It's an interesting criticism and there are competition properties that we've identified that can lead to overly complex models. An obvious one, for instance, i

It's an interesting criticism and there are competition properties that we've identified that can lead to overly complex models. An obvious one, for instance, is running a competition for a very long period of time. A more subtle factor is the degree to which the context of the data has been stripped away - with very little context (just an anonymized feature matrix) it's more likely that victory will go to someone who can squeeze every bit of performance out of an ensemble of ML techniques. But with good context, it's more likely that feature engineering and insight will lead to victory. Many times it is in fact the well-organized, well-engineered solutions that win. And they are often elegant and intuitive because the data scientist could iterate quickly and try out lots of ideas. Though the crazy ensemble does sometimes eek out a victory, it's not an inherent property of competitions by any means. One final note: I'm not sure if this is widely known, but impractical solutions are actually just fine for a certain set of competitions. Some competitions are designed to identify the "data frontier" - the limit of what's possible in a dataset - rather than getting a model into production.