war-gar-decomposition

NHL player evaluation using WAR and GAR metrics is calculated by running RAPM ridge regression on shift-level data, aggregating the regression components to quantify individual player contributions beyond basic goals and assists.

Shift data can estimate NHL contract surplus value by first modeling player GAR metrics through ridge regression, then translating those GAR outputs into visual player cards and cap efficiency estimates for contract analysis.

Ridge regression models RAPM by processing shift-level data to isolate a player's isolated impact on team performance, aggregating these statistical components into comprehensive WAR and GAR valuations for skaters.

Computing GAR and WAR requires a shift data CSV containing specific columns for on-ice events, along with regression setup configurations to properly process the component aggregation for player valuation.

WAR and GAR metrics support NHL trade assessments and contract negotiations by translating shift-level RAPM regression outputs into contract surplus value estimates and visual player cards for direct performance comparison.

WAR and GAR player valuation relies entirely on shift-level data inputs and ridge regression setup, meaning any missing columns or incomplete shift data will prevent accurate component aggregation and metric calculation.