  Machine Learning has become an integral part of engineering design and
decision making in several domains, including sports. Deep Neural Networks
(DNNs) have been the state-of-the-art methods for predicting outcomes of
professional sports events. However, apart from getting highly accurate
predictions on these sports events outcomes, it is necessary to answer
questions such as "Why did the model predict that Team A would win Match X
against Team B?" DNNs are inherently black-box in nature. Therefore, it is
required to provide high-quality interpretable, and understandable explanations
for a model's prediction in sports. This paper explores a two-phased
Explainable Artificial Intelligence(XAI) approach to predict outcomes of
matches in the Brazilian volleyball League (SuperLiga). In the first phase, we
directly use the interpretable rule-based ML models that provide a global
understanding of the model's behaviors based on Boolean Rule Column Generation
(BRCG; extracts simple AND-OR classification rules) and Logistic Regression
(LogReg; allows to estimate the feature importance scores). In the second
phase, we construct non-linear models such as Support Vector Machine (SVM) and
Deep Neural Network (DNN) to obtain predictive performance on the volleyball
matches' outcomes. We construct the "post-hoc" explanations for each data
instance using ProtoDash, a method that finds prototypes in the training
dataset that are most similar to the test instance, and SHAP, a method that
estimates the contribution of each feature on the model's prediction. We
evaluate the SHAP explanations using the faithfulness metric. Our results
demonstrate the effectiveness of the explanations for the model's predictions.
