This study investigates the performance characteristics of U18 semi-professional soccer players by examining both technical load (TL) and physical load (PL) variables across various playing positions during the 2021/2022 Spanish Football U18 National League Championship. Methods: Principal Component Analysis (PCA) was employed to simplify the dataset, which comprised 246 match records from 49 athletes (mean age 17.9 ± 0.7 years; height ~177.6 ± 6.3 cm; body mass ~72.0 ± 7.2 kg) across ten matches. This analytical approach aimed to facilitate a deeper understanding of player performance dynamics. Results: Kaiser–Meyer–Olkin (KMO) values varied across positions (technical load: 0.20–0.93; physical load: 0.27–0.91). This indicates acceptable sampling adequacy for several positional models, but low adequacy for others; therefore, results for positions with low-KMO values should be interpreted cautiously. Factor analysis for both technical and physical load variables identified two components each, explaining substantial total variance (technical load: 63.75–86.65%; and physical load: 71.74–88.92% across position), with significantly high factor correlations (p < 0.001). The findings further indicate that players occupying defensive positions, such as goalkeepers and center-backs, generally exhibit lower levels of physical intensity and technical engagement compared to their counterparts in more dynamic roles, including full-backs, wingers, and forwards. The latter groups demonstrate higher involvement in high-intensity running and offensive actions. Conclusions: The observed performance patterns highlight the necessity for tailored training programs that align with the specific demands of each playing position. This approach is expected to optimize individual player performance and enhance overall tactical efficiency. Furthermore, the study underscores the importance of developing individualized conditioning strategies that address the unique physical and technical requirements inherent to each role on the field. This analytical approach using PCA provides a more structured and data-driven understanding of these positional differences, reinforming the need for tailored training programs and individualized conditioning strategies.
Barbero-Álvarez et al. (Mon,) studied this question.