Before you can actually deploy a model for betting purposes you need to test it to make sure it works. You can test it in real time as the season goes on, day by day, but this is a slow process and by the time you build up a large enough sample, the season could already be over. Or you can backtest. Backtesting is quicker and allows you to test against a much larger set of games in a much shorter time, but there are some drawbacks as well, most notably lines being set at what was known about the two teams at the time, instead of what we know now. This creates false positives and inflated profit margins. However, you can still use this approach to test different models to see which one performs best. You can also use it to combine models and see which approaches have synergy together.
The best way to do it would be to have a set of data that is split out day by day and you backtest against what the stats were at the time the game was played. However this would be more trouble than it is worth due to the massive amount of data and effort required to run such a backtest system.