In this post we use a simple formula described by [Michael Mauboussin](https://twitter.com/mjmauboussin) in his book, [The Success Equation](http://success-equation.com/) to calculate the role of luck in [AFL](https://en.wikipedia.org/wiki/Australian_Football_League) matches, and make some comparisons to other sports.
If you work in data/statistics/DS/ML/AI and are active on LinkedIn you've probably seen this image. It typically recieves a large number of positive comments. This makes sense, data by itself isn't informative without a layer of interpretation. However, without a valid model of the world, 'telling a story from data' typically results in an erroneous post-hoc rationalisation of events obtained from noisy, incomplete data that is not representative of the wider population
[Zillow](https://www.zillow.com/) announced it would use 'algorithms' to identify cheap properties that it could later flip for a handsome profit. Sounds obvious enough right? Surely with a big-data advantage, they will be able to do better in the housing market than the average punter? Turns out they couldn't.
Typical bandits are only capable of adapting to new data that are being observed, they are not 'really' adapting to changes in the environment. Bandit methods typically assume stationarity and that the environment or underlying phenomenon we are trying to understand doesn't change in time!
Some of the most popular colour maps are difficult to interpret correctly and are terribly misleading. We demonstrate some of these issues with some simple examples and suggest superior alternatives.