Home vs. Away variance is killing my model's precision. How are you guys handling this?

I’ve been digging into some league and team-specific data lately at Oncastudy, and the home/away variance is proving to be a massive headache. It’s essentially acting as pure noise, dragging down the reliability of static statistical models.

It feels like a structural mismatch because environmental factors and other "unstructured" variables aren't being captured properly in the normalized datasets. I’m currently looking into dynamic regression with environmental weighting or maybe some situational correction algorithms to fix this.

When these environmental variables start distorting your model weights, what’s your preferred way to tune parameters? Do you have a specific approach to keep the predictions tight without over-indexing on the home-field advantage?

Would love to hear how you guys deal with this kind of structural inconsistency.