Optimizing Offensive Gameplan in the National Basketball Association with Machine Learning

TL;DR

This paper explores using machine learning models, including neural networks, to analyze and optimize offensive strategies in NBA basketball by modeling and correlating existing metrics like ORTG with gameplay features.

Contribution

It introduces a machine learning approach to model and optimize NBA offensive strategies based on existing metrics, demonstrating the effectiveness of neural networks over linear regression.

Findings

Neural network models slightly outperform linear regression in predicting ORTG.

Machine learning models can effectively correlate game features with offensive ratings.

Optimized feature combinations improve offensive performance predictions.

Abstract

Throughout the analytical revolution that has occurred in the NBA, the development of specific metrics and formulas has given teams, coaches, and players a new way to see the game. However - the question arises - how can we verify any metrics? One method would simply be eyeball approximation (trying out many different gameplans) and/or trial and error - an estimation-based and costly approach. Another approach is to try to model already existing metrics with a unique set of features using machine learning techniques. The key to this approach is that with these features that are selected, we can try to gauge the effectiveness of these features combined, rather than using individual analysis in simple metric evaluation. If we have an accurate model, it can particularly help us determine the specifics of gameplan execution. In this paper, the statistic ORTG (Offensive Rating, developed by Dean Oliver) was found to have a correlation with different NBA playtypes using both a linear regression model and a neural network regression model, although ultimately, a neural network worked slightly better than linear regression. Using the accuracy of the models as a justification, the next step was to optimize the output of the model with test examples, which would demonstrate the combination of features to best achieve a highly functioning offense.