  We propose a Statistically Enhanced Learning (aka. SEL) model to predict
handball games. Our Machine Learning model augmented with SEL features
outperforms state-of-the-art models with an accuracy beyond 80%. In this work,
we show how we construct the data set to train Machine Learning models on past
female club matches. We then compare different models and evaluate them to
assess their performance capabilities. Finally, explainability methods allow us
to change the scope of our tool from a purely predictive solution to a highly
insightful analytical tool. This can become a valuable asset for handball
teams' coaches providing valuable statistical and predictive insights to
prepare future competitions.
