Here are some of our guiding tenets.
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 not a proxy for the real thing 🏀
Analytics let us map what happens in reality to a set of numbers. It gives us a standardized 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 always tell the whole story and should be treated as something to supplement the experience of watching a game, not replace it.
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 empower us to 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.