What's here
- 4,385 player profiles with career and season-by-season stats, game logs, awards, championships, and an era-adjusted dominance multiplier.
- 30 team pages with all-time rosters, year-by-year records, and championship history.
- 80 season pages with standings, league leaders, MVP / Finals MVP / scoring leader, and full league averages for the year.
- Tools for cross-era analysis: era-adjusted dominance leaderboards, an era comparison, a GOAT calculator, and side-by-side player comparison.
Data sources
- basketball-reference.com — current and recent standings and per-game stats.
- the Kaggle NBA dataset — the foundation for player info, season stats, and game logs through 2022-23.
- the NBA API — current-season per-game stats; awards and championship rosters compiled from public sources.
Data is refreshed nightly.
How the dominance metric works
A player's era-adjusted "dominance" in a stat is their career average in that stat divided by the league average across the seasons they were active. A value of 1.0x means the player matched the league average of their era; 2.0x means they doubled it. Each season's ratio is computed against that season's league average, and the ratios are combined into a career figure weighted by games played — so a high-scoring era doesn't inflate a player's numbers relative to a low-scoring one.
Only qualifying seasons (at least 30 games played) count toward the league-average baseline. For shooting percentages (FG%, 3P%, FT%) the qualifier is "made at least one shot in that category" rather than attempts, since the underlying dataset omits attempts columns for 2014+ seasons.
How it's built
Static site, generated by a Python build script with Jinja2 templates. The output is plain HTML/CSS/JS — no server-side rendering, no database queries at request time. Everything you click was pre-computed at build time, including the era ratios and rankings.