From xG to WAR: A Comprehensive Framework for Evaluating NHL Player Value

Abstract

This thesis presents a machine learning-based Wins Above Replacement (WAR) model for NHL skaters, integrating play-by-play and shift data from the 2023–24 and 2024–25 seasons. A Random Forest classifier predicts expected goals (xG) at the shot level, capturing offensive and defensive contributions, while a team-level Random Forest regressor translates performance metrics into win probabilities. Individual player contributions are standardized per 60 minutes, compared to replacement-level baselines, and weighted using feature importances from the win model to compute WAR. The result is a single, context-aware metric that quantifies a skater’s total value in terms of added team wins.