Cracking the Black Box: Distilling Deep Sports Analytics
It addresses the need for actionable insights in sports analytics by providing a transparent model, though it is incremental as it builds on existing mimicry techniques.
The paper tackled the trade-off between accuracy and transparency in deep learning for sports analytics by developing a linear model tree that mimics neural networks, achieving high fidelity and interpretability for expert stakeholders.
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics. Neural nets achieve great predictive accuracy through deep learning, and are popular in sports analytics. But it is hard to interpret a neural net model and harder still to extract actionable insights from the knowledge implicit in it. Therefore, we built a simple and transparent model that mimics the output of the original deep learning model and represents the learned knowledge in an explicit interpretable way. Our mimic model is a linear model tree, which combines a collection of linear models with a regression-tree structure. The tree version of a neural network achieves high fidelity, explains itself, and produces insights for expert stakeholders such as athletes and coaches. We propose and compare several scalable model tree learning heuristics to address the computational challenge from datasets with millions of data points.