I have a logistic regression model which predicts win/loss on amount of money paid. I run my model every two hours on new data that I acquire and use it to predict the next two hours. However, I keep finding that my model underpredicts win/loss for each amount of money paid. So I'm in this situation where I have a statistical model but it doesn't seem to predict new data as it comes in.
I'm left wondering, what do I do now? My model doesn't predict for the new data, but I need for it to do so.
What are some general strategies for when a model has no predictive power?
As a side note, I should have mentioned that I actually had two models. One which predicted win/loss on 0 to 5 \$ and another for 5.01 \$ and more. It's possible that this may have been a culprit, and I might just want to utilize a robust regression model instead. Not entirely relevant, but just thought I'd mention.