Improving NHL draft outcome predictions using scouting reports

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-06-26 DOI:10.1515/jqas-2024-0047

Hubert Luo

引用次数: 0

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.

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利用球探报告改进国家冰球联盟选秀结果预测

我们利用大型语言模型（LLMs）从球探报告文本中提取信息，并改进对美国国家冰球联盟（NHL）选秀结果的预测。与此同时，我们根据球员在选秀前的场上表现得出统计特征。然后使用集合机器学习模型将这两个数据集结合起来。我们发现，冰上统计数据和球探报告都具有预测价值，但将它们结合起来会产生最强的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.