Quasi-linearity in deep learning regression problems (sports betting)
There are several principles in Data Science and Statistics which you will have to know before undertaking a project of any kind.

One of them is what is called parsimony principle (you could know him with other words) but it says that if you have two models with similar results, the simpler, with less parameters model should be chosen.

As you must have read, the neural networks have A LOT of parameters which of course might allow to learn your database (not generalize your model to not-seen-before data).

My recommendation is you use linear regression if you have a linear problem.

The neural networks can represent a linear problem too: Can neural network represent a relation that is a derivative?

But, if you want to do neural networks anyway, I will have to tell you that they work by trial and error. There are rules and suggested architectures, but you will have to find the best by yourself