BACKGROUND the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. METHODS in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. RESULTS in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). CONCLUSION the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

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谁会得分？支持足球队建设和转会的机器学习方法

背景技术机器学习 (ML) 技术已在许多应用中实现，包括医疗保健、安全、娱乐和体育。在本文中，我们将介绍如何使用 ML 来构建职业足球队和规划球员转会。在这项研究中，我们定义了许多用于球员评估的参数，以及成功转会的三个定义。我们使用随机森林、朴素贝叶斯和 AdaBoost 算法来预测球员转会成功。我们使用真实的、公开可用的数据来训练和测试分类器。结果在文章中，我们提出了许多实验；它们在参数权重、成功转移定义和其他因素方面有所不同。我们报告了有希望的结果（准确度 = 0.82，精确度 = 0.84，召回率 = 0.82，F1-score = 0。83)。结论 本研究证明机器学习有助于职业足球队的建设。所提出的算法将在未来开发，并可能作为足球人才侦察的专业工具来实施。