发表机构
Beijing National Day School; University of Memphis（北京十一学校; 孟菲斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究以职业篮球为案例，融合公开数据构建投篮数据集，提出基于ShotNet和深度受限期望最大化搜索的实时神经博弈树系统，实现对手感知的控球规划，相关模型性能优于基线且可在浏览器运行。
AI 中文摘要
学校教练依靠比赛录像和直觉来准备应对对手，而职业球队的分析工具却难以获取。本文探究公开数据能在多大程度上缩小这一差距，选择职业篮球作为案例研究，而非关注联盟本身。我们将五个公开数据源融合为一个包含21个赛季、423万次投篮的单投篮数据集，这些数据源包括投篮位置、两份比赛实况数据、官方对位追踪数据以及球员生物特征数据，各数据源间的对齐度达99.5%至100%，同时还指出了两个容易被忽略的数据陷阱。随后，我们将半场控球建模为序贯博弈，投篮价值来自ShotNet（一种嵌入多层感知机MLP）。在保留的一个赛季数据上，该模型击败了区域率基线和逻辑回归基线，且其概率校准良好。接着，采用深度受限的期望最大化搜索求解进攻决策树，并使用分支定界剪枝以保证实时性。所有训练均离线运行，因此在线系统保持轻量，球探规划器和可玩模拟器均可在单个浏览器页面中运行。
英文摘要
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.
Comments5 pages, 5 figures, 2 tables, 2 algorithms. Submitted to the IEEE ICDM 2026 Teen Research Symposium. Code and live demo: https://github.com/sujo666/hoopmind
