MVP-Shapley: Feature-based Modeling for Evaluating the Most Valuable Player in Basketball

2025-06-05

AI Summary

This paper addresses the lack of interpretability and misalignment with expert consensus in basketball MVP evaluation. We propose the first causal, Shapley-value-based framework for interpretable MVP assessment. Methodologically, it integrates event-level temporal feature engineering, a win-probability binary classification model, and Shapley value-based contribution attribution, augmented by a causal sensitivity optimization mechanism to align predictions with expert voting preferences. Our key contribution is the first systematic application of Shapley values to quantitative MVP evaluation, coupled with causal inference to enhance both the reasonableness and interpretability of player contribution attribution. Evaluated on the NBA and Dunk City Dynasty datasets, the framework achieves a +0.23 improvement in Kendall’s τ rank correlation with expert consensus. Deployed as an industrial-grade real-time scoring system, it bridges academic rigor and practical applicability.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• Develops MVP evaluation framework using Shapley values
• Optimizes algorithm to match expert voting via causality
• Validates method with NBA and esports datasets

Innovation

• Leverages Shapley values for MVP evaluation
• Optimizes algorithm with causality and expert voting
• Validates using NBA and Dunk City datasets