Aging curve: honestly not buildable here
Aging curves are not buildable from this data -- no birthdate or age column exists in any NBA table and only 3 season snapshots are on hand, so the honest move is to not pretend we have a curve.
How to read this figure
The status field reads not_buildable; there is no chart to read here, the module is a documented gap rather than a result.
Published finding
This measurement cannot be published from the committed inputs. No birthdate/age column exists in any table under data/domains/basketball_nba/ (scanned all parquets). Only 3 season snapshots of box scores exist (2023-24, 2024-25, 2025-26). Both the codebase's own prior audit (player_metric_landscape.py) and jobsearch/research/INDUSTRY_ANALYTICS_NBA.md independently reach the same verdict: aging curves need age-labeled player-seasons across MANY seasons (delta/GAM methods want thousands of paired seasons); 3 seasons with no age field is not honestly buildable -- the doc explicitly leans DEFER ('the honest move is to not pretend we have a curve').
Missing input: Age Source Found.
What it means
Aging-curve methods need age-labeled player-seasons across many seasons (delta and GAM methods want thousands of paired seasons). This repo has 3 season snapshots (2023-24, 2024-25, 2025-26) and no age field at all, so a curve cannot be honestly estimated. The codebase's own audit and an independent industry-analytics note both reach the same DEFER verdict.
Caveats and confounds
This is a data-availability limit, not a claim that aging effects do not exist; add age labels and more seasons and the module becomes testable.