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Improving NHL draft outcome predictions using scouting reports

Author

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  • Luo Hubert

    (Department of Computer Science, 12330 University of Texas at Austin and Data Analytics Group , Lazard, Toronto, Canada)

Abstract

We leverage Large Language Models (LLMs) to extract information from scouting report texts and improve predictions of National Hockey League (NHL) draft outcomes. In parallel, we derive statistical features based on a player’s on-ice performance leading up to the draft. These two datasets are then combined using ensemble machine learning models. We find that both on-ice statistics and scouting reports have predictive value, however combining them leads to the strongest results.

Suggested Citation

  • Luo Hubert, 2024. "Improving NHL draft outcome predictions using scouting reports," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 20(4), pages 331-349.
    • Handle: RePEc:bpj:jqsprt:v:20:y:2024:i:4:p:331-349:n:1006
    DOI: 10.1515/jqas-2024-0047
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    Keywords

    machine learning; LLM; hockey;
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