An Old Video I Found

Title: Rediscovering an Interactive Sports Science Visualization after 3 Years



Have you ever stumbled upon something you created in the past, completely forgetting about its existence? That’s precisely what happened to me recently when I rediscovered a video I made three years ago—an interactive visualization showcasing some fake sports science data. In this blog post, I want to share the story behind this discovery, highlight the features of the video, and delve into the technical aspects of its creation using the Bokeh package in Python.

The Video:

The video starts with a friendly greeting and an explanation of its purpose—to create a sports science visualization. It features a scatter plot utilizing the Bokeh package, a powerful data visualization library in Python. The scatter plot offers various interactive functionalities, allowing the viewer to explore different aspects of the data.

Exploring the Visualization:

The scatter plot offers flexibility by enabling the viewer to change the x- and y-axis, such as comparing minutes per game against player weight. It also allows for comparisons between player age and weight or maximum velocity. Although the data used in the visualization is fictional, it provides a clear demonstration of the interactive capabilities.

Furthermore, the video showcases additional customization options, such as coloring the plot based on player team and adjusting the circle size according to average miles per hour. Hovering over data points provides detailed information about each player, and zooming in allows for closer inspection of specific points of interest. Additionally, a reset function is available to restore the original view.

Technical Details:

The video briefly touches on the underlying code responsible for creating the visualization. The Bokeh package in Python plays a central role in this process, offering a range of tools and features for building interactive visualizations. The code allows for hover interactions, enabling the viewer to obtain detailed information about individual data points.

Future Possibilities:

During the video, some potential next steps for this project are mentioned. These include integrating the visualization into a website and incorporating real-time data from Catapult sensors to analyze player performance. The possibility of leveraging second spectrum data to track league-wide trends in speed, force, and acceleration is also discussed.


Rediscovering this interactive sports science visualization after three years was an exciting experience. It reminded me of the possibilities offered by the Bokeh package in Python for creating engaging and interactive data visualizations. As I reflect on the video, I am intrigued by the potential applications and future developments that could further enhance this project.

I’m grateful for the opportunity to have worked on this visualization and look forward to exploring more substantial projects in the field of data visualization. If you have any suggestions or ideas for collaboration, please feel free to reach out. Thank you for joining me on this journey of rediscovery and exploration.

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