Abstract: Predicting the football match results has received great attention both in sports industry and academic fields. Many researchers have studied on predicting the match outcome using the simple features such as the number of shots and passes. However, little attention has been paid to using pass interaction features, which can represent how players in a match interact to each other. To this end, we propose a win-lose prediction model that predicts a match result using the pass interaction and other features, achieving high accuracy of 79.5%. By conducting an ablation study, we find that the proposed interaction features play an important role in accurately predicting match results. We believe our work can provide important insights both for industry and academic researchers who want to understand the characteristics of winning teams.
© Copyright 2021 Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support icSPORTS - Volume 1. All rights reserved.