[OC] Hades Star Red Star Event Points scored by first place team Visualization
![[OC] Hades Star Red Star Event Points scored by first place team Visualization](/api/images/reddit-maps/1mu6l7r_1755604803837.jpg)
Data Analysis
What This Visualization Shows
This data visualization displays "[OC] Hades Star Red Star Event Points scored by first place team" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on *Created using python, only library used is pycairo for rendering.*
Explanation:
Hades Star is an MMO that hosts a monthly event where players compete over 48 hours in team to set the highest collective score doing Red Star or Dark Red Star missions (from level 2 to 12)
I am a player in the team that finished first this event, and wrote the bot that collects the team's scores from the game's webhook. (I only have access to my own team's data)
Normally, the team results are presented in a simple ranked table - but I felt given it's meant to be a team effort, having a ranked table didn't convey the 'group effort' nature of the event. Especially as higher leveled players can pull in over 10x the number of points per mission compared to the lower leveled players.
Originally I was going to try implementing a Voronoi based chart, however writing the code to do a (visually interesting) packed bubble chart was easier, but it also gave me the opportunity to turn each bubble into a pie chart showing where the player scored their points.
So, the area of each bubble is proportional to a player's score (IIRC each pixel is worth 1024 / pi points) - I then added the number of missions in the central circle. I removed all players' details for anonymity, except for the top scoring player (who gave permission) to give a sense of scale for the other bubbles.
As a final visual flourish, I added a donut shaped pie chart showing the team's total points distribution (this is just to frame the packed bubble chart, so doesn't match the same points per pixel value as the player bubbles, it is just scaled to frame the bubbles)
The colours are taken from the planet colour for the respective mission level.
As I have all the data in a chronological database I intend to go through make an animated version of the chart where the bubbles grow in relaton to the timeline., which allows us to understand complex relationships and insights within the data through visual storytelling.
Deep Dive into the Topic
This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.
The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.
Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.
Data Analysis and Insights
The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.
Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.
The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.
Significance and Applications
This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.
For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.
The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.
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About the Author

Alex Cartwright
Senior Data Visualization Expert
Alex Cartwright is a renowned data visualization specialist and infographic designer with over 15 years of experience in...