Match preparation is an essential component for football (soccer) teams to be successful. Consequently, it is important to predict the opponent’s possible lineup as accurately as possible to adjust tactics and increase the probability of success. Various providers offer their lineup predictions to clubs and fans at no cost. In addition to established expert-based predictions, providers relying on swarm intelligence can offer an effective alternative and potentially create different assessments. Therefore, we analyzed the prediction performance of three expert- and one swarm intelligence-based providers for teams during the 2024/25 German Bundesliga season. Our results show that swarm intelligence-based approaches can not only compete with established approaches but also outperform them for individual teams. The best provider achieved an overall accuracy of 84.32%, which underscores the potential for match preparation. Furthermore, we investigate the lineup stability and its impact on the predictions of the providers. All in all, our work provides the first insight into an underrepresented field of research on which future work can build.

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Match Preparation in Professional Football: The Potential of Swarm Intelligence-Based Lineup Predictions

  • Marco Klaiber,
  • Manfred Rössle

摘要

Match preparation is an essential component for football (soccer) teams to be successful. Consequently, it is important to predict the opponent’s possible lineup as accurately as possible to adjust tactics and increase the probability of success. Various providers offer their lineup predictions to clubs and fans at no cost. In addition to established expert-based predictions, providers relying on swarm intelligence can offer an effective alternative and potentially create different assessments. Therefore, we analyzed the prediction performance of three expert- and one swarm intelligence-based providers for teams during the 2024/25 German Bundesliga season. Our results show that swarm intelligence-based approaches can not only compete with established approaches but also outperform them for individual teams. The best provider achieved an overall accuracy of 84.32%, which underscores the potential for match preparation. Furthermore, we investigate the lineup stability and its impact on the predictions of the providers. All in all, our work provides the first insight into an underrepresented field of research on which future work can build.