> The second is that it's also common for the top places to game the structure of the data or overfit to the particular data or training set. And somewhat ofte

> The second is that it's also common for the top places to game the structure of the data or overfit to the particular data or training set. And somewhat often, the datasets for those competitions are f* up because having clean, unbiased, historical data is uncommon in organizations. Or they mess up while anonymizing the data. Other point about competitions is that you don't need to deal with things like concept drift, implementation complexity, resource/latency constraints for predictions and a lot of other things that people applying ML in practice have to face. More often than not, these factors dominate having a 1% or 0.1% better model - unless you're like Google or Facebook.