New to data science and I am working on a personal project in sports analytics. I have data in the form of one dataset for each year/season.
Each dataset has the team's wins, losses, and various offensive and defensive stats.
These will be
pandas.DataFrames indexed by team name.
My goal is to predict wins and losses based on this data for each year, and then create a more generalized model to predict next season using all of the previous years data.
I am having difficulty deciding how best to combine the data. I think that it would be very inefficient to combine all of the datasets into 1 and have variables for each stat in the year prior, 2 years prior, etc. How is this type of sequential data usually handled?