Soccer Transfer Market Prediction Using Machine Learning Techniques

Student: Denis Korotkov

Faculty: International College of Economics and Finance

Educational Programme: Financial Economics (Master)

Year of Graduation: 2024

This paper is dedicated to the analysis of transfer value of football players. Football clubs have financial and institutional constraints which limit them in buying every player they want. Research question in this paper is connected with the main part of football: goals. Football is a goal-oriented game where both teams try to score. Analysing this part of the game may influence the transfer value the most. Such machine learning methods as GradientBoostingRegressor, SVR, DecisionTreeRegressor, RandomForestRegressor, ensemble.GradientBoostingRegressor were used. Moreover, our performance measure is the accuracy of the model. Accuracy can be used as a benchmark to compare different models and their performance. It allows for easy comparison between different algorithms and models to determine which one is more accurate.

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