Question: 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.

Answer: 
What do you mean by doesn't predict? Are you implying the model is doing the same as randomly guessing?

Maybe your cutoff (for predicting a 'positive' result) is not adequate? You may way want to try producing some ROC curves based on data you currently have to choose an appropriate cutoff. You would want take into consideration the 'cost' of making a false positive as compared to a false negative when choosing this cutoff.

If you are still not doing well then your predictors are probably not associated with the response.
A general strategy when a model has no predictive power is to start over. 

But does it really have *no* predictive power? That is does it do no better than flipping a coin? 

In general, and with only rare exceptions, models will do better on the data they were trained on then on new data. 

Beyond that, some more context might help.