用423万次投篮数据构建实时进攻决策系统，让普通球队也能玩转职业级分析。
HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning
- 融合五类公开数据构建超大规模投篮数据库，对齐率99.5%以上
- 基于ShotNet的深度模型预测投篮价值，概率校准良好且超越基准
- 实时求解进攻决策树，浏览器内即可运行模拟与侦察工具
学校教练依赖比赛录像和直觉备战对手，而职业队的分析工具却难以触及。我们探讨公开数据能将这一差距缩小到何种程度。以职业篮球为案例研究，因其数据丰富而非联赛本身。我们将五个公开数据源整合成一份每回合的投篮数据集，覆盖21个赛季共423万次投篮。数据源包括投篮位置、两份逐回合记录、官方对阵追踪及球员生理数据。各数据间对齐率达99.5%至100%。我们还指出两个易被忽视的数据陷阱。随后将半场进攻建模为序列博弈，投篮价值由ShotNet——一个嵌入式多层感知机（MLP）——计算。在保留赛季测试中，该模型优于区域命中率基线和逻辑回归基线，且概率校准良好。采用深度受限的期望极大搜索结合分支定界剪枝，实现实时求解。所有训练离线完成，线上系统轻量高效。侦察规划器与可玩模拟器均可在单一浏览器页面运行。
原文摘要 · Abstract (English)
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.
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