## 4 Discussion  
The objective of this study was to analyze the effects of small-sided games (STM1), strength training (STM2), and combined training (DTM) on various performance parameters in soccer players. To this end, machine learning models were employed to evaluate classification accuracy pre- and post-training. The results demonstrated that while all training interventions led to performance improvements, the most distinctive changes were observed in the DTM group. These findings suggest a significant influence of combined training on athletic performance. Small-sided match (STM1) training includes frequent acceleration, deceleration, and direction changes in accordance with the characteristics of football; therefore, it stimulates both aerobic and anaerobic energy systems and provides very sensitive physical development in players [29]. Although STM1 mainly aims to improve in-game changes and decision-making skills, it can have positive effects on muscle strength and neuromuscular coordination thanks to the high intensity of growth. Strength training (STM2) aims to develop maximal strength, power production, and muscle structure. This type of training can increase sprint performance and lower-extremity epic power [30]. However, since the effects of STM2 on the neuromuscular system can lead to similar significant adaptations to those of STM1, it is expected that similar results will be shown in some measurements in both groups [31]. In contrast, the combined training program (DTM) aims to combine the advantages of both methods and improve both statistical data and muscle strength and power simultaneously. In the literature, it has been stated that the harmonious integration of different training types can provide more comprehensive performance transfers compared to single-training methods [32,33]. Machine learning models emerged as a potent instrument for analyzing the impact of training techniques on players. Physical measurements obtained before and after applying three disparate training techniques revealed substantial changes in performance parameters. Among the four machine learning algorithms utilized in this study, the XG-Boost model achieved the highest accuracy, demonstrating its efficacy in classifying player performance. Traditional statistical tests are effective for group-level comparisons and are typically limited to identifying significant differences in mean values or distributions. On the contrary, machine learning (ML) provides individual-level predictive capability and identifies nonlinear, multivariate patterns across features that are not easily captured by univariate tests. Therefore, in our study, they allowed the prediction of class membership (e.g., STM1 vs. DTM) at the individual subject level, which is directly useful for screening applications. Additionally, feature importance analysis using the considered tree-based models revealed which input features most strongly influenced predictions and guided targeted interventions. These advantages go beyond traditional group comparisons and support the development of generalizable and interpretable decision models that could aid trainers. The classification analysis demonstrated that distinguishing DTM from STM1 and STM2 improved considerably, indicating that combined training induced a unique adaptation profile. Conversely, the differentiation between STM1 and STM2 remained relatively unchanged, suggesting a degree of overlap in their training effects. These findings are con sistent with previous studies emphasizing the benefits of multimodal training approaches over isolated methodologies in soccer players [34]. Concerning STM1, significant enhancements were observed in flexibility (sit-and-reach test), short-distance sprinting (10 m sprint without a ball, 20 m sprint with a ball), and anaerobic power (Winggate anaerobic test). These findings align with prior research suggesting that small-sided games enhance agility, anaerobic capacity, and specific movement patterns relevant to soccer [35].