AI 中文总结
本研究利用1,006次投篮数据，通过逻辑回归、随机森林等模型评估xG框架，发现上下文基线随机森林最优，并探索了xPV作为探索性指标，为职业长曲棍球进攻影响力提供初步案例研究。
AI 中文摘要
职业长曲棍球统计数据总结了比赛结果，但对投篮质量或得分机会背后的角色提供的信息有限。本研究开发了一个有据可查的框架，用于估计预期进球（xG）并归因记录的进攻参与，使用了1,006次手动标注的罗切斯特刀锋队投篮尝试（包括151个进球），这些数据来自2025-2026赛季国家长曲棍球联盟连续13场比赛。逻辑回归、随机森林和极限随机树在三个嵌套特征集上进行了评估，采用留一比赛交叉验证和训练折基础率基准。上下文基线随机森林的观察合并对数损失最低（0.4189），布里尔分数最低（0.1260），分别比基准提高了1.22%和1.50%；九个规格中有五个未能超过基准。添加两人配合和掩护类型字段并未改善主要指标。核心进攻影响力属性通过射手xG和最终传球者的基于投篮的预期助攻来记录参与。预期掩护价值（xPV）将合格掩护的观察状态概率与无掩护反事实进行比较。其幅度与模型噪声无法区分。其方向性模式超过了200次行置换重复，但有限的尾部分辨率和未能保持比赛级别的掩护构成使该诊断具有描述性而非推断性。因此，xPV仅作为探索性增强组件报告。鉴于单队、13场比赛的样本，结果是初步案例研究，而非联盟范围或因果估计。
英文摘要
Professional box-lacrosse statistics summarize outcomes but provide limited information about shot quality or the roles behind scoring opportunities. This study develops a documented framework for estimating expected goals (xG) and attributing recorded offensive involvement using 1,006 manually annotated Rochester Knighthawks shot attempts, including 151 goals, from 13 consecutive 2025-2026 National Lacrosse League games. Logistic regression, random forest, and extremely randomized trees were evaluated across three nested feature sets using Leave-One-Game-Out cross-validation and a training-fold base-rate benchmark. The contextual baseline random forest had the lowest observed pooled log loss (0.4189) and Brier score (0.1260), improving on the benchmark by 1.22% and 1.50%; five of nine specifications did not beat the benchmark. Adding two-man-action and pick-type fields did not improve the primary metrics. Core Offensive Impact attributes recorded involvement through shooter xG and shot-based expected assists for final passers. Expected Pick Value (xPV) compares a qualifying pick's observed-state probability with a no-pick counterfactual. Its magnitude was indistinguishable from model noise. Its directional pattern exceeded 200 row-permutation replicates, but limited tail resolution and failure to preserve game-level pick composition make the diagnostic descriptive rather than inferential. Accordingly, xPV is reported only as an exploratory augmented component. Given the single-team, 13-game sample, the results are an initial case study rather than league-wide or causal estimates.
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