# Forecasting the future development in quality and value of professional football players

arXiv: https://arxiv.org/abs/2502.07528 · HTML: https://mlsanity.com/paper/2502.07528/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 58 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife](https://mlsanity.com/paper/1311.4555/) (2013)
- ["Why Would I Trust Your Numbers?" On the Explainability of Expected Values in Soccer](https://mlsanity.com/paper/2105.13778/) (2021)
- [Maximizing performance with an eye on the finances: a chance-constrained model for football transfer market decisions](https://mlsanity.com/paper/1911.04689/) (2019)
- [Valuing Players Over Time](https://mlsanity.com/paper/2209.03882/) (2022)

## Built on by (most cited first, 2 of 2)

- [Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football](https://mlsanity.com/paper/2603.15212/) (2026)
- [Bounded-Abstention Multi-horizon Time-series Forecasting](https://mlsanity.com/paper/2602.04714/) (2026)

## Abstract (arXiv)

Transfers in professional football (soccer) are risky investments because of the large transfer fees and high risks involved. Although data-driven models can be used to improve transfer decisions, existing models focus on describing players' historical progress, leaving their future performance unknown. Moreover, recent developments have called for the use of explainable models combined with uncertainty quantification of predictions. This paper assesses explainable machine learning models based on predictive accuracy and uncertainty quantification methods for the prediction of the future development in quality and transfer value of professional football players. The predictive accuracy is studied by training the models to predict the quality and value of players one year ahead. This is carried out by training them on two data sets containing data-driven indicators describing the player quality and player value in historical settings. In general, the random forest model is found to be the most suitable model because it provides accurate predictions as well as an uncertainty quantification method that naturally arises from the bagging procedure of the random forest model. Additionally, this research shows that the development of player performance contains nonlinear patterns and interactions between variables, and that time series information can provide useful information for the modeling of player performance metrics. The resulting models can help football clubs make more informed, data-driven transfer decisions by forecasting player quality and transfer value.