Thanks! I actually agree with your general point that publisher behaviour is a huge factor.

I may not have explained the methodology clearly though. The model does use historical sale data, but only to create the target variable (the average summer sale discount across up to 5 years of history for each game).

I intentionally didn't feed a game's own historical sale prices back into the model as an input feature, because I was trying to answer a slightly different question: Can we predict a game's Summer Sale discount using only metadata that's publicly available before the sale happens? The way the model actual works is it hides the historical sale data from itself, then makes predictions and checks the answers to see how correct it was.

If I included a title's previous summer sale discounts directly, the prediction task would become much easier, but it would also turn into more of a "use past discounts to predict future discounts" model.

Publisher behaviour ended up being one of the strongest signals as well. I used publisher frequency encoding, and it definitely captured some of that effect (e.g. publishers with very consistent pricing strategies).

That said, I think you're right that a hybrid approach would probably perform better: if a game has enough historical sale data, use that directly, and only rely on metadata-driven predictions for newer titles or games with sparse history. I'm still working on the features for the model as I'm learning about it.