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SHOT7M2 is a synthetic, hierarchical, compositional basketball behavior dataset with 7.2 million frames that demonstrate hierarchical organization of basketball actions. Based on the animation model of Starke et al., it uses neural state machines to predict future character poses. The dataset contains 4,000 clips, each with 1,800 frames, performed by a single agent executing various basketball motions. Each clip is labeled with one of four activity types: casual play, intense play, dribbling training, or no play. It provides 26‑keypoint skeletal poses and combinations of 4 activity types, 12 actions, and 14 basic motions.

The dataset collects RGB images, triangulated meshes with UV coordinates, texture maps, 3D human poses, and camera projection matrices from the NBA2K19 game engine. It features diverse basketball postures and provides industry‑grade meshes of clothed basketball players.