Every NBA rookie enters the league as an unknown. No career stats. No track record. Just signals.
The Bourne Identity concept applies directly: a player wakes up with no memory of who they are. We have to build their identity from scratch — before they've played a single NBA minute. The Rookie Identity model does exactly this using three data sources.
Percentage stats (FG%, FT%) are highly predictable from college efficiency — a player who shoots well in college generally shoots well in the NBA. Counting stats (PTS, REB, AST) are genuinely hard. Minutes allocation in year 1 is the dominant variable we cannot predict before a season starts.
Three-point shooting at 19.0% is the hardest rookie stat in all of sports analytics. The NBA three-point line is longer, the defense is better, and college 3P% has weak predictive validity for NBA 3P%. College shooting mechanics frequently don't survive contact with elite NBA defenders.
This is why we show ranges, not point estimates. “This player's historical comps produced between 8 and 22 points per game in year 1. The median was 14.2.” That is more honest than projecting 14.2 with false precision.
The comps method respects what we don't know: NBA coaching decisions, team roster construction, and the randomness of injuries in year 1 are all outside our model. What we can do is show you the full distribution of outcomes for similar players — and let you decide what to do with the uncertainty.
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