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Iterative Tactical Optimisation in Football Simulations through Process Mining and Agent-Based Modelling
Cebotar, Victor (2025) Iterative Tactical Optimisation in Football Simulations through Process Mining and Agent-Based Modelling.
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| Abstract: | This research presents a novel iterative framework that integrates agentbased modelling and simulation (ABMS) with process mining techniques to improve tactical decision-making in football simulations. Addressing the gap left by existing sports analytics tools that do not feed insights back into autonomous simulations, this data-driven approach employs a closed-loop structure inspired by CRISP-DM, Gaia, KDD, and Prometheus. In this framework, event logs generated by a multi-agent football simulator are repeatedly analysed using process mining—specifically as a verification tool—while tactical inefficiencies are primarily identified through detailed KPI analysis. Verified insights are then manually incorporated into agent decision logic, allowing for continuous refinement and measurable improvement in team performance. Across six iterative cycles, the method led to tactical gains, with the home team’s results improving from 2 wins and a –23 goal difference at baseline to 28 wins and a +104 goal difference, while process mining confirmed the emergence of more effective tactical patterns. The results, contrasted to real-world league data, demonstrate the framework’s potential as a white-box optimisation tool for bridging data-driven process diagnostics with structured ABMS refinement in football analytics. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 30 exact sciences in general, 50 technical science in general, 54 computer science, 58 process technology |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/107816 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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