I have data from around 1000 football matches and I want to apply machine learning algorithms and see which one has better accuracy and how I can improve it.
Certain statistics have been gathered from each football match since 2010 and my data set looks like in the picture attached below. Some features are: Home Yellow Cards,Away Yellow Cards,Home Attempts on target,Away attempts on target etc.
I want to output the column "TW" which stands for Team Won. It can have the value Draw,Home or Away (I know I have to remove chars and strings from the data features and replace them with numbers but that's not the problem for now). Therefore, this a multi-class classification problem.
But the real question here is if I want to predict a football match which is happening for example in 5 days from now, how can I input all those features before the match which are unknown? In particular, let's say team X is playing against team Y and I want to output the label "TW". I cannot know how many passes team X is going to complete or how many yellow cards they are going to have. I would still need to input all those features shown in the picture's sample right? Or there is any specific ML technique that will allow me to input just "HT" and "AT" meaning Home Team and Away Team respectively and give me the prediction of "TW" for this??
Is this case what is called "multi-output classification"?
I'm fairly new in Machine Learning and I hope this makes sense and my question is clear. Any help would be much appreciated!
Thanks in advance.