Basketball history,
made playable.
Full Court Games is an independent collection of free basketball strategy games. It was created to turn the debates fans already have—peak seasons, lineup fit, value, and all-time matchups—into decisions you can play.
More than a list of famous names.
A good historical basketball game should preserve the texture of the sport: a scorer can change an offense, a rebounder can rescue a small lineup, a great passer can raise everyone around him, and raw talent can still be weakened by poor positional fit.
That is why the games use player-seasons rather than a single career score. The same player can feel different from one year to another, and a less celebrated season can become the right choice for a particular roster. The goal is not to declare one permanent ranking. It is to create a fair set of tradeoffs that makes your basketball judgment matter.
A decision every possession
The best sports games create a steady rhythm of meaningful choices. Every spin, bid, comparison, lineup move, and upgrade asks you to balance immediate value against what could happen next.
Seasons, not just names
Players are represented by specific seasons. That distinction matters: a famous career can contain very different versions of the same player, each with a different role, statistical profile, and fit.
Fast to learn, worth replaying
Each game explains its central rule in a few lines, then lets the player discover the strategy. Random pools and changing opponents keep the answer from becoming automatic.
How the numbers become a game.
Historical inputs
Player cards begin with regular-season box-score records. Points, rebounds, assists, shooting, games played, position, team, and season are organized into comparable season profiles. The historical catalog spans thousands of players, while curated pools identify All-Stars and Hall of Fame players for focused modes.
Ratings and skill profiles
Overall ratings and skill grades are game-design values, not official league ratings. They combine production, efficiency, role, and position-aware measures into a consistent scale. Older seasons sometimes contain fewer recorded categories, so the system uses the available evidence and conservative fallbacks instead of pretending every era has identical data.
Lineup and simulation logic
A team is evaluated as five connected season profiles. Playing someone far from a listed position creates a fit penalty. Season simulation compares each roster with the field, then adds a limited amount of variance so the strongest team is favored without making every result inevitable. The output is a fictional game result—not a prediction, scouting report, or betting recommendation.
Corrections and iteration
Historical data can contain naming differences, missing records, and classification edge cases. The catalog is reviewed and normalized, but corrections are welcome. If you spot a factual problem or a game rule that behaves unexpectedly, please send feedback.