Developing a Reliable Hybrid Machine Learning Model for Objective Soccer Player Valuation

Abstract: Football is both a popular sport and a big business. Managers are concerned about the important decisions that team managers make when it comes to player transfers, player valuation issues, and particularly the determination of market values and transfer fees. Market values are important because they can be thought of as estimates of transfer fees or prices that could be paid for a player on the transfer market. Football specialists have historically estimated the market. However, expert opinions are opaque and imprecise. Thus, data analytics may offer a reliable substitute or supplement to expert-based market value estimates. This paper suggests a quantitative, objective approach to value football players on the market. The technique is based on applying machine learning algorithms to football player performance data. To achieve this objective, Decision Tree Regression (DTR) was employed to predict the market value of football players. Additionally, two novel metaheuristic algorithms, Honey Badger Algorithm (HBA) and Jellyfish Search Optimizer (JSO), were utilized to enhance the performance of the DTR model. The experiment made use of FIFA 20 game data that was gathered from sofifa.com. In addition, it aims to examine the information and pinpoint the key elements influencing market value assessment. The trial results showed that the DTJS hybrid model performed better in predicting the participants' market pricing than other algorithms. With an R2 value of 0.984 and the lowest error ratio when compared to the baseline, it gets the highest accuracy score. Lastly, it is thought that these findings may be crucial in the discussions that occur between football teams and the agents of players. This strategy may be used as a springboard to expedite the negotiation process and provide a quantifiable, objective assessment of a player's market worth.

Hongtao Yu and Jialiang Li. “Developing a Reliable Hybrid Machine Learning Model for Objective Soccer Player Valuation”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150648

@article{Yu2024,
title = {Developing a Reliable Hybrid Machine Learning Model for Objective Soccer Player Valuation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150648},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150648},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Hongtao Yu and Jialiang Li}
}