  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.
