Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques

Abstract

Football is a popular sport; however, it is a big business as well. From a managerial perspective, the important decisions that team managers make —Concerning player transfers, issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees— prices that could be paid for a player on the football market. Therefore, market values play an important role in transfer negotiations. The market has traditionally been estimated by football experts. However, expert judgments are inaccurate and not transparent. Data analytics may thus provide a sound alternative or a complementary approach to experts-based estimations of market value. In this study, we propose an objective quantitative method to determine football players’ market values. The method is based on the application of machine learning algorithms to the performance data of football players. The data used in the experiment are FIFA 20 video game data, collected from sofifa.com. We estimate players’ market values using four regression models that were tested on the full set of features—linear regression, multiple linear regression, decision trees, and random forests. Moreover, we seek to analyze the data and identify the most important factors affecting the determination of the market value. In the experimental results, random forest performed better than other algorithms for predicting the players’ market values. It has achieved the highest accuracy score and lowest error ratio compared to baseline. The results show that our methods are capable to address this task efficiently, surpassing the performance reported in previous works. Finally, we believe our results can play an important role in the negotiations that take place between football clubs and a player’s agents. This model can be used as a baseline to simplify the negotiation process and estimate a player’s market value in an objective quantitative way.

Keywords

• Player value prediction
• regression
• machine learning
• football analytics
• FIFA video game data