...and because we had a lot of free time during the early days of the pandemic!
We've since made visualizations across a variety of topics.
Here are our original guiding tenets:
Data visualization should show, not tell
Many of the pioneers of data visualization tied the effectiveness of a graphic to its ability to accurately convey data. We think that's only part of it. Good data visualization should also inspire, captivate, and delight. Humans have a natural capacity for seeing trends and understanding causal relationships. On the other hand, we're terrible with lots of big numbers. Data visualization should help us with what we're bad at and let us do what we're good at.
Models should be fundamentally useful
A weather forecast that's wrong most of the time is not a useful tool, regardless of how original and groundbreaking the underlying methodology is.
Tools should be comprehensible, transparent, and flexible
One problem with modern sports analytics is that they're just not simple to understand. A lot of advanced statistics are proprietary, meaning that only the creators know how they really work. You shouldn't need to use a black box, or have an advanced degree in mathematics, to enjoy and draw conclusions from the awesome amounts of sports data available out there.
Analytics are a supplement, not a replacement
Analytics let us map what happens in reality to a set of numbers. It gives us a standard way to compare things, do really cool math to draw insights, and create visualizations to share those insights. There is, however, no replacement for watching a game. Analytics don't tell the whole story and should be treated as something to enhance the experience of watching a game, not replace it.