How to handle multiple datasets of sequential data
Generally, to perform machine learning all data needs to be in a single dataframe. Team name, better yet team id, should be the primary key. Then modeling each year requires selecting the relevant columns (e.g., wins_year_1, loses_year_1, …). Finally, the general model would select all the columns. 

It is best practice to transform all data before modeling. This avoids making transformation errors on some of the data which can lead to errors in modeling.

Pandas support this type of combining through the merge command.

Typically, this data is not called sequential data. Sequential data implies a notion of state. The state changes values over time. In most sports, recent performance is not a strong indicator of how likely a team will the next game. This is often referred to as the Hot Hand Fallacy. Thus, current "win/lose" state of a team is not a strong feature in modeling.