I have a model that takes mostly cumulative data, and makes a prediction. It's not baseball, but I'm using this as a pretty accurate analogy. You put in all the totals so far, and it make a prediction of where a target will be in the future.
games_played|runs|runs_against|hits|hits_against|games_won 20| 100| 120| 200| 240| 10 Expected games won after 5 more games: 12
What I'd like to do is answer questions like "How many runs are we expected to have in N games? And if we had more runs than that, how many more wins would that get us?"
|games_played|runs|runs_against|hits|hits_against|games_won now| 20| 100| 120| 200| 240| 10 predicted| 25| 120| 150| 240| 300| 12 user_adjusted| 25| 130| 140| 260| 280| 13
The only way I can think of to do this right now is to have many models, one for each feature, and put all the predicted features into one other model. Then users can update individual features. That feels like excess work and prone to errors, but I could be totally wrong. I can't just linearly forecast the features in the future because the relationship is more complex.
What is the algorithm/method/tool that's best suited to this? I'm using scikit-learn right now.