用先验信息提升小样本篮球队组合的评分准确性
Lineup Regularized Adjusted Plus-Minus (L-RAPM): Basketball Lineup Ratings with Informed Priors
- 基于回归模型，结合对手强度和球员构成信息
- 小样本下预测性能显著优于现有基线方法
- 适合分析频繁换人导致数据稀疏的实战场景
识别篮球等运动中表现优异的球员组合（即阵容）是体育数据分析的核心任务。由于比赛中频繁换人，导致数据高度稀疏：一支NBA球队一个赛季使用超过600套阵容，平均每套阵容仅出场25-30次回合。由此产生的统计数据噪声大、预测能力差。目前尚无公开工作解决此问题。本文提出一种基于回归的L-RAPM方法，同时控制对手影响并利用阵容球员信息。实验表明，该方法在小样本条件下显著优于现有基线，且样本越少，优势越明显。
原文摘要 · Abstract (English)
Identifying combinations of players (that is, lineups) in basketball - and other sports - that perform well when they play together is one of the most important tasks in sports analytics. One of the main challenges associated with this task is the frequent substitutions that occur during a game, which results in highly sparse data. In particular, a National Basketball Association (NBA) team will use more than 600 lineups during a season, which translates to an average lineup having seen the court in approximately 25-30 possessions. Inevitably, any statistics that one collects for these lineups are going to be noisy, with low predictive value. Yet, there is no existing work (in the public at least) that addresses this problem. In this work, we propose a regression-based approach that controls for the opposition faced by each lineup, while it also utilizes information about the players making up the lineups. Our experiments show that L-RAPM provides improved predictive power than the currently used baseline, and this improvement increases as the sample size for the lineups gets smaller.
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