Prediction of Handball Matches with Statistically Enhanced Learning via Estimated Team Strengths
This provides incremental improvements for handball coaches by offering predictive insights for match preparation.
The authors tackled handball match prediction by developing a Statistically Enhanced Learning model that achieved over 80% accuracy, outperforming state-of-the-art models, and transformed it into an analytical tool for coaches.
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.