MVP-Shapley: Feature-based Modeling for Evaluating the Most Valuable Player in Basketball
This provides a practical and explainable MVP evaluation method for the esports and multiplayer gaming community, though it is incremental as it adapts existing Shapley value techniques to a specific domain.
The paper tackles the problem of evaluating the Most Valuable Player (MVP) in basketball by introducing MVP-Shapley, a framework that uses Shapley values to rank players based on their contributions from play-by-play data, and validates it on NBA and Dunk City Dynasty datasets with online deployment.
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.