Feature selection is not that useful?
My experience is the same. I think in my case at least it's largely down to the algorithms I would generally use, all of which have the capacity to ignore features or down-weight them to insignificance where they're not particularly useful to the model. For example, a Random Forest will simply not select particular features to split against. A neural network will just weight features into having no effect on the output and so on. My experience is that algorithms which take every feature into account (like a vanilla linear regression model) generally suffer far more. 

Additionally, in a "production" rather than competitive environment I found that feature selection became much more important. This is generally due to covariate shift - the distribution of values for certain features changes over time and, where that change is significant between your training dataset and the live predictions you're making day-to-day, this can really trash your model's outputs completely. This kind of problem seems to be administered out of the datasets used for competitions, so I never experienced it until starting to use ML at work.