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.

翻译：在美国职业篮球联赛（NBA）的分析革命过程中，特定指标和公式的发展为球队、教练和球员提供了审视比赛的新视角。然而，问题随之而来——我们如何验证这些指标？一种方法是单纯依靠目测估算（尝试多种不同的战术方案）和/或反复试错——这是一种基于估计且成本高昂的方法。另一种方法是利用机器学习技术，用一组独特的特征对现有指标进行建模。这种方法的关键在于：通过选取这些特征，我们可以尝试衡量这些特征组合的有效性，而不是在简单的指标评估中采用个体分析。如果我们拥有一个精确的模型，它将特别有助于确定战术执行的具体细节。本文发现，迪恩·奥利弗提出的进攻评分（ORTG）统计指标与NBA不同的进攻类型之间存在相关性，研究采用了线性回归模型和神经网络回归模型，尽管最终神经网络的表现略优于线性回归。以模型的准确性作为依据，下一步是使用测试示例对模型输出进行优化，从而展示实现最高效进攻的最佳特征组合。