Background/Objectives: Physical fitness in youth soccer impacts individual and team performance through the specific demands that must be met on the field. Therefore, this study aimed to characterize and identify youth soccer players with regard to the roles they play on the field. Material and Methods: A cross-sectional study was designed to characterize and identify the physical fitness levels of youth soccer players using previously validated measurement tests. A total of 36 players were evaluated (15 defenders and 24 attackers) using various physical fitness tests: Squat Jump (SJ), Countermovement Jump (CMJ), Single-leg Countermovement Jumps (SLCMJs), COD-Timer 5-0-5, Speed (5, 10, 15, and 20 m), Yo-Yo Intermittent Recovery Test Level I (YYIR1), and Running-Based Anaerobic Sprint Test (RAST). The data were confirmed using the Shapiro–Wilk test. Effect sizes were obtained using the Rank-Biserial coefficient, and, to identify the profiles of attackers and defenders, principal component analysis (PCA) was employed. Results: For the strength variables, attackers obtained better results than defenders in the variable flight time in the SJ (p = 0.03; R-b = −0.33) and contact time (%) in the SLCMJ test (p = 0.04; R-b = −0.33). Meanwhile, defenders achieved better results than attackers in the SLCMJ test for the variable flight time (%) (p = 0.01; R-b = 0.33) and breaking angle (A°) in the Nordic Hamstring (p = 0.01; R-b = 0.33). The results showed differences according to the players’ roles. Three principal components were identified for both attackers and defenders. The PC1 for attackers considered variables of strength, asymmetry, change of direction, and power. PC2 only considered strength and power variables. PC3 considered variables of strength, speed, endurance, and power. For defenders, PC1 considered strength, asymmetry, and power. PC2 analyzed variables of strength, asymmetry, change of direction and power. Finally, PC3 only grouped speed variables. Conclusions: Although youth soccer positions involve offensive and defensive roles, this study reveals differences in certain physical fitness variables. Therefore, it is necessary to tailor training tasks according to the specificity of the playing position, in line with the systems of play used and the predominance of the role that players occupy, whether in defense or attack.

Objective. To examine the scientific production of Small-Sided Games in soccer in the period from January 01, 2010 to February 19, 2024, through the consideration of a bibliometric study in the Web Of Science database. Methodology. To select the documents, an analysis matrix was elaborated in Microsoft Excel based on 15 categories. A review based on PRISMA recommendations was carried out in the database (Web of Science). The VOSviewer program was used to create co-occurrence maps based on keywords, co-authorship, country, institutional affiliation and journals. Results. A total of 568 documents were obtained. The results show that the greatest production is generated in research articles (92.42%). For the publication period, a systematic increase is established from the year 2018, finding that the year 2021 was the year of highest production with 84 documents. The authors with the highest production are Clemente, F.M. (10.17%), Praça, G.M. (6.25%), Sampaio, J., and Gonçalves, B. (5.53%). Regarding the area of knowledge, the one with the highest production is Sports Sciences with 398 documents (70.07%). The journal that has published the most papers and, in turn, receives the most citations (3374) is the Journal of Strength and Conditioning Research (44 papers and 2141 citations). The journals with the highest number of citations are those in the Q1 quartile. The Polytechnic Institute of Viana do Castelo is the institution with the highest number of papers (66). The prevailing language in the scientific production is English (93.66%). Conclusions. Small-Sided Games are a reason for interest in Sport Sciences, and, especially it has been studied in greater depth since 2017, with Spain and Portugal being the countries that have generated the greatest production. Key words: soccer, sport, sports science, small-sided-games, evaluation.

Introduction: Performance in weightlifting depends on the interaction between dynamic variables of movement and a highly specialized technique. Nevertheless, on the Colombian context there is limited biomechanical evidence that characterizes these factors in advance competitive athletes. Objective: Identify, describe and analyse the dynamic movement variables of the snatch in elite Colombian weightlifters in the 81 kilograms category, who participated in the XXII Juegos Nacionales 2023. Methodology: Based on the observational method supported by videographic technique, the attempts of 18 Colombian weightlifters were recorded employing WL Analysis v.2.9.9 app and calculating power, strength, speed and the barbell height. The statistical analysis was conducted with the SPSS Program, version 26, organizing the sample into quartiles on the basis of maximum load. Results: The weightlifters with the best results showed significantly higher values in power and strength, especially during the pulls. There were not found meaningful differences in kinematics variables as speed or barbell height. Discussion: The findings indicate a direct relationship between maximum force production and the capacity to perform heavier liftings, in agreement with previous research that associate the strength and power as determining causes of performance. The observed similarity in the kinematics variables suggests a high level of technical development among the competitors. Conclusion: The results demonstrate that the ability of generate maximum force represent a key factor in the snatch performance; while the technique is well stablished and is not decisive in that scenario.

Background. Recent advances in data systematization have enabled the development of machine learning models to evaluate performance in elite sports; however, studies are needed to analyze the performance of professional players in relation to their playing position. Objective. To analyze the performance of professional soccer players who competed between 2017 and 2024 by applying a multilevel classification approach that integrates different machine learning algorithms. Materials and Methods. We analyzed 9088 player-seasons from professional players during the 2017–2024 seasons. These data were extracted from standardized databases on sports performance analysis belonging to the following leagues: LaLiga (Spain), Premier League (England), Bundesliga (Germany), Serie A (Italy), and Ligue 1 (France). The final sample, distributed by performance level, was as follows: elite players (n = 1818; 20%), mid-level players (n = 2727; 30%), and low-level players (n = 4543; 50%). The average age of the players analyzed was 26.1 ± 4.0 years, distributed across three outfield playing positions: center backs (CB), central midfielders (CM), and strikers (ST); goalkeepers were excluded because the dataset contains no goalkeeper-specific performance metrics. Results. Under a leakage-controlled protocol (the label-defining indicators were excluded from the predictors and the train/test split preceded all preprocessing), a linear model (logistic regression) achieved the best overall performance (mean macro-F1 = 0.729), ahead of ensemble and kernel-based methods; this indicates that, once target leakage is removed, the classification does not require non-linear models. Strikers (ST) were the most separable position (best macro-F1 = 0.818, area under the receiver operating characteristic curve [AUC-ROC] = 0.948) and center backs (CB) the most difficult (macro-F1 = 0.602, AUC-ROC = 0.791), with midfielders (CM) intermediate (macro-F1 = 0.770, AUC-ROC = 0.916). Conclusions. The results confirmed that performance structures differ substantially depending on the position in the field, supporting the use of position-specific analytical strategies. In this context, the combination of position-stratified dimensionality reduction, handling of imbalance, and explainable artificial intelligence allowed for the identification of interpretable performance patterns associated with the profiles of elite, mid-level, and low-level players.

El objetivo de este artículo fue caracterizar la densidad competitiva de dos partidos de tenis modalidad sencillos de la fase eliminatoria de la Copa Davis 2017 disputados entre el equipo de Colombia (conformado por Santiago Giraldo y Alejandro Gonzalez) y Croacia (Marin Cilic y Franko Skugor). Se utilizó una rejilla de recolección de datos identificando la cantidad de puntos jugados, el marcador, el tipo de punto y la duración de cada uno de ellos para describir los tiempos totales y reales de los games y los sets; además, establecer cuál fue el tipo de punto predominante en cada encuentro y el que menos lo fue teniendo en cuenta cuales fueron los puntos, games y sets más largos y cortos en cada partido.