Dissecting Moneyball: Improving Classification Model Interpretability in Baseball Pitch Prediction

Abstract

Data science, where technical expertise meets do-main knowledge, is collaborative by nature. Complex machine learning models have achieved human-level performance in many areas, yet they face adoption challenges in practice due to limited interpretability of model outputs, particularly for users who lack specialized technical knowledge. One key question is how to unpack complex classification models by enhancing their interpretability to facilitate collaboration in data science research and application. In this study, we extend two state-of-the-art methods for drawing fine-grained explanations from the results of classification models. The main extensions include aggregating explanations from individual instances to a user-defined aggregation level, and providing explanations with the original features rather than engineered representations. We use the prediction of baseball pitch outcome as a case to evaluate our extended methods. The experiment results of the methods with real sensor data demonstrate their improved interpretability while pre-serving superior prediction performance.