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

arXiv: https://arxiv.org/abs/2401.16795 · HTML: https://mlsanity.com/paper/2401.16795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation](https://mlsanity.com/paper/1805.11088/) (2018)

## Built on by (most cited first, 1 of 1)

- [VALSE: Vertical Adaptive Layer Skipping for Efficient Inference in Large Language Models](https://mlsanity.com/paper/2610.07606/) (2026)

## Abstract (arXiv)

We developed an artificial intelligence approach to predict the transfer fee of a football player. This model can help clubs make better decisions about which players to buy and sell, which can lead to improved performance and increased club budgets. Having collected data on player performance, transfer fees, and other factors that might affect a player's value, we then used this data to train a machine learning model that can accurately predict a player's impact on the game. We further passed the obtained results as one of the features to the predictor of transfer fees. The model can help clubs identify players who are undervalued and who could be sold for a profit. It can also help clubs avoid overpaying for players. We believe that our model can be a valuable tool for football clubs. It can help them make better decisions about player recruitment and transfers.