So, I am currently learning machine learning and data analysis. I have created for my self a problem that is:
Who will win a match of soccer
Now, I have narrowed it down as being a
Binary Classification problem as I only want to figure out who will be the winner of a match.
For this I have some data containing the following data points:
- Team One
- Team Two
- Goal (Team One)
- Goal (Team Two)
Now, this is where the trouble begins (for me at least) I am unsure which features to choose for my model and also kind of off as to how to manage the data.
Say that I have two teams playing (Liverpool vs Chelsea) now I do have around 5000 data points for all matches played however only around 82 points where it is Liverpool against Chelsea. Which dataset should I use?
Also sometimes their position in the dataset changes meaning that sometimes Liverpool is
Team One and sometimes Chelsea is
Team One does this matter or should I process the dataset to always match one team at a certain position?
In general, what is the best way to train my model? Should I use the small dataset containing only the matches between these two teams or should I go for all matches in general?
I am sorry for the beginner question I really hope someone can help me out :)