How to predict an outcome of the game (next row) based on all previous games (rows)? I'm a data science student and I've come across a fairly unusual dataset (to me, which explains the vague title).
It's of the following form:




STAT_1
STAT_2
...
HOME
AWAY
NEXT_HOME
NEXT_AWAY
NEXT_RESULT




15
11
...
Team A
Team B
Team C
Team D
1


11
18
...
Team C
Team D
Team E
Team F
0


...
...
...
...
...
...
...
...


10
11
...
Team W
Team X
Team Y
Team Z
1




Basically, the rows represent the statistics of the "current" match and at the end of the row the columns NEXT_HOME and NEXT_AWAY represent the teams which are playing the next game (next row) and the result of that next game is stored in NEXT_WINNER.
The test data is of the same form but without the NEXT_WINNER column because that's the target variable.
This is unusual to me because essentially I need to predict the outcome of the next match based on all previous matches using a machine learning model of my choice.
I've never done anything like this so I would appreciate some guidance (not solutions, just advice and where to start). Something of the sort of how to transform the dataset to make it more manageable. I'm guessing I should somehow store stats for each team and do something with it but I'm unsure.
Thanks.

I would create some variables representing the history of games. For example, a variable could be number of matches that were played before this match. Percentage of home wins up until this match. You could use frequency of win for each team as a variable and add them as separate columns for each home and away. I would start from here, develop models, and see which of these contributed to it.
