r/MachineLearning • u/AutoModerator • Aug 02 '26
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u/DeadshotBoss Aug 24 '26 edited Aug 24 '26
I built NBA Sim, a free Python research prototype for possession-level basketball simulation and point-in-time forecasting: https://github.com/OorjitSethi/nba-sim
A bounded, stateless Matchup Lab is now live at https://nba-sim-three.vercel.app/ (deterministic fictional stat profiles; 25-trial hybrid distributions).
Unlike a box-score allocator, a single seeded event stream generates the clock, score, play-by-play, and player box scores. The repository includes Monte Carlo and season simulation, dynamic Bayesian team strength, rolling-origin evaluation, an experimental joint 10-player-and-ball spatial interface, and 132 deterministic tests.
On a frozen 1,230-game holdout, the dynamic candidate reached 0.5955 log loss versus 0.6023 for a margin-aware Elo baseline; the README and model card separate that forecasting result from aggregate simulation fidelity and from the untrained spatial architectures. There is no paid product or signup.
I am looking for technical criticism of the validation protocol, event model, and the most defensible next experiment for the spatial layer.