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.

我们提出一种统计增强学习（SEL）模型来预测手球比赛。我们的机器学习模型加上 SEL 特征，在准确率上超过了现有模型。我们展示了如何构建数据集，在过去的女子俱乐部比赛中训练机器学习模型。我们比较了不同的模型，并评估它们的性能能力。最后，解释性方法使我们的工具不仅是一个预测解决方案，而是一个高度洞察力的分析工具。这对手球队的教练提供了有价值的统计和预测见解，以准备未来的比赛。