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A novel machine learning method for estimating football players’ value in the transfer market

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Every year a huge amount of money is invested by the football clubs in the transfer window period to hire or release players. Estimating players’ value in the transfer market is a crucial task for the managers of the clubs. Also, it is one of the attractive aspects of football for fans. is a reference website that determines the transfer fee of the players based on its members’ opinions. The limitation of this website has attracted the attention of data scientists in recent years, resulting in creating datasets and data-driven estimating methods. In this paper, a novel method for estimating the value of players in the transfer market, based on the FIFA 20 dataset, is proposed. The proposed method has two phases. In the first phase, the dataset is clustered using an automatic clustering method called APSO-clustering. This automatic clustering method, which can detect the proper number of clusters, has divided the dataset into 4 clusters automatically indicating the position of the players: goalkeepers, midfielders, defenders, and strikers. In the second phase, a hybrid regression method which is a combination of particle swarm optimization (PSO) and support vector regression (SVR), is used to build a prediction model for each clusters’ data points. In this hybrid method, PSO is used for feature selection and parameter tuning of SVR. The achieved results show that the proposed method can estimate the players’ value with an accuracy of 74%. Comparing the performance of PSO with 3 other metaheuristics, the results demonstrated the superiority of PSO over GWO, IPO, and WOA.

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The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

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Authors and Affiliations



The main idea is first suggested by Dr. Seyed Mohammad Razavi, and it is developed and implemented by Iman Behravan (Corresponding author). Also, Dr. Razavi has contributed to the interpretation of the results. All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

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Correspondence to Iman Behravan.

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Conflict of interest

The authors declare that they have no conflict of interest.

Availability of data and material

The dataset used in this research (FIFA 20) is available on the Internet. Everyone can easily access the data.

Code availability

All the simulations are done in MATLAB R2019b which has several toolboxes including all of the algorithms used in this research specially SVR and PSO.

Additional information

Communicated by V. Loia.

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Behravan, I., Razavi, S.M. A novel machine learning method for estimating football players’ value in the transfer market. Soft Comput 25, 2499–2511 (2021).

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