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?