Academic literature on the topic 'Australian youth soccer players'

The current study aimed to investigate the performance characteristics that discriminate Australian youth soccer players according to their academy status. A total of 165 youth soccer players participated in this study and were sub-divided into either an early adolescence ( n = 92, age = 13.0 ± 0.6 years) or mid-adolescence ( n = 73 age = 14.8 ± 0.6 years) group. Players completed multifactorial assessments of anthropometry, motor competence, physical fitness, decision-making and psychological traits. Statistical significance was set at p ≤ 0.05. Multivariate analysis of variance identified dynamic balancing ability (both age groups), object manipulation (mid-adolescence), lateral jumping ability (both age groups), linear speed over 5 m (both age groups), change of direction skill (mid-adolescence), intermittent aerobic endurance (mid-adolescence) and total response time on a decision-making assessment (early adolescence) to discriminate academy status. Interestingly, a binomial logistical regression showed that a 0.1 s decrease in sprint time (i.e. running faster) increased the odds of a player belonging to a tier one academy by 19% and 47% for early and mid-adolescent players, respectively. Overall, performance in the motor competence and physical fitness assessments were in favour of the tier one academy players. These findings are indicative of a potential selection bias in the Australian talent pool or a training effect whereby tier one academy programmes emphasise the development of physical attributes. However, future research is required to further substantiate this in a larger sample of youth soccer players from other playing regions within Australia.

Objectives To determine whether the technical ability of Australian youth soccer players could distinguish between various playing levels. Design A cross-sectional observational design was used with each player required to complete four technical tests. Methods Sixty-two participants were representatives of three cohorts of youth soccer in Australia: national elite ( n = 18), state elite ( n = 22) and sub-elite ( n = 22). The technical tests used were Loughborough Short Passing Test (LSPT), long passing test (LPT), shooting test and speed dribbling, with all players familiarised with the tests prior to data collection. Differences between cohorts were analysed using a multiple analysis of variance test with follow-up analyses of variance and Tukey Honest Significant Difference post-hoc test, which were subsequently used to inform a sensitivity analysis, more specifically a bootstrapped receiver operating curve to determine cut-off scores for each variable. Results The national elite cohort scored better than state- and sub-elite cohorts on the LSPT, however, the state elite produced the fastest time before penalties. The sub-elite cohort scored less points on the LPT compared to both national- and state-elite cohorts, on both feet. In regards to speed dribbling, national-elite players were faster than both the state- and sub-elite cohorts. Shooting accuracy and velocity were able to discriminate the national- and sub-elite cohorts on the dominant foot, with shooting velocity on the nondominant foot being faster for the national elite compared to both the state- and sub-elite cohorts. Conclusions A number of differences in technical ability were identified between varying levels of Australian youth soccer players. Youth soccer coaches and sports scientists should use the cut-off scores for the technical tests in the talent identification and development process, with aspiring players aiming to reach these levels.

Objectives To determine whether Australian youth soccer players of varying levels could be distinguished based on their anthropometrical and physical attributes. Design A cross-sectional observational design was used, involving six anthropometrical and physical tests for each player. Methods Participants represented three youth levels of competition, namely national elite (n = 18), state elite (n = 22) and sub-elite (n = 22). Anthropometrical and physical tests included standing height; body mass; 5, 10, 30 m sprint and 20 m ‘flying start’ sprint; zig-zag agility test; vertical jump and Yo-Yo Intermittent Recovery test level 1. A multiple analysis of variance for the main effect of cohort, with a follow-up ANOVA and Tukey's Honest Significant Difference were used to discern which attributes differed between each cohort. Receiver operating characteristic curves were calculated, providing cut-off values between cohorts. Results The national elite cohort was significantly taller than the state elite cohort (ES = 0.94) and faster than the sub-elite athletes across 30 m (ES = 0.79) and 20 m with a flying start (ES = 0.77) (P < 0.05). The national elite cohort had a significantly higher level of intermittent endurance, compared to the state elite athletes who also performed better than the sub-elite cohort. The discrepancy between groups in the Yo-Yo Intermittent Recovery test level 1 was exemplified by the receiver operating characteristic with 94.1% of national elite players running further than 1980 m, while 95.7% of state elite and 100% of sub-elite players failed to reach this distance (ES = 0.88–1.77). Conclusions It is evident that anthropometrical and physical attributes differ between youth cohorts, particularly intermittent endurance. It is important to use this knowledge to enhance the current processes used to identify future talent for success in Australian soccer.

Objectives Football Federation Australia (FFA) has identified that Australian athletes are proficient physically, however often lack the technical and tactical skills to excel internationally. The aim of the current study was to assess if a video-based decision-making test could discriminate different age-matched skill levels of talent in Australian soccer. Design Cross-sectional observational. Methods Sixty-two youth male soccer players completed a video-based decision-making test. Results An ANOVA test showed that the video-based test significantly discriminated between all three groups, with the national elite athletes selecting more correct responses than the state elite (65.3 ± 8.1%; 56.0 ± 9.1%, respectively). The state elite were more accurate than the sub-elite (45.9 ± 8.8%). Conclusions Results suggest that a video-based test may be a suitable tool to use in the selection of athletes as a measure of decision-making skill. The low accuracy scores, even for the national elite cohort, suggest that decision-making skill at the youth level has room for improvement and should be prioritised as an area for development.

AbstractThe reliability and validity of maximal mean speed (MMS), maximal mean metabolic power (MMPmet), critical speed (CS) and critical metabolic power (CPmet) were examined throughout the 2016-2017 soccer National Youth League competitions. Global positioning system (GPS) data were collected from 20 sub-elite soccer players during a battery of maximal running tests and four home matches. A symmetric moving average algorithm was applied to the instantaneous velocity data using specific time windows (1, 5, 10, 60, 300 and 600 s) and peak values were identified. Additionally, CS and CP¬met values calculated from match data were compared to CS and CPmet values determined from previously validated field tests to assess the validity of match values. Intra-class correlation (one-way random absolute agreement) scores ranged from 0.577 to 0.902 for speed, and from 0.701 to 0.863 for metabolic power values. Coefficients of variation (CV) ranged from good to moderate for speed (4-6%) and metabolic power (4-8%). Only CS and CPmet values were significantly correlated (r = 0.842; 0.700) and not statistically different (p = 0.066; 0.271) to values obtained in a shuttle-running critical test. While the present findings identified match-derived MMS, MMPmet, CS and CPmet to be reliable, only CS and CPmet derived from match play were validated to a CS field test that required changes in speed and direction rather than continuous running. This suggests that both maximal mean and critical speed and metabolic power analyses could be alternatives to absolute distance and speed in the assessment of match running performance during competitive matches.

We aimed to analyze injury profiles and injury severity in Korean youth soccer players. Data on all injuries that occurred in U-15 youth soccer players during the 2019 season were collected from 681 players of 22 teams through a medical questionnaire. The questionnaire was based on injury surveillance procedures of the Federation International de Football Association Medical and Research Centre and International Olympic Committee, and it comprised questions on demographic characteristics, training conditions, and injury information. Among all players, defenders accounted for 33.0%, followed by attackers (30.7%), midfielders (26.8%), and goalkeepers (7.9%). Most players played soccer on artificial grounds (97.4%). Injuries occurred more frequently during training (56.3%) than during matches (43.7%). Recurrent injury rate was 4.4% and average days to return to full activities were 22.58. The ankle (26.6%) and knee joints (14.1%) were the most common injury locations, and ligament sprains (21.0%), contusions (15.6%), and fractures (13.9%) were the most frequent injury types. In conclusion, Korean youth soccer players have a high injury risk. Therefore, researchers and coaching staff need to consider these results as a key to prevent injuries in youth soccer players and injury prevention programs may help decrease injury rate by providing injury management.

Context Most researchers investigating soccer injuries have studied elite athletes because they have greater athletic-exposure hours than other athletes, but most youth participate at the recreational level. If risk factors for injury vary by soccer level, then recommendations generated using research with elite youth soccer players might not generalize to recreational players. Objective To examine injury risk factors of strength and jump biomechanics by soccer level in female youth athletes and to determine whether research recommendations based on elite youth athletes could be generalized to recreational players. Design Cross-sectional study. Setting Seattle Youth Soccer Association. Patients or Other Participants Female soccer players (N = 92) aged 11 to 14 years were recruited from 4 randomly selected elite (n = 50; age = 12.5 years, 95% confidence interval [95% CI]) = 12.3, 12.8 years; height = 157.8 cm, 95% CI = 155.2, 160.3 cm; mass = 49.9 kg, 95% CI = 47.3, 52.6 kg) and 4 randomly selected recreational (n = 42; age = 13.2 years, 95% CI = 13.0, 13.5 years; height = 161.1 cm, 95% CI = 159.2, 163.1 cm; mass = 50.6 kg, 95% CI = 48.3, 53.0 kg) soccer teams. Main Outcome Measure(s) Players completed a questionnaire about demographics, history of previous injury, and soccer experience. Physical therapists used dynamometry to measure hip strength (abduction, adduction, extension, flexion) and knee strength (flexion, extension) and Sportsmetrics to measure vertical jump height and jump biomechanics. We compared all measurements by soccer level using linear regression to adjust for age and mass. Results Elite players were similar to recreational players in all measures of hip and knee strength, vertical jump height, and normalized knee separation (a valgus estimate generated using Sportsmetrics). Conclusions Female elite youth players and recreational players had similar lower extremity strength and jump biomechanics. This suggests that recommendations generated from research with elite youth soccer players could be generalized to recreational players.

The hypothesis that sport-specific skill learning is correlated with earlier childhood multi-sport practice experiences was empirically studied among youth soccer players. Fourteen youth soccer coaches (38.1 ± 12.0 years) evaluated 100 youth players (11.8 ± 0.7 years) regarding their progress in soccer-specific skill learning through the course of a one-year season. The players completed a questionnaire recording their earlier and current participation in coach-led practice and youth-led play in soccer and in other sports. Reliability of the coach rating and of players’ reported sport activities ranged 0.83 ≤ rtt ≤ 1.00. Analyses revealed that the progress of the youth players in soccer-specific skill learning was not significantly correlated with their earlier or current amounts of coach-led soccer practice (–0.07 ≤ rs ≤ 0.07), youth-led soccer play (0.01 ≤ rs ≤ 0.08), or youth-led play in other sports (0.13 ≤ rs ≤ 0.22). Progress in soccer-specific skill learning was significantly correlated with the accumulated years and hours of earlier (but not current) coach-led practice in other sports (0.54 ≤ rs ≤ 0.57). A binary logistic regression accurately classified 83% of players with better and poorer learning progress based on earlier years and hours of practice in other sports. The observations suggest that earlier practice experiences in other sports had a lagged effect in interaction with later soccer practice and facilitated skill learning in soccer-specific practice.

Context: The incidence of injury for elite youth and professional adult soccer players is an important concern, but the risk factors for these groups are different. Objective: To summarize and compare the injury incidences and injury characteristics of male professional adult and elite youth soccer players. Data Sources: We searched MEDLINE and Web of Science using the search terms elite, international, European, soccer, football, injury, injuries, epidemiology, incidence, prevalence, not female, not American football, and not rugby. We also used the search terms professional for studies on professional adult soccer players and high-level, soccer academy, youth, adolescent, and young for studies on elite youth soccer players. Study Selection: Eligible studies were published in English, had a prospective cohort design, and had a minimum study period of 6 months. To ensure that injury data were assessed in relationship to the athlete's individual exposure, we included only studies that reported on injuries and documented exposure volume. Data Extraction: Two independent reviewers applied the selection criteria and assessed the quality of the studies. Data Synthesis: A total of 676 studies were retrieved from the literature search. Eighteen articles met the inclusion criteria: 6 for elite youth and 12 for professional adult soccer players. Conclusions: Injury rates were higher for matches than for training for both youth and adult players. Youth players had a higher incidence of training injuries than professionals. Efforts must be made to reduce the overall injury rate in matches. Therefore, preventive interventions, such as adequately enforcing rules and focusing on fair play, must be analyzed and developed to reduce match-related injury incidences. Reducing training injuries should be a particular focus for youth soccer players.

The purpose of this study was to develop a physiological profile of elite Australian Youth soccer players. Over three years, 150 players from the U'17, U'20 and U'23 national squads were tested for six measurements - height, weight, sum of eight skinfolds, vertical jump, maximum oxygen consumption and speed over twenty metres. Comparisons were made between those selected in the final team (classified as Successful) and those who failed to be selected (classified as Unsuccessful) to determine any significant differences between the two groups A physical and physiological profile was obtained for each player - expressed as a single value in both numerical and graphical formats. Players were ranked based on this score to determine significant differences between successful and unsuccessful players. Several significant differences (p<0.05) were found between Successful and Unsuccessful groups for a number of the variables primarily in the performance area rather than in the anthropometry parameters. For all squads, significant differences (P<0.05) were found between those who made the squad and those who did not when ranked based on their physical and physiological score. This study highlights the importance of the application of scientific testing to soccer Furthermore, it provides a system by which players' results can be analysed and ranked, and expressed in a format that provides the coach with immediate feedback as to an individual's specific strengths and weaknesses as a basis for training and team selection.

This thesis examined the physical development and match performance of elite youth academy soccer players some of whom were likely to progress to become professional soccer players. Physical characteristics such as standing height, body mass and estimated body fat composition, physical performance and match performance were explored. Furthermore, the relationships between physical performance and match running performance were examined in players from the U9 to U18 age group squads. Finally, the influence of biological maturity on physical characteristics, physical performance and match running performance in these elite youth soccer players was investigated and recommendations are made concerning talent identification and player development. One hundred and eighty-three elite soccer players (chronological age: 8.9 to 18.7 years; age grouping U9-U18) from an English Premier League Academy in the East Midlands were assessed for standing height, body mass, skinfolds, 30 m sprint, slalom and 505 agility, squat jump, counter movement jump with and without arms, Yo-Yo intermittent recovery test (level 1) and Multi-stage fitness test. All physical and performance variables measured in the study developed over time with chronological age except for the sum of 4 skinfold sites and estimated body fat composition (squad mean ± SD, U9 vs. U17: standing height, 139.4 ± 4.8 cm vs. 181.3 ± 5.6 cm; body mass, 33.6 ± 3.9 kg vs. 72.6 ± 5.7 kg; 30 m sprint, 5.26 ± 0.25 vs. 4.15 ± 0.11 s; slalom agility test, 4.83 ± 0.25 vs. 3.96 ± 0.09 s; counter movement jump with arms, 30 ± 3 cm vs. 48 ± 6 cm; the Yo-Yo intermittent recovery test (level 1), 787 ± 333 vs. 2617 ± 573 m). Standing height, body mass, 10, 15, and 30 m sprint times, performance on both agility tests, performance of squat jump and counter movement jump with arms; performance on the Yo-Yo intermittent recovery test (level 1) and on Multi-stage fitness test continued developing until the players reached the U17 squad. Moreover, the highest rate of development in standing height, body mass and all physical fitness tests occurred between the U9-U13 squads. Distance run during match play by 9 to 16 year old boys varied from 4056 (U9) to 7697 (U16) m per match (p < 0.05), and varied from 4675 to 6727 m·hour-1 of a match (p < 0.05). The U11-U16 squads covered a greater distance by high speed running (range: 487-553 mhour-1) compared to the U9 (178 m·hour-1) and U10 (219 m·hour-1) squads (p < 0.05 for all). Similarly, the percentage of time spent in high speed running by the U9 (1.1 %) and U10 (1.3 %) squads was less than that seen in the U11-U16 (2.6-3.0 %) squads (p < 0.05 for all). Chronological age accounted for 43% (p < 0.01), and the Multi-stage fitness test performance explained 7% (p < 0.05) of the variance in total distance covered per hour of a match in the U11-U16 group. Chronological age (p < 0.01) and the Multi-stage fitness test performance (p < 0.05) accounted for 10% and 11% respectively of the variance in percentage of time spent in moderate speed running. Chronological age accounted for 11 % of the variance in the percentage of time spent in high speed running (p < 0.01), whereas 30 m sprint and the Multi-stage fitness test performances explained 15% and 8% respectively of the variance in percentage of time spent in high speed running (p < 0.05 for both). The U9 and U10 squads showed a positive relationship between 20 m sprint time and distance covered in moderate speed running per hour of a match (r = 0.54, p < 0.05). In the U11-U13 squads relationships were evident between performance in 5, 10, 15, 20 and 30 m sprint (r = -0.67 to -0.46), the 3 standing vertical jumps (r = 0.46 to 0.73) and the 2 endurance tests (r = 0.45 to 0.60), and distance covered by moderate and high speed running per hour of a match (p < 0.05 for all). However, in the U14-U16 squads no significant relationships were evident. When stage of genital development was used to categorise players, standing height and body mass in the U12, U13 and U14 squads were positively influenced by biological maturity (p < 0.05 for all). The more mature players in the U13 squad also performed better in counter movement jump without arms and the Multi-stage fitness test (p < 0.05 for both). When stage of pubic hair development was used to categorise players, maturity status showed a positive influence on standing height and slalom agility test performance in the U12 squad (p < 0.05 for both) and on standing height and body mass in the U14 squad (p < 0.05 for both). When estimated chronological age at peak height velocity was used to categorise players, earlier maturing players were heavier (p < 0.01) and performed worse in counter movement jump without arms (p < 0.05) than later maturers in the U9 and U10 squads. Earlier maturers were taller (p < 0.01), heavier (p < 0.01) and possessed a thicker sum of 4 skinfold sites (p < 0.05) and higher estimated body fat (p < 0.01) compared to the later maturers in the U11 and U12 squads. Moreover, early maturers covered a greater distance than late maturers in the multi-stage fitness test (p < 0.05) in the U13 and U14 squads. In the U15 and U16 squads, early maturers were heavier and possessed thicker sum of 4 skinfold sites and higher estimated body fat compared to the late maturers (p < 0.01 for all). Furthermore, early maturers possessed a thicker sum of 4 skinfold sites (p < 0.05), higher estimated body fat (p < 0.01) and covered a shorter distance during the Yo-Yo intermittent recovery test (p < 0.01) compared to later maturers in the U17 and U18 squads. When stage of genital development was used to categorise players, the U12 and U13 players in stage 4 covered a greater distance in high speed running during a match than players in stage 3 (p < 0.05). There was a tendency for this still to be the case when distance was standardised into per hour of a match (p = 0.065). In the U9 and U10 squads, compared to later maturers, earlier maturers were given greater playing time during a match (p < 0.05), and consequently covered a greater distance during match play (p < 0.05). In the U13 and U14 squads, earlier maturers covered more distance per hour of a match and spent a higher percentage of time in high speed running when compared to their later maturing counterparts (p < 0.05 for both). In summary this research has provided the most extensive description yet of the physical characteristics, field test performance and match performance of elite youth soccer players. In addition, for the first time the effect of biological maturity (using 3 different methods of assessment) on a wide range of field tests and on match performance has been reported. The major changes in physical characteristics, field test performance and match performance between 10 and 14 years of age suggest that coaches should avoid as many selection decisions as possible during this age period, that they should take into account the fact that match distances covered at high speeds will be affected by maturity at these ages and that they should be aware that at present, coaches choose to give more mature players additional pitch time which obviously gives them an advantage in terms of playing development. An enhanced awareness of these findings in the coaching community could lead to an improved development and more appropriate selection decisions for elite youth soccer players in England.

Background: Football (Soccer) is arguably the most popular sport in the international sporting arena. A survey conducted by FIFA (Fédération International de Football Association) (FCPA, 2000) indicated that there are 240 million people who regularly play soccer around the world. Internationally, there are 300 000 clubs with approximately 1.5 million teams. In South Africa, there were 1.8 million registered soccer players in 2002/2003 (Alegi, 2004). Although youth players are predominantly amateurs and have no financial value for their clubs or schools, their continued health and safety are still of vital importance. There are some clubs which contract development players at 19 years of age in preparation for playing in their senior sides and these young players should be well looked after, to ensure a long career playing soccer. Being able to predict injuries and prevent them would be of great value to the soccer playing community. Aims: The main aim of this research was to create a statistical predictive equation combining biomechanics, balance and proprioception, plyometric strength ratios of ND/Bil (Non dominant leg plyometrics/ Bilateral plyometrics), D/Bil (Dominant leg plyometrics/ Bilateral plyometrics) and ND+D/Bil (Non dominant leg + dominant leg plyometrics/ Bilateral plyometrics) and previous injuries to determine a youth soccer player's risk of the occurrence of lower extremity injuries. In the process of reaching this aim it was necessary to record an epidemiological profile of youth soccer injuries over a two season period. It was also necessary to record a physical profile of, and draw comparisons between, school and club youth soccer players. Following the creation of the prediction model a preventative training programme was created for youth soccer players, addressing physical shortcomings identified with the model. Design: A prospective cohort study Subjects: Schoolboy players from two schools in the North West Province, as well as club players from three age groups were used for this study. Players from the U/16 and U/18 teams in the two schools were tested prior to the 2007 season. Players from the U/17, U/18 and U/19 club development teams were tested prior to the 2008 season. The combined total number of players in the teams amounted to 110 players. Method: The test battery consisted of a biomechanical evaluation, proprioceptive and plyometric testing and an injury history questionnaire. The Biomechanical evaluation was done according to the protocol compiled by Hattingh (2003). This evaluation was divided into five regions with a dysfunction score being given for each region. A single limb stance test was used to test proprioception. A Sergeant jump test was utilised using the wall mark method to test plyometric jumping height. A previous injury questionnaire was also completed on all players prior to testing. Test subjects from the schools were tested with the test battery prior to commencement of the 2007 season. The testing on the club teams was undertaken prior to the 2008 season. Injuries were recorded on the prescribed injury recording form by qualified Physiotherapists at weekly sports injury clinics at each of the involved schools and clubs. The coaching staff monitored exposure to training activities and match play on the prescribed recording forms. These training and match exposure hours were used, along with the recorded injuries for creating an epidemiological profile. Injuries were expressed as the amount of injuries per 1000 play hours. Logistical regression was done by using the test battery variables as independent variables and the variable injured/not injured as dependent variable (Statsoft, 2003). This analysis created prediction functions, determining which variables predict group membership of injured and non injured players. Results: There were 110 youth players involved in the research study from seven teams and four different age groups. There were two groups of U/16 players, an U/17 group, three U/18 groups and an U/19 group. The players were involved in a total of 7974 hours of exposure to training and match play during the seasons they were monitored. The average age of the players was 16.6 years. The majority of players were right limb dominant (83.6%) and 65.7% of players failed a single limb stance test. The mean jump height for both legs combined was 33.77cm, with mean heights of 22.60cm for dominant leg jump and 22.66cm for the non dominant leg. In the biomechanical evaluation of the lower leg and foot area, the average youth player presented with adaptation of toes, normal or flat medial foot arches, a normal or pronated rear foot in standing and lying and a normal or hypomobile mid-foot joint. Between 42.7% and 51.8% of players also presenting with decreased Achilles tendon suppleness and callusing of the transverse foot arch. The youth profile for the knee area indicated that the players presented with excessive tightness of the quadriceps muscles, normal patella tilt and squint, normal knee height, a normal Q-angle, a normal VMO: VL ratio and no previous injuries. This profile indicated very little dysfunction amongst youth players for the knee area. For the hip area, the youth profile was described as follows: There was shortening of hip external rotators, decreased Gluteal muscles length, normal hip internal rotation and no previous history of injury. Between 38.2% and 62.7% of players also exhibit shortened muscle length of the adductor and Iliopsoas muscles and decreased length of the ITB (Iliotibial Band). In the Lumbo-pelvic area there was an excessive anterior tilt of the pelvis with normal lumbar extension, side flexion, rotation and lumbar saggital view without presence of scoliosis. Between 58.18% and 65.45% of players presented with an abnormal coronal view and decreased lumbar flexion. Between 41.81% and 44.54% of players also presented with leg length, ASIS, PSIS, Cleft, Rami and sacral rhythm asymmetry. The similarity of the results for these tests in all players contributed to a new variable called 'SIJ dysfunction'. This was compiled from the average of the scores for Leg length, ASIS, PSIS, Cleft, Rami and Sacral rhythm, which was also considered for inclusion in the prediction model. The neurodynamic results of youth players indicated that approximately between 44.54% and 50.91% of players presented with decreased Straight leg raise and prone knee bend tests. The total combined dysfunction scores for the left and right sides were 17.091 and 17.909 respectively, indicating that there were higher levels of dysfunction on the right side than the left. This increased unilateral dysfunction could probably be attributed to limb dominance and increased use of the one leg for kicking and passing during the game. In the epidemiological study on youth players, there were a total of 49 training injuries and 52 match injuries. The total injury rate for youth players was 12.27 injuries/1000 hours, with a total match injury rate of 37.12 injuries/1000 match hours. The combined training injury rate was 7.17 injuries/1000 training hours. 87.13% of injuries were of the lower limb area and the individual areas with the highest percentage of injuries were the Ankle (25.74%), Knee (19.80%), Thigh (15.84%) and Lower leg (14.85%).The totals for youth players indicated that sprains (30.69% of total), strains (27.72% of total) and contusions (27.72% of total) were the most common causative mechanism of injuries. The severity of injuries show 'zero day' (no time off play) injuries to be the most common type (35.64%), followed by 'slight' (1 to 3 days off play) (33.66%) and 'minor' (4 to 7 days off play) (14.85%). School players had higher injury rates than club players but the severity of injuries to club players was higher, with longer absences from play. Non-contact injuries accounted for 52.47% of the total with 46.53% being contact injuries. School players had lower levels of non-contact injuries than club players, which correlated well with lower dysfunction scores recorded for school players during the biomechanical evaluations. This demonstrated that there was a definite relationship between levels of biomechanical dysfunction and the percentage of non-contact injuries in youth players, which formed the premise of the creation of a prediction model for non-contact youth soccer injuries. The next step in the creation of a prediction model was to identify the variables that discriminated maximally between injured and non-injured players. This was done using stepwise logistic regression analysis. After the analysis, ten variables with the largest odds ratios were selected for inclusion in the prediction model to predict non-contact injuries in youth soccer players. The prediction model created from the stepwise analysis presented as follows: P (injury)= exp(-8.2483 -1.2993a + 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g + 1.8273h-0.9460i-0.5193j) l + exp(-8.2483-1.2993a + 1.8418b+ 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g + 1.8273h-0.94601-0.5193J) a = Toe dysfunction b = Previous ankle injury c = Ankle dysfunction d = SIJ dysfunction e = Lumbar Extension f = Straight Leg Raise g = Psoas length h = Patella squint i = Gluteal muscle length j = Lumbar dysfunction P = probability of non contact injury exp(x) = e x , with e the constant 2.7183 In the ankle area, the toe positional test, previous ankle injury history and combined ankle dysfunction score were included in the prediction model. In the knee area, the patella squint test was included in the model. In the hip area, the Psoas component of the Thomas test was included, along with the Gluteal muscle length test. In the Lumbo-pelvic area, the SIJ dysfunction (average of Leg length, ASIS, PSIS, Rami, Cleft and Sacral rhythm tests), lumbar extension test and lumbar dysfunction scores were included in the prediction model. In the neurodynamic area, the Straight leg raise test was included in the prediction model. The prediction model therefore contained tests from all five the bio mechanical areas of the body. Overall, this model correctly predicted 86.91% of players as either injured or not-injured. The I value (effect size index for improvement over chance) of the prediction model (1=0.67), along with the sensitivity (65.52%), specificity (94.87%), overall correct percentage of prediction (86.91%) and Hosmer and Lemeshow interferential goodness-to-fit value (X 2(8) = 0.7204), all demonstrated this prediction model to be a valid and accurate prediction tool for non-contact youth soccer injuries A second prediction model, for the prediction of hip and groin injuries amongst youth players, was also created. The prediction model created from the stepwise analysis for groin injuries presents as follows: P (Groin injury)^ exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d+14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyometric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 The prediction model for hip and groin injuries included the variables of SIJ dysfunction, previous knee injury, previous hip injury, lumbar extension, straight leg raise, limb dominance and the ratio of non-dominant leg to bilateral legs plyometric height. When all the validifying tests were examined, the I-value (0.64868), sensitivity (66.67%), specificity (98.01%), false negatives (1.98%), false positives (33.33%), Hosmer and Lemeshow goodness-to-fit value (X2(8) = 0.77) and the overall percentage of correct prediction (96.26%) all reflected that this model was an accurate prediction tool for hip and groin injuries amongst youth soccer players. Conclusion: This study showed that it was possible to create a prediction model for non-contact youth soccer injuries based on a pre-season biomechanical, plyometric and proprioceptive evaluation along with a previous injury history questionnaire. This model appears as follows: P (injury)= exp(-8.2483 -1.2993a + 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f - 1.1566g + 1.8273h - 0.9460i - 0.5193J) l + exp(-8.2483-1.2993a+ 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g+1.8273h-0.94601-0.5193J) a = Toe dysfunction b=Previous ankle injury c = Ankle dysfunction d= SIJ dysfunction e=Lumbar Extension f = Straight Leg Raise g = Psoas length h = Patella squint i = Gluteal muscle length j = Lumbar dysfunction P = probability of non contact injury exp(x) = ex, with e the constant 2.7183 It was also possible to create a prediction model for non contact hip and groin injuries, which appears as follows: P (Groin injury)= exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyo metric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 It was also possible to create a prediction model for non contact hip and groin injuries, which appears as follows: P (Groin injury)= exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyo metric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 Using the hip and groin prediction model, combined with the injury prediction model, injuries in youth soccer players can be predicted. The data for each player should first be substituted into the injury prediction model, to determine the chance of getting injured during the season. The data should then be substituted into the hip and groin injury prediction model, determining the chance of hip and groin injuries during the season. The results from the groin injury prediction model could then be used to exclude groin injuries amongst players. A negative result for the hip and groin injury, which showed a false negative percentage of 1.98%, could be used to determine that an injury that was predicted using the overall injury prediction model, would not be a hip and groin injury. A positive result in the groin injury test could, however, not exclude injuries to other body areas that were predicted by the overall injury prediction model, so the groin injury prediction model could only be used to exclude hip and groin injuries.
Thesis (Ph.D. (Education)--North-West University, Potchefstroom Campus, 2009.

Sleep is a recurring state that is considered a critical process in the optimal attainment of daily functions and recovery in athletes. However individuals from elite sports, such as soccer, may be exposed to a number of situations that may impact sleep within the competitive season (such as inconsistent schedules and travel), which may result in sub-optimal sleeping patterns. However, research documenting the sleep of soccer players is at present limited. Therefore it would seem important to investigate how soccer players sleep to further the understanding of how sleep may be impacted. On this basis, the aim of the current thesis was to examine the typical sleeping patterns of youth soccer players and the factors effecting sleep. This was completed through a series of investigations conducted during the competitive youth soccer season. The aim of the first study (Chapter 3) was to validate a commercially available wireless sleep-monitoring device (WS). This was done in an attempt to provide a viable methodology to measure sleep within the habitual environment of soccer players. Nine randomly selected male participants were monitored over 3 nights and comparisons were made between the WS and other established field measures of sleep (Wristwatch actigraphy, sleep diary and Firstbeat bodyguard heart rate system). The relationships between the WS and the other sleep devices, indicated strong to very strong correlations (r > 0.6) and no significant differences between a range of outputs; total sleep time (Actigraphy assumed sleep time [0.97] & Sleep Diary [0.87] p > 0.05), sleep onset latency (Actigraphy [0.69] p > 0.05) and number of awakenings (Sleep Diary [0.69], p > 0.05). There were also low bias and narrow limits of agreement (LOA) within the comparison of mean differences with the WS for assumed sleep time (2 ± 17 min 95% LOA: -30 to 34 min [Actigraphy]), sleep onset latency (7 ± 17 min, 95% LOA -28 to 40 min [Actigraphy]), and number of awakenings (0.05 ± 1, 95% LOA -3 to 3 [Sleep Diary]). These results suggested that the WS is a viable device for the detection of these selected sleep variables. Chapter 4 looked to provide a comparison of sleep measures between a sample of youth soccer players (N=8) and non-athletes (N=8). Both groups were monitored over a period of 6 days within the habitual setting using the WS. The findings showed the soccer player group attained greater amounts of sleep quantity in comparison to the non-athlete group (504 ± 22 vs. 433 ± 46 min [+71 min] total sleep time, ES: 2.0, Large, p < 0.01). This may have been as a result of a later time of final awakening (08:54 ± 00:14 vs. 07:34 ± 00:46 [+77 min], ES: 1.7, Large, p < 0.01). Such an observation suggested that the soccer players were afforded greater time in bed as a result of the imposed soccer schedule. The soccer players also displayed a statistically greater time spent in wake (13(13) vs. 3(5) min [+10 min], PS: 0.86 ES: 1.5, Large, p < 0.05) on average each night. This data suggested that the sleep of the youth soccer players might be less efficient (as a consequence of greater levels of disturbance), despite desirable quantities of sleep being attained than non-athlete controls. Chapter 5 provided a daily comparison of sleep measures conducted over a 14-day assessment period. It is apparent that youth soccer players attained more sleep quantity in the nights preceding the match day (M-2: 488 ± 53 min [ES: 0.91, Moderate; p = 0.06] & M-1: 486 ± 64 min [ES: 0.84, Moderate; p = 0.02] respectively) in comparison to the night of the day after the match day (M+1: 422 ± 61 min). Such a finding suggested that youth soccer players adopt behaviours that reduce their sleep quantity on the designated recovery day (M+1) by >60 min, which may impact the recovery processes associated to this day. Relationships between sleep parameters and training and match load indicated a 100 au rise in RPELOAD (RPE * Duration) increased the time spent in wake (42 s [90% CI: 0 to 84 s]; ES: 0.36, Small; p = 0.098). It was also observed that an increase of 1000 m total distance increased the time spent in wake (40 s [90% CI: 5 to 75 s]; ES: 0.33, small; p = 0.06) A 100 m rise in high-speed running distance increased the number of awakenings observed (0.14 [90% CI: 0.03 to 0.25]; ES: 0.28, p =0.04) and the time spent in wake on average each night (1.5 min [90% CI: 0.78 to 2.3 min]; ES: 0.57, Small; p = 0.04). A similar outcome was observed in Chapter 6 were a 100 m rise in average high-speed running distance across three different 14-day training periods during the youth soccer season showed a 5 min increase in the time spent in wake on average (ES: 0.88, moderate; p = 0.04). Such outcomes provided a potential link between increases in training intensity (i.e. high-speed running distance) and sleep disturbances within youth soccer players. Increases in high-speed running distance also related to an increase of 24 min (90% CI: 12 to 36 min) on average for total sleep time (ES: 1.3, large; p < 0.01). Similarly increased high intensity heart rate (>85% max HR) was shown to effect total sleep time +20 min (90% CI: 6 to 32 min; ES: 0.87, moderate; p = 0.035). This may suggest that increases in intensity also may impact the amount of sleep quantity within youth soccer players. At present the mechanism for this response largely remains unknown. Within Chapter 7, a practical sleep hygiene strategy (10 min showering at ~40 °C, 20 min before time of lights out) was investigated. A group of ten youth soccer players were evaluated under normal sleeping conditions (control) and a shower intervention period, each consisting of three days within a randomized cross over trial design. Sleep information was collected using the WS. In addition to skin temperature, which was evaluated using iButton skin thermistors. The iButtons were used to establish both distal and proximal skin temperatures. This data was also used to create the distal to proximal gradient (average of distal measures – average of proximal measures = DPG). The data demonstrated that the shower intervention elevated distal skin temperature by (+1.1 °C [95% CI: 0.1 to 2.1 °C]; ES: 0.44, Small; p = 0.04) on average during a 10-minute period prior to lights out in comparison to the control condition. This elevation was also present during the first 30 minutes following lights out (1.0 °C [95% CI: 0.4 to 1.6 °C]; ES: 0.65, Moderate; p < 0.01), which was also accompanied by an increased DPG between conditions (0.7 °C [95% CI: 0.3 to 1.2 °C]; ES: 0.45, Small; p < 0.01). Additionally it was observed that on average the sleep onset latency of the youth soccer players was lower in the shower intervention condition (-7min [95% CI: -13 to -2 min]; ES -0.55, Moderate; p = 0.007). However no other sleep variable was affected as a result of the intervention. These findings demonstrate that a warm shower performed before lights out may offer a practical strategy to alter the thermoregulatory properties of distal skin temperature, which may advance sleep onset latency within youth soccer players.

As youth sport has become increasingly professionalized, many believe that the route to elite level play is through early specialization. Early specialization is a contentious issue, and many risk factors have been associated with high levels and intensities of training in youth. Youth today participate in highly competitive sport in pursuit of elite levels of play, recognition, and financial gain. Early specialization is thought to be a requirement for advancement, yet little is known about the early experiences of team sport athletes who grew up in the US. This is the story of 15 elite female athletes who “made it” to Division I soccer. The study offers us a window into the professionalized and commercialized world of youth soccer. It is a description of the childhood and adolescent journeys through sport and spans 10+ years of development. Through its telling, we learn about the expensive pay-to-play pipeline in soccer, we hear of the differences in opportunities that exist between social classes, and we confirm theories of expertise development. We learn that whether a young athlete specializes early or chooses to play multiple sports has little relevance to her progression to Division I. Through our thematic analysis of injury, we see how young athletes routinely play through injury, hide injury from coaches, and carry injury forward into their collegiate playing careers.

Background Organized sports offer an opportunity to promote physical activity and healthy eating. However, current data suggest that youth sports settings may not necessarily provide these benefits. In one study, youth were sedentary nearly half of a soccer match and in another foods and beverages offered at different youth sporting events were found to be energy-dense with little nutritional value. Parents, coaches and their respective sports organizations have the capacity to support a positive sports environment by promoting nutritious foods and beverages as well as optimal movement. To date, there is little research available on physical activity and sports trends of younger audiences, as well as perceptions of coaches and parents of young children toward sports snacks and policies to support healthy eating. The goal of these three studies were to better understand the youth soccer setting as an opportunity to address healthy eating and physical activity. Study 1 Objective: Assess snack offerings of parents and coaches of young soccer players, and policies. Methods: Beverage and Snack Questionnaires were distributed among all parents (n=120) and coaches (23) participating in recreational under five (U5) and under six (U6) soccer. The questionnaires assessed: socio-demographic information; types, as well as frequency, of snacks and beverages offered to children; reasons for snack and beverage choices; and attitudes toward snack policies. Results: Of the 44 parents and 23 coaches that participated, nutrition was ranked as the number one factor in choosing snacks and beverages for children participating in soccer. Yet parents and coaches reported offering many low-nutrient dense foods to their children as snacks. Coaches were receptive to limiting snack options and recommending healthy alternatives. Study 2 Objective: Observe snack offerings for young, recreational soccer players at combined practices and games to determine nutrient content and energy density of the foods and beverages provided. Methods: Snack observations for multiple, randomly selected teams were recorded using an observational checklist by trained researchers following all scheduled combined practices/games. Mean values across all snack foods and beverages were computed for the following key nutrients: calories, protein, fat, carbohydrates, fiber, sugars, and sodium. Results: Offered snacks were high in sugar, contributing nearly 77% of recommended total sugar intake per day, and low in sodium, fiber, and protein. Study 3 Objective: Determine the level of physical activity among young soccer players. Methods: Six random U5 and U6 teams were selected with 36 eligible players to participate in accelerometer collection data. Participants wore magnetic running pouches containing an accelerometer for a combined practice/game totaling 60 minutes. Informed, voluntary consent was obtained from each child and parent. Results: For the entire recorded session, average speed was 2.2 km/hour, average distance was 1.3 miles. Children were considered sedentary 55.0% of the recorded time. Discussion and Conclusions Organized sports settings offer an ideal avenue for promoting health and wellness among youth athletes. The current culture unfortunately promotes unhealthy snacking and sub-optimal physical activity. While the location and sample sizes limit generalizability, our results support research conducted with older children and highlight the importance of nutrition education for parents and coaches, as well as the potential for snack policies and strategies to encourage more vigorous physical activity in youth sports settings.
Master of Science

The collection of on-field, head impact data in soccer has been a challenge for researchers. Given the lack of headgear, the ability to instrument players, similar to what has been done in other sports [1, 2], is not possible. [3–5]. Laboratory recreations using reflective targets, EMG electrodes, and an instrumented bite plate have provided some insight into heading impact events [3, 5]. However, all of this instrumentation changes the ability of the player to move freely and, therefore, alters the dynamic of the impact. By developing a wireless acceleration measurement system which does not inhibit movement and provides no head protection, the linear and angular head accelerations can be measured during actual soccer play.

Purpose: Vitamin D is known to have a significant role in numerous body-system processes. Specifically, it has an impact on muscle functioning and, therefore sports performance. Chil-dren and adolescents have increased need for vitamin D because of its importance in growth and development, and it is evident that they are more susceptible to have vitamin D deficien-cy. Consequently, vitamin D status is particularly important issue in youth competitive sport. The aim of this study was to determine the prevalence of vitamin D deficiency/insuficiency (measured as 25(OH)D concentration), and the possible associations between vitamin D, with age, maturity status, sprinting- and jumping-performance among youth soccer players. Methods: The sample of participants in this research comprised 62 youth soccer players (age: 15.7 ± 2.2 years). They were divided into two categories according to 25(OH)D levels measured at the end of the winter season: group with inadequate levels of 25(OH)D (vitamin D deficiency/insuficiency [ 75 nmol/L]). Biological maturity status (maturity offset) was calculated from participants age and height by the following equation: Maturity offset = −7.999994 + (0.0036124 × (age(yrs.) × height(cm)). Performance variables were 10 meters sprint test (S10m) and countermovement jump test (CMJ). Results: Results showed relatively good 25(OH)D concentrations (78.32 ± 23.39 nmol/L), with prevalence of deficiency ( < 50 nmol/L) in 8.06%, and insuficiency (50–75 nmol/L) in 46.77% athletes. Significant correlations were evidenced between the CMJ and 25(OH)D level (R = 0.27, p < 0.05), but chronological age was also correlated with CMJ (R = 0.64, p < 0.05). Further, higher chronological age was found in participants with suficient vitamin D levels (15.1 ± 2.4 vs. 16.4 ± 1.6 years; t-test = 2.43, p < 0.05). However, no significant as-sociation was evidenced between vitamin D and maturity status. Conclusion: Vitamin D groups significantly differed by chronological age but not by maturity status, which collectively with correlation between CMJ and vitamin D status indicates that both vitamin D status and performance in youth soccer players is actually influenced by chronological age. Meanwhile, biological age doesn’t have a significant physiological influ-ence on vitamin D concentration, while some external factors (i.e. time spent outdoors, pa-rental control, sunscreen usage), should be considered important.

Purpose: Agility is an important determinant of success in football (soccer), but there is a lack of reliable and valid tests applicable in the evaluation of different agility components in youth football players. In this study we evaluated the reliability and factorial validity of the two newly developed tests of agility in male youth football players. Methods: The sample comprised 44 youth football players (all males, 14–15 years of age) who were tested on anthropometrics (body height and mass), newly developed tests of foot-ball specific reactive agility (FS-RAG) and change of direction speed (FS-CODS), one stand-ard test of CODS (20-yards), and sprinting over 20-m distance (S20M). The relative reliability is evaluated by calculation of Intra-Class-Correlation coeficients (ICC), while the absolute reliability was evaluated by calculation of the coeficient of variation (CV). Further, systematic bias was checked by analysis of variance for repeated measurements (ANOVA). The asso-ciations between studied variables were evidenced by Pearson’s correlation. Finally, factor analysis was calculated to define the factorial validity of agility tests (FS-RAG, FS-CODS, 20-yards). Results: The newly developed football-specific tests were found to be reliable, with better re-liability of FS-CODS (ICC: 0.81, CV: 6%), than of FS-RAG (ICC: 0.76, CV: 9%). The ANOVA evidenced significant (p < 0.05) learning effects for FS-RAG, but post-hoc analysis indicated stabilization of the results until the third testing trial. Factor analysis extracted one significant factor under the Guttmann-Kaiser criterion (Explained Variance: 1.67), showing the appro-priate factorial validity of newly developed tests in comparison to standard agility indicator 20-yards. Meanwhile, the significant correlations between all agility performances with S20M (Pearson’s R: 0.52–0.63; all p < 0.01) revealed that sprinting capacity significantly influence agility performances and that conditioning capacities of youth football players are not yet discriminated. Conclusion: Results showed appropriate reliability and validity of the newly developed tests of football specific change of direction speed and reactive agility. Therefore, here proposed FS-CODS and FS-RAG can be used as reliable and valid measures of agility components in youth football players. Further studies should evaluate the discriminative validity of the here proposed tests (i.e. identification of position-specific or performance-related differences), as well as reliability in younger players than those studied herein.