Comparing Machine Learning Methods for Predicting Wins Above Replacement (WAR) in Major League Baseball

Study analyzing machine learning methods for predicting player Wins Above Replacement (WAR) in Major League Baseball (MLB). The study highlights the superiority of the Regularization method, specifically Lasso regression, over Random Forests and Neural Networks, offering practical insights for precise player performance forecasts and informed decision-making in MLB teams.