At the beginning, I also considered the idea of gathering individual player data and assembling team profiles based on active rosters for each game. That way, team strength could be evaluated more accurately based on who actually played, rather than relying on aggregate team stats.

I completely understand your point about using a method like TrueSkill to model team performance more dynamically — based on the presence or absence of specific players and the impact each one has on the team's overall performance. It’s a compelling approach and definitely something that would make predictions much more responsive to roster changes.

The main challenge, though, is the data itself. Even getting reliable game-level data for all teams from the 2000–01 season through to 2024–25 was already quite complex. So when it comes to going a level deeper — pulling individual player data, lineups, or starting rosters for every single game — it becomes difficult to know where to start. These data sources are often scattered, inconsistent, or hidden behind APIs that may have usage limits or costs. There’s also the issue of computational load and the sheer scale of the data, especially when you're working solo, as I currently am.

That’s actually part of why I’m sharing the project publicly — to see if others might be interested, just like you, and maybe even want to contribute. Sometimes just having another perspective helps catch something I may have overlooked.

Thanks again for your suggestions — I’ll definitely explore them further during the NBA off-season and hopefully come back with a more refined version of the project for the next season.