用博弈论方法量化球员贡献，精准评估篮球MVP
MVP-Shapley: Feature-based Modeling for Evaluating the Most Valuable Player in Basketball
- 基于玩家行为数据，用谢尔林值计算每位球员的贡献
- 在NBA和Dunk City数据集上验证，结果与专家投票高度一致
- 可解释性强，适合篮球分析、赛事评分与行业应用
电子竞技和多人在线游戏社区的快速发展凸显了评选最佳球员（MVP）的重要性。构建一个可解释且实用的MVP评估方法极具挑战性。本研究聚焦于比赛逐事件数据，记录如助攻、得分等关键行为，提出一种新框架 exttt{MVP-Shapley}，利用谢尔林值（Shapley values）实现特征处理、胜负模型训练、贡献分配与MVP排名。通过因果视角优化算法，使其更贴近专家投票结果。在NBA数据集与Dunk City Dynasty数据集上验证有效性，并已实现在工业界的在线部署。
原文摘要 · Abstract (English)
The burgeoning growth of the esports and multiplayer online gaming community has highlighted the critical importance of evaluating the Most Valuable Player (MVP). The establishment of an explainable and practical MVP evaluation method is very challenging. In our study, we specifically focus on play-by-play data, which records related events during the game, such as assists and points. We aim to address the challenges by introducing a new MVP evaluation framework, denoted as \oursys, which leverages Shapley values. This approach encompasses feature processing, win-loss model training, Shapley value allocation, and MVP ranking determination based on players' contributions. Additionally, we optimize our algorithm to align with expert voting results from the perspective of causality. Finally, we substantiated the efficacy of our method through validation using the NBA dataset and the Dunk City Dynasty dataset and implemented online deployment in the industry.
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