通过深度学习与因果建模，发现冰球进攻节奏和阵型可显著提升得分概率。
Gaining Momentum: Uncovering Hidden Scoring Dynamics in Hockey through Deep Neural Sequencing and Causal Modeling
- 用逻辑回归与LSTM捕捉微事件序列，构建可解释的进攻动量模型。
- 发现最优进攻序列使得分潜力提升15%，因果效应达0.12（p<1e-50）。
- 适合教练和分析师用于实时战术优化，推动冰球分析向因果驱动发展。
我们提出一个统一的数据驱动框架，用于量化和提升职业冰球比赛中的进攻动量与得分可能性（预期进球，xG）。基于包含54.1万条NHL赛事记录的Sportlogiq数据集，完整流程包括五个阶段：(1) 使用逻辑回归对微事件进行可解释的动量加权；(2) 采用梯度提升决策树进行非线性xG估计；(3) 利用长短期记忆网络（LSTM）建模时间序列；(4) 通过主成分分析（PCA）结合标准化球员坐标进行K均值聚类，发现空间阵型；(5) 使用X-Learner因果推断估计器量化采用识别出的“最优”事件序列与阵型的平均处理效应（ATE）。结果显示，ATE为0.12（95%置信区间：0.05–0.17，p < 1e-50），相当于得分潜力相对提升15%。结果表明，有策略的序列结构与紧凑阵型具有因果提升作用。本框架为教练和分析师提供实时可操作洞察，推动冰球分析向原理化、因果驱动的战术优化迈进。
原文摘要 · Abstract (English)
We present a unified, data-driven framework for quantifying and enhancing offensive momentum and scoring likelihood (expected goals, xG) in professional hockey. Leveraging a Sportlogiq dataset of 541,000 NHL event records, our end-to-end pipeline comprises five stages: (1) interpretable momentum weighting of micro-events via logistic regression; (2) nonlinear xG estimation using gradient-boosted decision trees; (3) temporal sequence modeling with Long Short-Term Memory (LSTM) networks; (4) spatial formation discovery through principal component analysis (PCA) followed by K-Means clustering on standardized player coordinates; and (5) use of an X-Learner causal inference estimator to quantify the average treatment effect (ATE) of adopting the identified "optimal" event sequences and formations. We observe an ATE of 0.12 (95% CI: 0.05-0.17, p < 1e-50), corresponding to a 15% relative gain in scoring potential. These results demonstrate that strategically structured sequences and compact formations causally elevate offensive performance. Our framework delivers real-time, actionable insights for coaches and analysts, advancing hockey analytics toward principled, causally grounded tactical optimization.
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