用大模型把足球射门预测转化为教练能懂的口语化描述
Automated Explanation of Machine Learning Models of Footballing Actions in Words
- 用逻辑回归建预期进球模型，提取关键因素系数
- 基于系数生成描述性句子，再用大模型润色成生动解说
- 开源互动工具可实时解析大赛射门，适合教练与解说员使用
尽管足球分析已改变球队和分析师评估表现的方式，但机器学习成果与教练语言之间仍存在沟通鸿沟。教练需要可操作的洞察，而现有模型常无法提供。为此，我们提出一种名为「wordalization」的新方法，将足球射门的预测结果转化为自然语言描述。首先，利用逻辑回归构建预期进球模型；接着，根据回归系数生成描述各因素（如距离、角度、防守压力）如何影响预测的句子；最后，借助大语言模型对这些句子进行润色，生成富有娱乐性的射门解说。我们以模型卡片形式记录该方法，并提供一个交互式开源应用，用于解析近期赛事中的射门。讨论了射门语义化对教练沟通与足球解说的潜在帮助，并展示了该方法在其他足球动作上的可扩展性。
原文摘要 · Abstract (English)
While football analytics has changed the way teams and analysts assess performance, there remains a communication gap between machine learning practice and how coaching staff talk about football. Coaches and practitioners require actionable insights, which are not always provided by models. To bridge this gap, we show how to build wordalizations (a novel approach that leverages large language models) for shots in football. Specifically, we first build an expected goals model using logistic regression. We then use the co-efficients of this regression model to write sentences describing how factors (such as distance, angle and defensive pressure) contribute to the model's prediction. Finally, we use large language models to give an entertaining description of the shot. We describe our approach in a model card and provide an interactive open-source application describing shots in recent tournaments. We discuss how shot wordalisations might aid communication in coaching and football commentary, and give a further example of how the same approach can be applied to other actions in football.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。