Maturity-Associated Differences in Match Running Performance in Elite Male Youth Soccer Players

Is Biobanding the Future of Youth Sport Participation?

Traditionally, sports participation has been based on the chronological age of the individual with year of birth determining participation grouping. However, grouping by chronological age can result in individuals who are nearly a full year different in age competing within the same age group. Moreover, during the pubertal years, age grouping may provide physical (size) advantage to early maturers and disadvantage to late maturers. These advantages/disadvantages could impact talent selection, psychosocial aspects of sport participation, technical and tactical skill development, competitiveness, and injury risk. Biobanding is an alternative method for determining sport participation categorization and uses factors of growth and/or maturation, rather than traditional chronological age, for grouping athletes. Implementation of biobanding in sports may be advantageous to both early and late-maturing athlete development.

Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach

Introduction

A promising approach to optimizing recovery in youth football has been the use of machine learning (ML) models to predict recovery states and prevent mental fatigue. This research investigates the application of ML models in classifying male young football players aged under (U)15, U17, and U19 according to their recovery state. Weekly training load data were systematically monitored across three age groups throughout the initial month of the 2019-2020 competitive season, covering 18 training sessions and 120 observation instances. Outfield players were tracked using portable 18-Hz global positioning system (GPS) devices, while heart rate (HR) was measured using 1 Hz telemetry HR bands. The rating of perceived exertion (RPE 6-20) and total quality recovery (TQR 6-20) scores were employed to evaluate perceived exertion, internal training load, and recovery state, respectively. Data preprocessing involved handling missing values, normalization, and feature selection using correlation coefficients and a random forest (RF) classifier. Five ML algorithms [K-nearest neighbors (KNN), extreme gradient boosting (XGBoost), support vector machine (SVM), RF, and decision tree (DT)] were assessed for classification performance. The K-fold method was employed to cross-validate the ML outputs.

Results

A high accuracy for this ML classification model (73-100%) was verified. The feature selection highlighted critical variables, and we implemented the ML algorithms considering a panel of 9 variables (U15, U19, body mass, accelerations, decelerations, training weeks, sprint distance, and RPE). These features were included according to their percentage of importance (3-18%). The results were cross-validated with good accuracy across 5-fold (79%).

Conclusion

The five ML models, in combination with weekly data, demonstrated the efficacy of wearable device-collected features as an efficient combination in predicting football players' recovery states.

The Relative Age Effect across an International Soccer Programme in Male and Female Players Aged 12 Years Old to Seniors

The purpose of the study was to examine the prevalence of the Relative Age Effect (RAE) across an international soccer programme in male and female players aged 12 years old to seniors. One hundred forty-five male (age: 18.8 ± 4.6 years; body mass: 68.1 ± 10.2 kg; body height: 177.3 ± 10.5 cm) and 218 female (age: 15.9 ± 4.6 years; body mass: 66.2 ± 10.5 kg; body height: 170.6 ± 8.3 cm) players from a National Association were assessed. All participants were divided into four quartiles: January to March (BQ1), April to June (BQ2), July to September (BQ3), and October to December (BQ4). The results showed that the distributions for all male squads were significantly skewed, with more players than expected from BQ1 in the U-21, U-19, and U-17 and less players than expected from BQ4 in the U-19 squad. The distributions for all female squads showed significantly more players than expected from BQ1 in the U-16 and less players than expected from BQ4 in the U-14 squad. The distributions across the different positions for the male squads combined were significantly skewed with more forward players than expected from BQ1 and less forward and midfielder players than expected from BQ4. For the female squads, there were significantly more BQ1 defenders from the U-16 squad than expected. In conclusion, this study unveils significant disparities in quartile distributions among male and female squads. Moreover, the data emphasize the potential impact of heightened physical demands in certain positions on the RAE.

Exploring the existence, strength, and independence of relative age and maturation selection biases: a case study in Gaelic football talent development programmes

BACKGROUND

Biological maturity and relative age player selection biases are well documented in youth sports. However, there has been limited examination of the relationship between these biases.

AIM

This study investigated the presence, strength, and independence of relative age and biological maturity selection biases in Gaelic football.

SUBJECTS AND METHODS

A total of 247 male players from U14 to U16, from two talent academies were assessed for relative age (decimal age (DA)) and biological maturity (discrepancy between biological and chronological age (BA-CA)).

RESULTS

Relative age effects (RAE) were observed in the U14 (DA = 0.62, d = 0.40) and U15 squads (DA = 0.57. d = 0.26) only. A bias towards advanced maturity status was present at U14 (BA-CA = 0.60, d = 0.83), U15 (BA-CA = 0.78, d = 0.89), and U16 (BA-CA, d = 1.01). There was a trivial (U14, r(83) = -0.210; U15, r(88) = 0.060) and low (U16, r(76) = 0.352) correlation between relative age and maturity status.

CONCLUSION

Substantial maturity selection biases and, to a lesser degree, relative age biases are evident in youth Gaelic football. Critically, these biases are independent constructs. Coaches and policy makers should be educated on the distinct influences of relative age and maturation, and on strategies to address these biases.

Does maturity estimation, 2D:4D and training load measures explain physical fitness changes of youth football players?

OBJECTIVES

The purpose of the present study was two-fold: (1) To analyse physical fitness changes of youth football players after a full-season; and (2) to examine whether physical fitness changes are explainable by estimated maturity status, 2digit:4digit ratio (2D:4D) from each hand and training load (TL) measures.

METHODS

Twenty-seven youth elite Under-15 football players were daily monitored for training load measures during 38 weeks. At the beginning and at the end of the season, all players were assessed for physical fitness. Also, the maturity status estimation and the length of the second and fourth digits of both hands were collected at the beginning of the season.

RESULTS

Significant differences were found for all physical fitness measures after the season. The second and fourth digits of left and right hands had negative moderate correlations with change of direction (COD) changes (r=-.39 to - 0.45 | p = .05 to 0.02). Also, the maturity offset measure had negative moderate correlations with COD changes (r=-.40 | p = .04). From the reported significant correlations, the maturity offset, Left 4D, Right 2D and Right 4D significantly predicted the Mod.505 COD test changes (β = 0.41, p = .04; β = -0.41, p = .04; β = -0.45, p = .02; and β = -0.44, p = .03, respectively).

CONCLUSION

The maturity offset and the 2D:4D measures have the potential to predict COD performance changes over-time in youth football players. Given the lack of associations between the maturity estimation, 2D:4D and training load measures, with the overall physical fitness measures, coaches should rely only at COD changes.

Growth, maturation and injuries in high-level youth football (soccer): A mini review

Understanding the challenges football (soccer) players face during adolescence is fundamental to avoid disruptions in their development due to injury. This mini review will describe basic concepts of somatic growth and biological maturity, examine data from 53 prospective epidemiological studies on high-level youth football players and discuss how age, growth and maturity may affect the injury patterns observed. Based on the existing evidence, at least every third player sustains an injury during a football season. The thigh (median for studies of boys: 25%, median for girls: 21%), ankle (b: 18%, g: 30%), knee (b: 17%, g: 18%) and hip/groin (b: 14%, g: 10%) are the body parts injured most often, while muscle strains (b: 31%, g: 25%), sprains (b: 20%, g: 27%) and contusions (b: 17%, g: 16%) are the most common injury types. Injury trends are, however, not consistent throughout adolescence, and players' age, maturity status and position relative to peak height velocity (PHV) have shown to influence the number, type and location of injuries sustained. Despite a high volume of observational injury studies published on high-level youth players, girls (7 studies) and settings outside of Europe (included in 23% of studies) are underrepresented and should receive extra attention in the future. Based on the available epidemiological data, tailored injury reduction programmes can be considered in youth football, alongside application of general training principles such as progression, variation and individualization which may be especially important during vulnerable phases such as the adolescent growth spurt.

Associations between biological maturity level, match locomotion, and physical capacities in youth male soccer players

INTRODUCTION

Biological maturity level has shown to affect sport performance in youths. However, most previous studies have used noninvasive methods to estimate maturity level. Thus, the main aim of the present study was to investigate the association between skeletal age (SA) as a measure of biological maturation level, match locomotion, and physical capacity in male youth soccer players.

METHOD

Thirty-eight Norwegian players were followed during two consecutive seasons (U14 and U15). Match locomotion was assessed with GPS-tracking in matches. SA, assessed by x-ray, physical capacities (speed, strength and endurance) and anthropometrics were measured in the middle of each season. Analysis of associations between SA, match locomotion, and physical capacities were adjusted for the potential confounding effect of body height and weight.

RESULTS

In matches, positive associations were found between SA and maximal speed and running distance in the highest speed zones. Further, SA was associated with 40 m sprint time and countermovement jump (CMJ) height, and with intermittent-endurance capacity after adjusting for body height (U14). Associations between SA and leg strength and power, and between SA and absolute VO_2max were not significant after adjusting for body weight. There was no association between SA and total distance covered in matches.

CONCLUSION

Biological maturity level influence match locomotion and performance on physical capacity tests. It is important that players, parents and coaches are aware of the advantages more mature players have during puberty, and that less mature players also are given attention, appropriate training and match competition to ensure proper development.

Training Management of the Elite Adolescent Soccer Player throughout Maturation

Professional soccer clubs invest significantly into the development of their academy prospects with the hopes of producing elite players. Talented youngsters in elite development systems are exposed to high amounts of sports-specific practise with the aims of developing the foundational skills underpinning the capabilities needed to excel in the game. Yet large disparities in maturation status, growth-related issues, and highly-specialised sport practise predisposes these elite youth soccer players to an increased injury risk. However, practitioners may scaffold a performance monitoring and injury surveillance framework over an academy to facilitate data-informed training decisions that may not only mitigate this inherent injury risk, but also enhance athletic performance. Constant communication between members of the multi-disciplinary team enables context to build around an individual's training status and risk profile, and ensures that a progressive, varied, and bespoke training programme is provided at all stages of development to maximise athletic potential.