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

TL;DR

This paper introduces \\oursys, a Shapley value-based framework for evaluating NBA players' contributions as MVPs using play-by-play data, validated on real datasets and aligned with expert opinions.

Contribution

The study presents a novel, explainable MVP evaluation method leveraging Shapley values and optimized for causality, validated on NBA datasets and deployed industry-wide.

Findings

Effective MVP ranking aligned with expert votes

Validated on NBA and Dunk City Dynasty datasets

Deployed in real-world industry settings

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.