HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
Title:HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning
Abstract:School coaches prepare for opponents with game film and intuition. The analytics tools of professional teams stay out of reach. We ask how far public data can close this gap. Professional basketball is our case study, chosen for its data rather than the league. We fuse five public sources into one per-shot dataset of 4.23M shots over 21 seasons. The sources are shot locations, two play-by-play feeds, official matchup tracking, and player biometrics. Alignment across them is 99.5% to 100%. We also report two data pitfalls that are easy to miss. We then model a half-court possession as a sequential game. Shot values come from ShotNet, an embedding multilayer perceptron (MLP). On a held-out season it beats a zone-rate baseline and a logistic baseline, and its probabilities are well calibrated. A depth-limited expectimax search then solves the offensive decision tree, with branch-and-bound pruning to keep it real time. All training runs offline, so the online system stays light. A scouting planner and a playable simulator both run in a single browser page.
| Comments: | 5 pages, 5 figures, 2 tables, 2 algorithms. Submitted to the IEEE ICDM 2026 Teen Research Symposium. Code and live demo: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2608.29563 [cs.LG] |
| (or arXiv:2608.29563v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29563
arXiv-issued DOI via DataCite (pending registration)
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