Prediction of Handball Matches with Statistically Enhanced Learning via Estimated Team Strengths

TL;DR

This paper introduces a Statistically Enhanced Learning model for handball match prediction, combining machine learning with statistical features to improve accuracy and provide insightful analysis for coaches.

Contribution

The paper presents a novel SEL-based approach that enhances machine learning predictions with statistical features, outperforming existing models in handball match outcome prediction.

Findings

Achieved over 80% prediction accuracy.

Demonstrated the effectiveness of SEL features in improving model performance.

Provided a framework for analytical insights beyond prediction.

Abstract

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.