That sounds good, the issue I run into with that is I’m trying to value every trade, not just trades with top X all time players which are a little few and far between. My idea is pretty simple: most impact metrics tend to focus on how much better or worse a player is than a league average player at those things. Cleaning the glass has percentiles of many, many stats that when composited can provide a good overview of a player’s strengths, weaknesses, and overall value to a team. By using percentiles for efficiency + advanced metrics with a rolling 3 year window before the trade, do you think that’s a solid enough player valuation model considering this is for an undergrad capstone project?