I apologize if this is too simple a question for this forum but...
If I'm trying to build a neural network or other machine learning based prediction of who will win a football game and by how many points given such and such inputs do I use the actual score of past games as the correct answer for my neural network for training purposes.
For example if I'm using winning percentage of each team to predict winners. I might have the following:
[Home Team Win Pct] [VisitingvTeam Win Pct] [Home Team Win Margin (points)] 50% 45% +2 50% 75% -6 .... ..... ....
...and so on from past games - I might have 10 years of game data let's say.
Can I use the actual outcome point differential ([Home Team Win Margin]) as the "correct answer" for training purposes? Since I have no way of knowing if these two teams played 100 times under the same exact conditions if the average winning margin would be +2 or some other number since for a given game result I only have a sample size of one. Or is there some other measure of "correct answer" that I should use instead? I certainly can't think of one.
Thanks for your help.