谁将得分？一种支持足球队建设与转会的机器学习方法。
Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers.
作者信息
Ćwiklinski Bartosz, Giełczyk Agata, Choraś Michał
机构信息
Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland.
出版信息
Entropy (Basel). 2021 Jan 10;23(1):90. doi: 10.3390/e23010090.
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.
背景
机器学习（ML）技术已在众多应用中得到应用，包括医疗保健、安全、娱乐和体育等领域。在本文中，我们展示了机器学习如何用于组建职业足球队以及规划球员转会。
方法
在本研究中，我们定义了众多用于球员评估的参数，以及三种成功转会的定义。我们使用随机森林、朴素贝叶斯和AdaBoost算法来预测球员转会的成功率。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
在本文中，我们展示了众多实验；这些实验在参数权重、成功转会定义和其他因素方面存在差异。我们报告了令人鼓舞的结果（准确率 = 0.82，精确率 = 0.84，召回率 = 0.82，F1分数 = 0.83）。
结论
所呈现的研究证明机器学习在职业足球队组建中可能会有所帮助。所提出的算法未来将得到进一步发展，并且可能会作为足球人才球探的专业工具得以应用。
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