Neural network with missing features in prediction data? Lets say I want to create a model that predicts an outcome of a certain match. I use features to train the network that are known before a match starts (such as individual performance of each player in the last x months, etc.) and some features that are not known beforehand (just data about how the match went, when certain things happened, etc).
Now, if I want to predict a result of an upcoming match (with some features missing, since they are not know before the match starts). Will my NN perform worse since I used features that I don't have at the time of the prediction to train it or would it perform the same/better compared to a model based on only features that are know beforehand.
And if it's alright to have missing data in the prediction set, how do I go about marking certain features as "missing". Do I simply enter a 0 in those columns?

I think it is unlikely that the model will perform better than a network trained using only the features known before the start of the match.  If all of the other set of features is missing when you make the predictions, there is no way for the network to make use of them.
What you might consider doing is making those features additional outputs of the model.  If they are useful attributes, they may represent useful sub-goals for the network, so if it can predict them, it may guide the learning of the hidden layer neurons to provide useful features for predicting the result itself.  That way the additional features can be used to give hints in the learning of the main task, without them needing to be available when predictions are made.
