AI for Handball: predicting and explaining the 2024 Olympic Games tournament with Deep Learning and Large Language Models

TL;DR

This paper presents a deep learning and large language model-based approach to predict and explain the outcomes of the 2024 Olympic handball tournament, offering insights for sports experts and stakeholders.

Contribution

It introduces a novel combination of deep learning, explainable AI, and LLMs to predict match results and generate human-friendly explanations in Olympic handball.

Findings

Accurate predictions of match outcomes for 2024 Olympic handball.

Explainable AI reveals key factors influencing match results.

LLMs produce understandable explanations for sports analysts.

Abstract

Over summer 2024, the world will be looking at Paris to encourage their favorite athletes win the Olympic gold medal. In handball, few nations will fight hard to win the precious metal with speculations predicting the victory for France or Denmark for men and France or Norway for women. However, there is so far no scientific method proposed to predict the final results of the competition. In this work, we leverage a deep learning model to predict the results of the handball tournament of the 2024 Olympic Games. This model, coupled with explainable AI (xAI) techniques, allows us to extract insightful information about the main factors influencing the outcome of each match. Notably, xAI helps sports experts understand how factors like match information or individual athlete performance contribute to the predictions. Furthermore, we integrate Large Language Models (LLMs) to generate human-friendly explanations that highlight the most important factors impacting the match results. By providing human-centric explanations, our approach offers a deeper understanding of the AI predictions, making them more actionable for coaches and analysts.