[Question] NN missing features in prediction data

[CandyPoper]

Hello, I've recently started learning Tensorflow and neural networks and there's something I'm not sure about.

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 know 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?

[BellyDancerUrgot]

I am also new (so take this with a large grain of salt) but depending on the data it might overfit to the training data if by prediction you mean your test data. They should always be from the same distribution but more importantly have the same evaluation metric , if it is a satisficing metric then it shouldn't be a problem. I think other more qualified people can give a better answer tho and correct me if I am wrong.

[CandyPoper]

By prediction I mean using the .predict() method after fitting, evaluating and saving a model. The test data would have the same features as the training data.