Shooting location is a core indicator of offensive style in invasion sports. Existing basketball shot-chart analyses often use spatial information for descriptive visualization, location-based efficiency modeling, or clustering players into shooting archetypes, yet few studies provide a unified framework for fair comparison of shot-type-specific tendencies. We propose the shot-type-aware areal multilevel Poisson (STAMP) model, which jointly models team-level field-goal attempts across predefined court regions, seasons, and shot types using a Poisson likelihood with a possession-based exposure offset. The hierarchical random-effects structure combines team, area, team-area, and team-side random effects with shot-type-specific random slopes for key shot categories. We fit the model using approximate Bayesian inference via the Integrated Nested Laplace Approximation (INLA), enabling efficient analysis of more than $3\times 10^{5}$ shots from two seasons of B.LEAGUE (the men's professional basketball league in Japan). The STAMP model achieves better out-of-sample predictive performance than simpler baselines, yielding interpretable relative-rate maps and left-right bias summaries. Case studies illustrate how the model reveals team-specific spatial tendencies for comparative analysis, and we discuss its limitations and potential extensions.

翻译：投篮位置是入侵类运动中进攻风格的核心指标。现有篮球投篮热图分析常利用空间信息进行描述性可视化、基于位置的效率建模或球员投篮风格聚类，但鲜有研究提供统一框架以公平比较不同投篮类型的倾向性。我们提出了一种面向投篮类型的区域多级泊松（STAMP）模型，该模型结合基于控球次数的暴露偏移量，采用泊松似然函数，对预设球场区域、赛季和投篮类型下的球队级投篮出手次数进行联合建模。分层随机效应结构整合了球队、区域、球队-区域和球队-方位随机效应，并针对关键投篮类别引入投篮类型特异性的随机斜率。我们通过集成嵌套拉普拉斯近似（INLA）进行近似贝叶斯推断以拟合模型，从而实现对日本男子职业篮球联赛（B.LEAGUE）两个赛季中超过$3\times 10^{5}$次投篮的高效分析。STAMP模型在样本外预测性能上优于简单基线模型，可生成可解释的相对出手率热图及左右偏差汇总。案例研究展示了该模型如何揭示球队特异性空间倾向以支持比较分析，同时讨论了其局限性及潜在扩展方向。