Review of: "Accuracy Without Profit: A Statistical Evaluation of Machine Learning Profitability in the English Premier League"

This paper provides a demonstration of why using just predictive accuracy can be a misleading metric for pro tability in ef cient markets like sports betting.By analyzing English Premier League data, the author showed that while machine learning models can achieve statistically distinct forecasts, they fail to generate consistent pro ts, primarily due to signi cant calibration errors that cause overcon dent bets.This insight positively shifts the focus from merely optimizing accuracy to prioritizing well-calibrated probability estimates-an important lesson for applying predictive models in any real-world, adversarial decision-making context.Positive aspects:The text makes a balanced discussion about machine learning techniques' outcomes and the nuances of human-driven markets.The feature engineering process is well described, detailing how rolling metrics like "Last 5 Games" transform raw match data into actionable trends.