I want to create a NHL game predictor and have already trained one neural network on game data.
What I would like to do is train another model on player seasonal/game data and combine the two models to archive better accuracy.
Is this approach feasible? If it is, how do I go about doing it?
EDIT:
I have currently trained a neural network to classify the probability of the home team winning a game on a dataset that looks like this:
h_Won/Lost h_metric2 h_metric3 h_metric4 a_metric2 a_metric3 a_metric4 h_team1 h_team2 h_team3 h_team4 a_team1 a_team2 a_team3 a_team4
1 10 10 10 10 10 10 1 0 0 0 0 1 0 0
1 10 10 10 10 10 10 1 0 0 0 0 1 0 0
1 10 10 10 10 10 10 1 0 0 0 0 1 0 0
and so on.
I am preparing a dataset of player-data for each game that will have the shape of this:
Player PlayerID Won/Lost team opponent metric1 metric2
Henke 1 1 NY CAP 10 10
Hopefully, this new dataset will have some accuracy on if team is going to have some predictive features that are good and recognised.
Now, say I have these two trained Nural Networks and they both have an accuracy of 70% by them self. But I want to combine them both in the hopes to achieve better predictability. How is this archived? How will the test-dataset be structured?