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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | An Open-Source Implementation of FIFA’s Enhanced Football Intelligence |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | This thesis addresses the implementation of concepts outlined in FIFA's Enhanced Football Intelligence (EFI) document through an open-source library, filling the gap with accessible implementations for these concepts. The EFI document provides descriptions for various metrics related to football performance analysis used in the FIFA World Cup 2022. Existing packages in football analytics do not fully incorporate the latest methodologies used in the FIFA World Cup 2022, essential for the creation of a source that aligns with FIFA's definitions. The implemented concepts cover possession control, phases of play, ball recovery time, line breaks, receptions behind midfield and defensive lines, defensive line height and team length, team shape, final third entries, pressure on the ball, forced turnovers, and expected goals (xG). Utilizing the explanations of these concepts, the thesis formulates a main approach and involves refinements. The level of stability varies, with methods that incorporate fewer heuristics tending to be more stable, while those that rely on a greater number of heuristics tend to be less stable. However, during implementation, limitations were encountered, including the lack of technical details and absence of FIFA's resources regarding the technology they have employed. Specifically, the lack of heuristics mentioned in the definitions of the concepts was a notable gap. Challenges were also observed, such as specific matches that are labeled as outliers due to their performance in distinct concepts. Despite these limitations and challenges, the implementation overall offers stable and accurate performance, aligned with FIFA's outcomes. In future work, these limitations can be addressed through a comprehensive approach. Firstly, revisiting the concepts with additional information regarding their descriptions will enhance the understanding of the underlying factors. Secondly, the expansion of datasets will not only provide a broader foundation for analysis but also improve the heuristics employed, leading to enhanced accuracy and stability of the outcomes. Additionally, the application of advanced technologies, similar to those employed by FIFA, can significantly contribute to improving the reliability and effectiveness of the results. By considering these avenues, future research can overcome the identified limitations. This thesis contributes to advancing football performance analysis by addressing these challenges and provides a valuable resource for researchers, analysts, and football enthusiasts seeking to reproduce FIFA's match reports and gain insights into football performance. Keywords: Enhanced Football Intelligence, FIFA, open-source implementation, football performance analysis, sports analytics. |
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