows positions 1, 2, and 3. For years in which a player ranked outside the top 3, the underlying value was pragmatically set to 4 — ensuring the line remains continuous. Position 4 is also drawn but uses the same color as the background, effectively hiding it. You could calculate this differently, but this was the practical approach.
Figure Friday is an initiative by the Dash/Plotly community, where each Friday a new dataset is shared and participants create a visualization or small app to extract insights from it. The following Friday at 18:00, there’s a Zoom session where some participants explain the thought process behind their work. In the community thread, people also share their code — and when possible, a live demo — to learn from each other.
