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Querying Football Matches for Event Data: Towards Using Large Language Models

  • Conference paper
  • First Online:
Sports Analytics (ISACE 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14794))

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Abstract

Football, being one of the most popular sports in the world, has attracted significant attention from researchers exploring the potential of Artificial Intelligence (AI). In particular, Large Language Models (LLMs), exemplified by digital assistants such as ChatGPT, have proven their capabilities and offer a potentially effective avenue for football research. However, accessibility of football data remains a challenge, as the datasets collected by providers are often inaccessible. This case study presents a proof-of-concept that addresses this challenge by introducing an innovative web scraping approach to extract football event data and making it accessible e.g. for scientific research with LLMs. To this end, the extracted data is structured into coherent sentences for linguistic compatibility. The results show the successful integration of LLMs with football event data, enabling the extraction of information through retrieval-augmented generation. This work makes a first contribution to the field by bridging the gap between football and LLMs, demonstrating the potential for further analysis.

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Notes

  1. 1.

    Code and prompts are available online: https://ml-and-vis.org/football-query/.

  2. 2.

    https://understat.com/.

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Author information

Authors and Affiliations

  1. Aalen University of Applied Sciences, Beethovenstr. 1, 73430, Aalen, Germany

    Alexander Schilling, James Anurathan, Johannes Mühlberger, Felix Gerschner, Manfred Rössle, Andreas Theissler & Marco Klaiber

Authors
  1. Alexander Schilling
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  2. James Anurathan
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  3. Johannes Mühlberger
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  4. Felix Gerschner
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  5. Manfred Rössle
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  6. Andreas Theissler
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  7. Marco Klaiber
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Corresponding author

Correspondence to Marco Klaiber .

Editor information

Editors and Affiliations

  1. National University of Singapore, Singapore, Singapore

    Jin Song Dong

  2. Television Content Analytics Pte. Ltd., Singapore, Singapore

    Masoumeh Izadi

  3. Griffith University, Nathan, QLD, Australia

    Zhe Hou

A Appendix

A Appendix

1.1 A.1 Overview of the Different Prompts, Their Difficulty and Results

(See Table 2).

Table 2. All RetrievalQA Chain questions with reference to their answer figures
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1.2 A.2 Prompts and Answers of the Model

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Question 1 with RetrievalQA chain answer (Difficulty: easy)

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Question 2 with RetrievalQA chain answer (Difficulty: easy)

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Question 3 with RetrievalQA chain answer (Difficulty: easy)

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Question 4 with RetrievalQA chain answer (Difficulty: easy)

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Question 5 with RetrievalQA chain answer (Difficulty: hard)

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Question 6 with RetrievalQA chain answer (Difficulty: hard)

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Question 7 with RetrievalQA chain answer (Difficulty: hard)

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Question 8 with RetrievalQA chain answer (Difficulty: hard)

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Question 9 with RetrievalQA chain answer (Difficulty: hard)

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Question 10 with RetrievalQA chain answer (Difficulty: hard)

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Question 11 with RetrievalQA chain answer (Difficulty: hard)

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Question 12 with RetrievalQA chain answer (Difficulty: hard)

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Question 13 with RetrievalQA chain answer (Difficulty: hard)

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Question 14 with RetrievalQA chain answer (Difficulty: hard)

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Schilling, A. et al. (2024). Querying Football Matches for Event Data: Towards Using Large Language Models. In: Dong, J.S., Izadi, M., Hou, Z. (eds) Sports Analytics. ISACE 2024. Lecture Notes in Computer Science, vol 14794. Springer, Cham. https://doi.org/10.1007/978-3-031-69073-0_19

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  • DOI: https://doi.org/10.1007/978-3-031-69073-0_19

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  • Online ISBN: 978-3-031-69073-0

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