ABSTRACT

Research question

Our study aims to extend findings from previous efforts exploring the factors associated with transfer fees to and from all big five league clubs in European football (men) by building upon advances in machine learning, which allow to depart from linear functional forms. Furthermore, we provide a simple test of whether the transfer market has changed since the beginning of the COVID-19 pandemic.

Research methods

A fully flexible random forest estimator as well as generalized and quantile additive models are used to analyze smooth (non-linear) effects across different quantiles of scraped data (including remaining contract duration) from transfermarkt.de (n = 3,512). While we train our models with a randomly drawn subsample of before-COVID-19 transfers, we compare the prediction accuracy for two subsets of test data, that is, before and during COVID-19.

Results and findings

Since our findings suggest several non-linear predictors of transfer fees, moving beyond linearity is insightful and relevant. Moreover, our models trained with before-COVID-19 data significantly underestimate the actual transfer fees paid during COVID-19 particularly for high- and medium-priced players, thus questioning any cooling-off effect of the transfer market.

Implications

In the discussion of our findings, we showcase how moving beyond linearity and modeling quantiles can be revealing for both research and practice. We discuss limitations such as sample selection issues and provide directions for future research.

中文翻译：

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使用机器学习在 COVID-19 之前和期间预测职业欧洲足球的转会费

摘要

研究问题

我们的研究旨在通过机器学习的进步来扩展之前探索与欧洲足球（男子）所有五大联赛俱乐部之间的转会费相关因素的努力的结果，这允许偏离线性函数形式。此外，我们提供了一个简单的测试，看看自 COVID-19 大流行开始以来，转会市场是否发生了变化。

研究方法

完全灵活的随机森林估计器以及广义和分位数加性模型用于分析来自 transfermarkt.de ( n  = 3,512) 的不同分位数的抓取数据（包括剩余合同期限）的平滑（非线性）效应。当我们使用随机抽取的COVID-19 转移之前的子样本训练我们的模型时，我们比较了两个测试数据子集（即COVID-19之前和期间）的预测准确性。

结果和发现

由于我们的研究结果表明了转会费的几个非线性预测因素，因此超越线性是有见地且相关的。此外，我们使用COVID-19之前的数据训练的模型显着低估了 COVID-19期间支付的实际转会费，特别是对于高价和中价位球员，因此质疑转会市场的任何冷却效应。

启示

在讨论我们的发现时，我们展示了如何超越线性和建模分位数可以为研究和实践提供启示。我们讨论了样本选择问题等局限性，并为未来的研究提供了方向。