The introduction of data-based modeling in football (soccer) in the last decade has led to the creation of models that describe player performance through key performance indicators (KPIs). However, relying solely on historical and current KPI values is insufficient for scouting departments, as predicting future values could significantly enhance transfer decision-making. This research aimed to identify the optimal model for forecasting the development of player performance KPIs over the next year, focusing on explainability, uncertainty quantification, and predictive performance.
To achieve this, we implemented linear models, tree-based models, and time- series-based kNN models to forecast two specific KPIs one year in the future: SciSkill, which measures the general quality of a player, and Estimated Transfer Value, representing the player’s monetary value. Tree-based models showed the best predictive performance. The random forest in particular emerged as the best due to its explainable predictions, uncertainty quantification method based on bagging, and good predictive performance. In the Sciskill case study, the random forest model achieved low loss values, especially for young players. For the Estimated Transfer Value, the random forest model demonstrated the best predictive performance on the general set of players, and specifically on the subset of players valued at over €10 million.
Our findings suggest that tree-based models, particularly the random forest, are well-suited for predicting the future development of football player perfor- mance KPIs. Although it is important to monitor the predictive performance using the most recent data, the insights and the resulting models of this research can enhance scouting decisions via both data-informed and data-based decision- making. Finally, this research paves the way to study the influence of time series information or contextual information on player performance metrics.