Authors :
Shubh Harde; Vedant Bhawnani; Shruti Savant
Volume/Issue :
Volume 10 - 2025, Issue 3 - March
Google Scholar :
https://tinyurl.com/3e34ez7x
Scribd :
https://tinyurl.com/bdejxkmh
DOI :
https://doi.org/10.38124/ijisrt/25mar351
Google Scholar
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Abstract :
Football clubs from all around the world are in constant business of buying and selling players each transfer window.
These transactions of signing and selling players involve multi-million dollar deals. Therefore, it is essential for buyer clubs to
estimate the cost of acquiring the services of a player they have set their eyes upon before they make the decision of spending
millions of dollars for that particular player. This need has caught the attention of researchers, statisticians and enthusiasts of
the sport, which has led to the development of several techniques and platforms which predict the how much the player would
cost. Transfermarkt is one such platform which relies heavily on its community to decide market values of players. This
assessment is subjective and results in inconsistencies. As a result, there is a proliferation in the number of data-driven techniques
being developed to statistically predict price of players. In this paper, we review and compare such several data-driven
techniques for predicting player prices in the footballing world.
References :
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- McHale, Ian G. & Holmes, Benjamin, 2023. "Estimating transfer fees of professional footballers using advanced performance metrics and machine learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 389-399.
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- Yi, Qing et al. “Situational and Positional Effects on the Technical Variation of Players in the UEFA Champions League.” Frontiers in psychology vol. 11 1201. 19 Jun. 2020, doi:10.3389/fpsyg.2020.01201
- M. A. Al-Asadi and S. Tasdemır, "Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques," in IEEE Access, vol. 10, pp. 22631-22645, 2022, doi: 10.1109/ACCESS.2022.3154767.
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- Behravan I, Razavi SM. A novel machine learning method for estimating football players’ value in the transfer market. Soft Comput. 2021;25(3):2499–511. https://doi.org/10.1007/ s00500-020-05319-3.
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- 22 Data," 2022 IEEE 19th India Council International Conference (INDICON), Kochi, India, 2022, pp. 1-6, doi: 10.1109/INDICON56171.2022.10040117.
Football clubs from all around the world are in constant business of buying and selling players each transfer window.
These transactions of signing and selling players involve multi-million dollar deals. Therefore, it is essential for buyer clubs to
estimate the cost of acquiring the services of a player they have set their eyes upon before they make the decision of spending
millions of dollars for that particular player. This need has caught the attention of researchers, statisticians and enthusiasts of
the sport, which has led to the development of several techniques and platforms which predict the how much the player would
cost. Transfermarkt is one such platform which relies heavily on its community to decide market values of players. This
assessment is subjective and results in inconsistencies. As a result, there is a proliferation in the number of data-driven techniques
being developed to statistically predict price of players. In this paper, we review and compare such several data-driven
techniques for predicting player prices in the footballing world.