研究人员用公开数据源构建了篮球战术分析系统，能实时模拟进攻决策，接近专业队分析水平。
HoopMind融合5个公开数据源构建了423万次投篮数据集，覆盖21个赛季。该系统使用ShotNet多层感知机评估投篮价值，在保留赛季上表现优于基线模型。系统采用深度受限的expectimax搜索解决进攻决策树，通过分支定界剪枝保持实时性能。所有训练离线进行，在线系统保持轻量级，可在单个浏览器页面运行。
HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning
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.