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Data-Driven Sports Science: Advances and Applications

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Special Issue Editors

Dr. Francisco García-Angulo

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Guest Editor

Faculty of Sports Sciences, University of Murcia, 30720 Santiago de la Ribera, Spain
Interests: sport; early stages; kinematics; competition; methodology; sensors

Prof. Dr. Francisco Ayala

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Guest Editor

Faculty of Sports Sciences, University of Murcia, 30720 Santiago de la Ribera, Spain
Interests: sports sciences; physical education; injury prevention; artificial intelligence; machine learning; movement competency

Dr. Antonio García-Angulo

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Guest Editor

Faculty of Sports Sciences, University of Murcia, 30720 Santiago de la Ribera, Spain
Interests: sports performance; soccer; observational methodology

Special Issue Information

Dear Colleagues,

The use of sensors in sports has provided information relevant for understanding human movement in the sporting arena. This information has been used to improve performance, optimise teaching and learning processes, recruit sporting talent, and develop healthy parameters, among other applications.

Against this background, this Special Issue aims to bring together research articles and reviews that analyse human movement in the field of sports. Topics will include the following:

Use of sensor systems in isolated contexts, training, and competition.
Application of new instruments for the assessment of human movement.
Kinematic and biomechanical characteristics of different sports practices.
Use of artificial intelligence for the classification of biomechanical patterns.
Use of sensors to adapt sports, identify anomalies, and predict injuries.
Effects of different biomechanical patterns and kinematic ranges on sports practice.
Quality of human movement and health.
Analysis, teaching, and sports performance based on movement.
Validation of sports movement analysis systems.
Evolution of movement patterns throughout sporting life.

Dr. Francisco García-Angulo
Prof. Dr. Francisco Ayala
Dr. Antonio García-Angulo
Guest Editors

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17 pages, 330 KB

Open AccessArticle

Decoding Positional Variability in U18 Semi-Professional Soccer Players: A Principal Component Analysis Utilizing Inertial Measurement Units to Identify Key Determinants

by José Carlos Barbero-Álvarez, José Antonio Sánchez Fuentes, Luis Manuel Martínez-Aranda, Filipe Manuel Clemente and Ana Filipa Silva

Appl. Sci. 2026, 16(5), 2596; https://doi.org/10.3390/app16052596 - 9 Mar 2026

Viewed by 372

Abstract

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. Full article

(This article belongs to the Special Issue Data-Driven Sports Science: Advances and Applications)

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