Pricing Football Players using Neural Networks

2017/11/16 by Sourya Dey, Dey, Sourya
Economics, Econometrics and Finance · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sports Analytics and Performance

paper · pdf · doi:10.48550/arxiv.1711.05865

openalex publication_date 2017/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We designed a multilayer perceptron neural network to predict the price of a football (soccer) player using data on more than 15,000 players from the football simulation video game FIFA 2017. The network was optimized by experimenting with different activation functions, number of neurons and layers, learning rate and its decay, Nesterov momentum based stochastic gradient descent, L2 regularization, and early stopping. Simultaneous exploration of various aspects of neural network training is performed and their trade-offs are investigated. Our final model achieves a top-5 accuracy of 87.2% among 119 pricing categories, and places any footballer within 6.32% of his actual price on average.

Related

• Machine Learning Modeling to Evaluate the Value of Football Players
• Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players
• Performance Insights-based AI-driven Football Transfer Fee Prediction
• Prediction of Football Player Value using Bayesian Ensemble Approach
• What should clubs monitor to predict future value of football players