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HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning

Paper recorded by Signals 4 on 2026-08-30 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

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 fee

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#275 most recent of 300 cs.AI papers we have recorded · ↑ newer: SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LL · ↓ older: PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spat

Cite this page: HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning: the #275 most recent of 300 cs.AI papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/hoopmind-a-real-time-neural-game-tree-system-for-opponent-aware-possession-plann.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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