Hybrid Decision Support Model Integrating Decision Trees and AHP for Basketball Player Evaluation

Technological advances in data collection and processing methods have led to the development of sports analytics at the same pace. Although there are many studies on player performance indicators in the existing literature, there are still gaps in models that systematically determine which variables are really effective and subject players to a completely objective ranking. In this study, a hybrid framework combining Decision Trees and Analytic Hierarchy Process methods is proposed to address this gap. The model, which is tested using data from the 2024-2025 NBA season, first identifies the performance indicators that directly affect the match result with the Decision Trees algorithm and is then transferred to the Analytic Hierarchy Process for multi-criteria decision making. Analytic Hierarchy Process generates an importance ranking for each player. The main advantage of this method is that it provides a completely data-based weighting instead of the subjective expert opinion seen in classical Analytic Hierarchy Process applications. The results of the analyses show that the proposed model is effective in identifying success factors and produces more consistent player rankings than using the individual methods alone. The study aims to provide coaches and team managers with concrete and applicable results for strategic planning.