Predicting handball matches with machine learning and statistically estimated team strengths - Felice, F.; Ley, C. (Journal of Sports Analytics, 2025)
We propose a Machine Learning model to predict handball games and derive insightful information for sport coaches. Our model, augmented with statistical 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 compare different models, evaluate them to assess their predictive capabilities and show that our statistical variables, estimating the strengths of the teams, appear as the most important features to the selected model. 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 by providing statistical and predictive insights to prepare future competitions.