Research Article

Advancing Football Analytics: Predictive Modeling and Performance Analysis in the Bundesliga Using Machine Learning

Authors

Seyyed Ahmad Edalatpanah * Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran.
Markus Hess Department of System Dynamics and Friction Physics, Faculty V-Mechanical Engineering and Transport Systems, Technische Universität Berlin, Berlin, Germany.

https://doi.org/10.48314/ceti.v1i4.42

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Abstract

The Bundesliga Data Shootout (BDS) is an innovative competition that merges the thrill of professional football with the analytical power of Data Science (DS). Its primary aim is to foster collaboration between data scientists and football enthusiasts to explore and harness the vast data available from Germany's premier football league, the Bundesliga. The competition encourages participants to develop creative, data-driven models to uncover new insights, enhance Performance Analysis (PA), and refine Decision-Making (DM) processes within the sport. By leveraging extensive datasets that include player statistics, match outcomes, and team performance metrics, the competition empowers participants to apply cutting-edge Machine Learning (ML) techniques, fostering advancements in Football Analytics (FA). This initiative not only enhances our understanding of the game but also demonstrates the transformative role that DS can play in shaping the future of football strategy and performance evaluation.

Keywords

Bundesliga data shootout Challenges Data science Football analytics Machine learning Performance analysis Decision-making Sports data Predictive modelling

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Published 2024-12-02
Issue Vol. 1 No. 4 (2024): Computational Engineering and Technology Innovations
Section Articles
Document Type Research Article
Views 1666
Downloads 9433

How to Cite

Edalatpanah, S. A., & Hess, M. (2024). Advancing Football Analytics: Predictive Modeling and Performance Analysis in the Bundesliga Using Machine Learning. Computational Engineering and Technology Innovations, 1(4), 233-247. https://doi.org/10.48314/ceti.v1i4.42

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