Explainable AI techniques with application to NBA gameplay prediction

In this paper, an explainable artificial intelligence (AI) technique is employed to analyze the match style and gameplay of the national basketball association (NBA). A descriptive analysis on the evolution of the NBA gameplay is conducted by using clustering and principal component analysis. Supervised-learning based AI models (including the random forest and the feed-forward neural network) are applied to produce accurate predictions on NBA outcomes at a season-by-season and a month-by-month basis. To evaluate the interpretability of the established AI models, an explainable AI algorithm is utilized to deduce and assess the precise reasoning behind the model prediction based on the local interpretable model-agnostic explanation method. To illustrate its application potential, the method is applied to the open-source NBA data from 1980 to 2019. Experimental results demonstrate the effectiveness of the introduced explainable AI algorithm on predicting NBA outcomes with interpretation.