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Uncertainty and randomness are absolutely things that models take into consideration. That's the statistical part of statistical modeling. If unpredictability were a deal breaker, we wouldn't be able to model coin flips.

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Yes, even I use random forest for some of the predictions at my new project. Somehow I feel doing statistical modeling for the sake of modeling/predictions is not the right approach. I'm still new (year or two exp.) into ML/statistical modeling so I'm much more conservative in my approach.

Predicting things and forecasting is an enormous part of ML and stats. Especially in the more statistical side of the community, there are very principled effective ways to do this, even in the presence of weird outliers like the germany/brazil meltdown.

Take a look at robust regression and influence functions to see some of the interesting flavor of one way to look at outlier weirdness.

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