# Performance Insights-based AI-driven Football Transfer Fee Prediction

**Authors:** Daniil Sulimov  
**arXiv:** [2401.16795](https://arxiv.org/abs/2401.16795)  
**Date:** 2024-01-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.16795v1/

## Key points

- Daniil Sulimov introduces an AI approach for football transfer fee prediction
- Model assists clubs in making informed decisions on player acquisitions and sales
- Machine learning model predicts player impact based on performance data
- Helps identify undervalued players and avoid overpaying for talent
- Valuable tool for optimizing player recruitment and transfer strategies in football clubs

## Summary

In their paper titled "Performance Insights-based AI-driven Football Transfer Fee Prediction," Daniil Sulimov introduces an artificial intelligence approach to accurately forecast the transfer fee of football players. The authors' model aims to assist clubs in making informed decisions regarding player acquisitions and sales, ultimately leading to enhanced performance and increased club budgets. By leveraging data on player performance, transfer fees, and various factors influencing a player's value, the researchers trained a machine learning model capable of accurately predicting a player's impact on the game. The obtained results were incorporated as features into the transfer fee predictor, enabling clubs to identify undervalued players with potential for profitable sales while also avoiding overpaying for talent. This proposed model serves as a valuable tool for football clubs seeking to optimize their player recruitment and transfer strategies through its predictive capabilities. With this AI-driven solution at their disposal, clubs can make strategic decisions that positively impact their overall performance and financial outcomes in the competitive landscape of professional football.