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The relative age effect and physical fitness characteristics in German male tennis players.

Introduction

The identification, selection, and development of talented tennis players at early ages can have significant social, professional, and financial implications for the players and their families (Abbott and Collins, 2004; Roetert and Ellenbecker, 2007; Vaeyens et al., 2008). A talented young tennis player can be considered as someone whose performance is better and/or increasing faster than his or her peers at training and competition (Elferink-Gemser et al., 2004). Typically, those players are surrounded by the social and material environmental conditions that increase the likelihood of them reaching the elite level (Hohmann, 2001).

Like most competitive sports, youth tennis competition is divided into age categories based on chronological age as defined by a player's date of birth (Helsen et al., 2005).

In tennis and most sports, the different competition age groups are organized by grouping players born within the same 12-month period. Besides these "within one year" groupings, other age-groupings (e.g. annual age grouping and multiyear age bands) used in sport have been shown to generate different effects (e.g. within-year effects, constituent year effects and constant year effects). It is important to differentiate in-between the outcomes (Schorer et al., 2013), although regardless of the group system used, these cut-off dates are commonly used with the goal of reducing maturational differences and create homogenous competition groups allowing a more sensible coaching and evaluation of the athletes as well as to ensure that there is an equal chance of success and fair competition for all players in youth sports (Helsen et al., 2005; Musch and Grondin, 2001; Schorer et al., 2011).

However, research in different sports has found that athletes born early within the selection year are more likely to be selected for elite teams and talent development programs than those born later in the same year (Augste and Lames, 2011; Delorme and Raspaud, 2009; (Helsen et al., 2012; Mujika et al., 2009). This overrepresentation of relatively older athletes in youth sport has been labeled as the relative age effect (RAE) (Barnsley et al., 1985). Previous studies in tennis documented the existence of RAEs, with a skewed birth date distribution in top junior players as well as in senior players, with more players involved in elite development programs born in the first half of the calendar year (e.g., values ranging from 60 to 86%) than in the second half (Baxter-Jones et al., 1995; Dudink, 1994; Edgar and O'Donoghue, 2005b; Filipcic, 2001; Loffing et al., 2010). While the existence of RAEs has been associated with a loss of potential talent, eliminating these effects has proven challenging (Musch and Grondin, 2001). This appears to be related to difficulties in identifying potential causal mechanisms. One of the most recurrent suggested mechanisms is selection bias. That is, coaches mistakenly grant fewer opportunities (e.g., instruction, access to elite group or team) to relatively younger individuals than should be warranted by their latent ability or talent (Deaner et al., 2013).

In Germany, young talented tennis players are scouted and recruited to join regional and national training centers by coaches who are affiliated to the German Tennis Federation (DTB). Players are selected within the same 12-month period (from January 1st to December 31st) based on a repetitive observation of the players' technical/tactical abilities as well as their competitive performance. While selection bias is likely to be mediated by several interacting mechanisms, maturational factors are often the focus of study (Musch and Grondin, 2001). This maturational hypothesis is based on the large inter-individual biological differences within the same chronological age groups during childhood and adolescence assuming that players born close to the selection date profit from their advanced physical and cognitive maturation (Baxter-Jones and Sherar, 2007). Even small age differences (i.e., months) within an annual age-group can provide substantial advantage in physical and cognitive maturity (Baxter-Jones et al., 1995). In a sport like tennis, in which height, strength, speed, and power are important performance factors (Fernandez-Fernandez et al., 2009), relatively older children are more likely to dominate youth tennis, be identified as "more talented" and be selected to be part of elite teams (Baxter-Jones et al., 1995; Edgar and O'Donoghue, 2005a). It is therefore possible that in order to remain competitive and have chances to be selected for the next levels of talent development, relatively younger players within their age group have to match either anthropometrics and/or physical fitness performances of the older players. However, an investigation into the link between anthropometrics, physical fitness, and RAEs in young tennis players has not yet been conducted.

Therefore, the aims of the present study were: 1) to test the existence of RAE in young German male tennis players, 2) to examine if the potential RAE was influenced by age and/or skill level and 3) to investigate whether players who were born later in the selection year and were still selected into the elite squads were likely to be similar across a range of anthropometric and fitness attributes compared with those born earlier in the year.

Methods

Study design

Between the years 2009 and 2011, a sample of the 348 best male young players in Germany (from the national and regional selection groups) was evaluated using a battery of standard anthropometric and physical performance tests implemented by the DTB at the national level. Players were recruited from their respective regional federations and all federations in the country were tested. For the purpose of the present study, the players were grouped on the basis of chronological age into 1-year age categories (i.e., from January 1st to December 31st). The cohort spanned 6 years and included U12 (n = 70), U13 (n = 96), U14 (n = 57), U15 (n = 57), U16 (n = 32), U17 (n = 36) male tennis players. The players and parents were informed of all experimental procedures and written informed consent was completed before participation. The study was approved by the institutional research ethics committee and conformed to the recommendations of the Declaration of Helsinki (World Medical, 2013).

Participants

To assess the prevalence of RAEs in tennis, a substantial data set had to be collected from different sources. The birth dates of all male players affiliated with the German Tennis Federation (DTB) born between 1992 and 2000 (11 to 17 years old) (n = 120,851) were analyzed and this group was labeled as "licensed players". Among all these players, various subgroups were subsequently made and retained for further analyses (Table 1). The first subgroup, defined as "ranked players", included all male players with official ranking in the German youth ranking list (players aged 11-17 years old, n = 7,165). The second subgroup, defined as the "regional squad", was made up of the most talented players in each region (up to 30 players per region, aged 11 to 17 years old), selected by the regional federations coaching staff based on their technical/tactical abilities and competitive performance (n = 381). A third subgroup, defined as the "national squad", was drawn from the best of the 381 regional players (previous group), selected by the national federation coaching staff based on their technical/tactical abilities and competitive performance (n = 57, from 11 to 17 years old). In addition, the birth dates of the first 50 senior players of the national ranking (i.e., including the Davis Cup squad) were collected from the DTB database. Moreover, the birth distribution of the whole male German population born between 1992 and 2000 was extracted from the Federal Statistical Office ("Statistische Bundesamt") (https://www.destatis.de/DE/Startseite.html).

Procedures

All testing was completed in a three-week period, beginning at the end of September each year. Test sessions were undertaken between 14:00 and 20:30h and the players were assessed at their respective federation training centers. To ensure standardization of test administration across the entire study period, all tests were carried out in the same order and using the same testing devices and operators. All fitness tests were performed in an indoor tennis court (Rebound Ace surface). Testing began after a 15-min individual warm-up, which consisted of low-intensity forward, sideways, and backwards running, general dynamic mobility, multi-directional acceleration runs, skipping, and hopping exercises, and jumps of in creasing intensity. The following physical performance tests were conducted.

Anthropometry. Sessions started with the measurement of players' body dimensions, which included body height, body mass, and sitting height. Body height was measured with a fixed stadiometer ([+ or -] 0.1 cm, Holtain Ltd., Crosswell, UK), sitting height with a purpose-built table ([+ or -] 0.1 cm, Holtain Ltd., Crosswell, UK), body mass with a digital scale ([+ or -] 0.1 kg, ADE Electronic Column Scales, Hamburg, Germany). For the prediction of the age of peak linear growth according to Mirwald et al. (2002), leg length was estimated by subtracting sitting height from body height.

Maturity Status. Pubertal timing was estimated according to the biological age of maturity of each individual as described by Mirwald et al. (2002). The age of peak linear growth (age at peak height velocity) is an indicator of somatic maturity representing the time of maximum growth in stature during adolescence (Mirwald et al., 2002). Biological age of maturity (years) was calculated by subtracting the chronological age at the time of measurement from the chronological peak-velocity age (Baxter-Jones and Sherar, 2007, Mirwald et al., 2002). Thus, a maturity age of -1.0 indicates that the player was measured 1 year before this peak velocity; a maturity of 0 indicates that the player was measured at the time of this peak velocity; and a maturity age of +1.0 indicates that the participant was measured 1 year after this peak velocity (Mendez-Villanueva et al., 2010).

RAE. To determine the existence of RAEs, player birth dates were firstly recorded to reflect their birth quartile (Q), according to the dates used for creating annual age groups. The cut-off date for the selection in German Tennis is January 1st and participants were divided into one of four groups. Therefore, Q1= players born in January, February and March; Q2 = players born April, May and June; Q3 = players born in July, August and September; and Q4 = players born in October, November and December.

Grip strength. Handgrip strength was measured using a hydraulic hand dynamometer (Baseline [R]; Irvington, NY). The player was asked to perform a maximal voluntary contraction, standing with the dynamometer at one side (i.e., dominant hand) and gripping the dynamometer as hard as they could, for 3 s. This was repeated for each hand (i.e., dominant and non-dominant hand). The average of the 2 trials for each hand was considered to be the maximum voluntary handgrip strength (Innes, 2002).

Vertical jumping. Countermovement jumps (CMJ) without arm swing were performed on a contact platform (Haynl Elektronik, Germany) according to Bosco et al. (1983). Each player performed 2 maximal CMJs interspersed with 45 seconds of passive recovery, and the best jump (i.e., highest height attained) was retained for further analysis (Bosco et al., 1983).

Linear sprint. Time during a 20-m dash in a straight line was measured by means of single beam photocell gates placed 1.0 m above the ground level (Sportronic TS01-R04, Leutenbach-Nellmersbach, Germany). Each sprint was initiated from an individually chosen standing position, 50 cm behind the photocell gate, which started a digital timer. Each player performed 2 maximal 20-m sprints interspersed with 3 minutes of passive recovery, and the fastest time achieved was retained.

Serve velocity. A radar gun (Stalker Professional Sports Radar, Radar Sales, Plymouth, MN) was used to measure first-serve. The radar gun was positioned on the center of the baseline, 4 m behind the server, aligned with the approximate height of ball contact and pointing down the center of the court. The serves for subjects who were right-handed served to the left serve box (from the right) and the ones who were left-handed served at the right serve box (from the left). Athletes were instructed to perform eight maximal serves down the "T" (center line). A target area (150cm x 60cm) was placed into the serve box. Shots landing within the target area were given two points, serving into the serve box was counted one point and balls landing outside the serve box were associated with o points. A total score was recorded for each trail. The average speed was used for further analysis.

Hit and Turn Tennis Test. The Hit and Turn Test was developed as an acoustically controlled progressive on-court fitness test for tennis players, which can be performed simultaneously by several players (Ferrauti et al., 2011). The test involves specific movements along the baseline (i.e., side steps and running), combined with forehand and backhand stroke simulations at the doubles court corner (distance 11.0 m). At the beginning of each test level, the players stand with their racket in a frontal position in the middle of the baseline. Upon hearing a signal, the player turns sideways and runs to the prescribed (i.e., by a CD player) backhand or forehand corner. After making their shot, they return to the middle of the court using side steps or crossover steps (while looking at the net). When passing the middle of the baseline again, they turn sideways and continue to run to the opponent's opposite corner. The end of the test was considered when players fail to reach the cones in time or was no longer able to fulfill the specific movement pattern. Maximal completed level was used for the determination of the tennis-specific aerobic fitness.

Statistical analyses

Chi-square tests were used to test the observed and expected birth distribution across the sample of players. As recommended by Delorme and Raspaud (2009), all players affiliated to the German Tennis Federation (i.e., "licensed players") were used as the theoretical expected distribution (Delorme and Raspaud, 2009). In separate steps, all "ranked players", "regional squad" and "national squad" players were taken as the observed distribution. Anthropometrical and fitness variables were reported as mean and standard deviation ([+ or -] SD). A one-way analysis of variance ANOVA was used to compare all anthropometrical and fitness variables across birth quarters for each age group. In addition, the standardized difference or effect sizes (ES) and the 90% confident intervals between the first and fourth quarter were calculated for each parameter. Threshold values for Cohen ES statistic were > 0.2 (small), 0.5 (moderate) and > 0.8 (large) (Cohen, 1988). All calculations were performed using Microsoft Excel (Microsoft, Seattle, Washington, USA) and SPSS (version 20.0, SPSS Inc., Chicago, Ill., USA) and the level of significance was set atp < 0.05.

Results

The distribution of birth dates in the analyzed tennis players as well as the corresponding German population is shown in Table 2 and Figure 1. Figure 1 shows the percentage of males with different playing statuses born in the first half of the year, and the birth distribution of the German Top 50 senior players (including the Davis Cup team). There is a balanced distribution of the German population (49.5% born in the first half of the year (1stHY) and 50.5% born in the second half of the year (2ndHY), respectively). Similarly, a balanced distribution of "licensed players" (49.0% and 51.0% in the 1stHY vs. the 2ndHY, respectively) was observed. A moderate bias toward the 1stHY was observed for "ranked players" (54.4% and 45.7% for the 1stHY vs. the 2ndHY, respectively). On the contrary, 65.1% of players of the "regional squad" were born in the first half (1stHY) of the year, and 34.9% in the second half (2ndHY). For the "national squad", the birth months were even more skewed towards the 1stHY (70.2%) compared to the 2ndHY (29.8%) (Figure 1).

When the "licensed players" were used as the expected distribution (Table 2), results showed significant differences between birth quartiles (p values ranging from < .001 to .03 for all age groups) with more players born in the first quarters of the year.

Results regarding the anthropometrical characteristics of the regional selected junior players compared across the four birth quarters of each age category are presented in Table 3. Results showed significant differences between quartiles only in some parameters and age groups (p < .05; effect sizes ranging from 0.06 to 1.28) in APHV (U12), years from/to APHV (U14 and U15), height (U14 and U15), body mass (U14) and body mass index (U13, U14 and U17).

Table 4 shows the results of the different physical fitness performance tests. There were significant differences between quartiles in grip strength (U13; Q1>Q4) and serve velocity (U12; Q4>Q2), while there were no differences in CMJ, 20 m sprint and Hit and Turn test between any age group.

Discussion

The aims of the present study were to test the existence of RAEs in young German male tennis players and to examine if these effects were influenced by age and/or skill level. A further aim was to investigate whether players born later in the selection year but still selected into the elite squads were likely to be similar across a range of anthropometric and fitness attributes compared with those born earlier in the year. The main findings of the present study were as follows: 1) an uneven birth distribution was present in German youth competitive tennis; 2) the observed effect was present in all of the age groups analyzed and more pronounced with an increased competition level in youth players; 3) the RAE was less apparent at elite senior level; 4) players born later in the selection year and still selected into the elite squads were likely to be similar across a range of anthropometric and fitness attributes compared with those born earlier in the year.

In order to accurately examine the birth distribution of players, and for a precise measurement of a potential RAE, we followed the suggestions of Delorme and Raspaud (2009). These authors suggested that it is necessary to analyze all licensed players as the expected distribution, rather than to use the national population, since there might be already existing differences and there could be a misinterpretation of the results (Delorme and Raspaud, 2009). Therefore, if an asymmetric distribution is found among all licensed players, it would not be surprising to find the same tendency of distribution also among higher level players (i.e., regional and national squads). Thus, despite licensed players showing a balanced distribution, results showed that relative age effects exist in the regional and national squads, with a greater percentage of players born in the 1stQ (Table 2). Results are in line with previous research analyzing the birth dates of tennis players (e.g., 12 to 18 years old), with players born in the 1stHY accounting for 60 to 86% of the whole population analyzed (Baxter-Jones et al., 1995; Edgar and O'Donoghue, 2005b; Filipcic, 2001). Also, when ranked players were analyzed (7,165 players aged 10-17 years old), results showed significantly more players born in the 1stQ than in the last quarter of the year. Although the bias was less pronounced here (54.0%) than in the regional (65.1%) and national squads (70.2%), we can speculate that among these ranked players, there is a process of "self-elimination" in the later born players, since the selection of this group is not based on the decision of coaches and talent scouts, as in the regional and national groups. Although the causes for this self-adjusting distribution seem to be multifaceted and not clearly understood, some researchers from other sports speculate that this self-adjusting distribution could be provoked by a possible drop out of late born players as they might expe rience more situations of failure or inferiority, losing the ambition to compete and therefore withdraw from competitive tournaments (Delorme et al., 2010).

Analyzing the possible age related differences regarding RAEs, the findings revealed a skewed distribution of birth dates over the age categories analyzed (i.e., U12 to U18; Table 2) towards an earlier birth date. However, although the skewed distribution is still evident and an effect is more prevalent at younger ages, it seems that there is a tendency showing that the relative proportion of players born in the first quarters of the year diminishes from U12 to U18, also in the national group (i.e., 54.6% of the players born in the 1stQ in U12 and 28.0% in U18). Previous research in other sports investigating age as a moderator of risk found a progressively increased effect from the child (Under 10 years) age range to the adolescent (15-18 years) category, before decreasing at the senior (>19 years) age category (Cobley et al., 2009). Interestingly, this declining tendency is found when senior players (first 50 players in the DTB ranking list) are compared with the junior national squad players (56% of the players born in the 1stHY and 44% in the 2ndHY). These results are in agreement with previous research examining team sport athletes. Although the mechanisms for this age-related effect are not known, the relative advantage of total life experience is reduced as players get older (e.g., in 12-year old players, an 11-month difference in age represents ~10% of total life experience, while in 18-year old players that means ~5%).

Regarding competitive level, our results show that the percentage of players born in the 1stHY increased according to the selection level in youth tennis players (i.e., from all licensed players to the national selection of players) (Figure 1). Present results, however, concur with previous research where the magnitude of the RAEs was greater at higher competitive level in soccer players (Mujika et al., 2009; Sherar et al., 2007). Moreover, and according to the present data, it can be speculated that in the transition from junior to senior professional level a greater number of relatively older players are more likely to dropout, which has also been reported in handball and soccer (Baker et al., 2010; Cobley et al., 2008). Therefore, in the long term, the former disadvantage might turn into an advantage as relatively younger and late mature players might develop superior technical and tactical skills once they "survive" the talent detection and development system (Schorer et al., 2011).

Overall, no systematic differences were observed in any of the anthropometrical characteristics between the players born in different quarters (Table 3). However, there were some substantial differences in some variables in certain age groups, which should be noted. For example, in the U12 group, later born players have their APHV earlier (0.61 years) than their relatively older peers (i.e., 13.78 vs. 13.17 for players born in the 1stQ vs. 4thQ, respectively), possibly compensating the "disadvantage" of being relatively young with an earlier age for onset of puberty. On the contrary, in U14 and U15 players, those players born in the 1stQ already achieved their respective ages of PHV, while players born in the 4thQ were almost one year behind them (i.e., APHV in U14: 0.01 vs. -1.23 for players born in the 1stQ vs. 4thQ, respectively). Also, in the U14 and U15 players and in the U13 and U14, players born in the 1stQ were taller and heavier than their 4thQ born peers. Whilst these tendencies in anthropometry are likely to be practically important (e.g., relatively taller players would be able to serve faster (Vaverka and Cernosek, 2013), they were not accompanied by superior physical fitness (see below), which might have enabled them to minimize the potential advantages associated to superior anthropometrical parameters.

No systematic differences were found in any of the physical fitness parameters analyzed when comparing players born across different quarters of the year, supporting the hypothesis that selected talented players born later in the year presented similar physical fitness values than their earlier born peers. Similarly, previous research conducted in soccer, showed that players born later in the selection year, and selected into the elite teams, had similar physical characteristics than their relatively older peers (Deprez et al., 2012).

There were some limitations to this type of study. Most notably, we tested the players only once during the season. Longitudinal data of the same players during multiple years might have yield different results since some physical fitness performance and anthropometrical measures have been shown to be unstable throughout adolescence (Buchheit and Mendez-Villanueva, 2013). The regression equations used to estimate the pubertal timing according to Mirwald et al. (2002) were calculated on a sample of Canadian children. Since we used non-Canadian children, this might have an effect on the outcomes.

Conclusion

The results of the present study show that RAEs exist in the selection of youth tennis players in Germany, with a greater percentage of players analyzed born in the 1st Quarter compared to all licensed tennis players in the country, and more pronounced with an increased competition level in youth players. However, players selected into the higher competitive groups (regional and national) were physically homogenous regardless of relative age. While the selection process of the present elite tennis players seems to follow the trend observed in other team sports as soccer or basketball, with early born players being more selected at junior levels, especially at younger ages and at higher playing standards (i.e., national selection), the reasons for this "over-selection" appear to be related with current performance rather than potential progression, as a RAE is much less evident in senior players. Players born later in the selection year and still selected in elite squads were likely to be similar across a range of physical fitness attributes compared with those born earlier in the year. Results of the present study may help improve the current selection policies in elite tennis in Germany, facilitating the selection of greater number of players born in the latter part of the year.

Key points

* RAEs exist in the selection of youth tennis players in Germany, a greater percentage of players analyzed was born in the 1st quarter compared to all licensed tennis players in the country, and more pronounced with an increased competition level in youth players.

* Players born later in the selection year and still selected in elite squads were likely to be similar across a range of physical fitness attributes compared with those born earlier in the year.

* The selection process should be reevaluated and changed to reduce the impact of RAEs on tennis players.

Received: 02 February 2015 / Accepted: 22 June 2015 / Published (online): 11 August 2015

Acknowledgements

The authors would like to especially thank Peter Pfannkoch (German Tennis Federation) for his support within the project.

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Alexander Ulbricht (1 [mail]), Jaime Fernandez-Fernandez (2), Alberto Mendez-Villanueva (3) and Alexander Ferrauti (1)

(1) Department of Training and Exercise Science; University Ruhr-Bochum, Germany; (2) Sports Research Centre, Miguel Hernandez University, Elche, Spain; (3) Strength & Conditioning Coach; Football Performance & Science Department (ASPIRE, Academy for Sports Excellence), Qatar

([mail]) Alexander Ulbricht

Department of Training and Exercise Science; University RuhrBochum, Germany

AUTHOR BIOGRAPHY

Alexander ULBRICHT

Employment

Scientific co-worker at the Department of Training and Exercise Science; University Ruhr-Bochum (Germany) Strength &

Conditioning Coach

Degree

MSc Sports Sciences

Research interests

Intermittent sports, Maturation, performance, physical fitness testing

E-mail: alexander.ulbricht@rub.de

Jaime FERNANDEZ-FERNANDEZ

Employment

Assistant Professor at the Sports Research Centre, Miguel Hernandez University, Elche, Spain

Degree

PhD Sports Sciences

Research interests

Intermittent sports, physical performance, maturation

E-mail: jaime.femandez@umh.es

Alberto MENDEZr VILLANUEVA

Employment

Senior Football Strength & Conditioning Coach; ASPIRE (Academy for Sports Excellence)

Degree

PhD Exercise Physiology

Research interests

Intermittent sports physiology, physical fitness testing, strength and conditioning in team sports

E-mail: amendezvillanueva@/yahoo.com

Alexander FERRAUTI

Employment

Dean of the Faculty of Sport Science; Professor for training science and head of the Department of Training and Exercise Science; University Ruhr-Bochum (Germany)

Degree Prof. Dr.

Research interests

Testing, training and recovery in intermittent sports

E-mail: alexander.ferrauti@rub.de
Table 1. Overview of the different groups analyzed

Group name                 Group Definition          Sample Size (n)

German Population     The German male population         3216811
                        born between 1992-2000
                              (U12-U18)

Licensed Players    All players affiliated to the        120851
                    German Tennis Federation (DTB)
                      born between 1992 and 2000
                              (U12-U18).

Ranked Players       All players having a ranking         7165
                    position on the Under 18-Youth
                      ranking list born between
                         1992-2000 (U12-U18)

Regional Squad      All players selected from the          381
                     regional federation coaching
                      staff as the most talented
                    players born between 19922000
                              (U12-U18)

National Squad      All players selected from the          57
                     national federation coaching
                      staff as the most talented
                    players born between 19922000
                              (U12-U18)

Senior Players       Top 50 senior players of the          50
                     national ranking (including
                         the Davis Cup team)

Table 2. Season of birth distribution of young male tennis players
and the corresponding German population.

Age group         Status               Q1 (%)          Q2 (%)

U12               National selection   6 (54.6)        2 (18.2)
                  Regional selection   38 (41.9)       22 (26.9)
                  Ranked players       501 (30.0)      444 (26.5)
                  Listed players       5987 (25.2)     6110 (25.7)
                  German population    196281 (24.9)   196644 (24.9)

U14               National selection   11 (44.0)       5 (20.0)
                  Regional selection   52 (38.5)       36 (26.9)
                  Ranked players       694 (32.5)      507 (23.5)
                  Listed players       7974 (25.0)     7916 (25.0)
                  German population    202693 (24.7)   204780 (25.0)

U16               National selection   5 (35.7)        7 (50.0)
                  Regional selection   34 (36.3)       25 (26.2)
                  Ranked players       495 (28.0)      436 (24.7)
                  Listed players       7987 (23.8)     8158 (24.3)
                  German population    193793 (24.2)   193239 (24.1)

U18               National selection   2 (28.6)        2 (28.6)
                  Regional selection   21 (34.1)       20 (34.6)
                  Ranked players       429 (27.6)      389 (25.0)
                  Listed players       7454 (23.7)     7620 (24.2)
                  German population    201252 (25.0)   202051 (25.1)

All age groups    National selection   24 (42.1)       16 (28.1)
pooled together   Regional selection   145 (38.1)      103 (27.0)
                  Ranked players       2119 (29.6)     1776 (24.8)
                  Listed players       29402 (24.3)    29804 (24.7)
                  German population    794019 (24.7)   796714 (24.8)

Age group         Status               Q3 (%)          Q4 (%)

U12               National selection   0 (0.0)         3 (27.3)
                  Regional selection   20 (22.9)       9 (8.3)
                  Ranked players       417 (24.5)      325 (19.0)
                  Listed players       6354 (26.7)     5364 (22.5)
                  German population    210187 (26.6)   186507 (23.6)

U14               National selection   6 (24.0)        3 (12.0)
                  Regional selection   32 (22.6)       16 (12.0)
                  Ranked players       530 (24.6)      424 (19.5)
                  Listed players       8530 (26.7)     7519 (23.6)
                  German population    219002 (26.7)   193396 (23.6)

U16               National selection   1 (7.1)         1 (7.1)
                  Regional selection   19 (20.0)       16 (17.3)
                  Ranked players       441 (24.9)      397 (22.4)
                  Listed players       8999 (26.8)     8456 (25.2)
                  German population    215388 (26.9)   198961 (24.8)

U18               National selection   1 (14.3)        2 (28.6)
                  Regional selection   13 (19.3)       8 (12.0)
                  Ranked players       388 (24.9)      348 (22.4)
                  Listed players       8580 (27.2)     7843 (24.9)
                  German population    212213 (26.3)   190424 (23.6)

All age groups    National selection   8 (14.0)        9 (15.8)
pooled together   Regional selection   84 (22.1)       49 (12.9)
                  Ranked players       1776 (24.8)     1494 (20.9)
                  Listed players       32463 (26.9)    29182 (24.2)
                  German population    856790 (26.6)   769288 (23.9)

Age group         Status               n         p

U12               National selection   11
                  Regional selection   89        .001
                  Ranked players       1687      .000
                  Listed players       23815
                  German population    805940

U14               National selection   25
                  Regional selection   136       .001
                  Ranked players       2155      .000
                  Listed players       31939
                  German population    801381

U16               National selection   14
                  Regional selection   94        .016
                  Ranked players       1769      .000
                  Listed players       33600
                  German population    819871

U18               National selection   7
                  Regional selection   62        .031
                  Ranked players       1554      .001
                  Listed players       31497
                  German population    789619

All age groups    National selection   57        .011
pooled together   Regional selection   381       .000
                  Ranked players       7165      .000
                  Listed players       120851
                  German population    3216811

U12-U18: under 12 to under 18: Q1-Q4: first quarter to fourth quarter;
p < 0.05: significant differences compared with licensed players

Table 3. Biological maturity, anthropometric characteristics and
ranking positions of youth elite tennis players according to
birth distribution.

                 Age Group           1st Q

APHV [year]         U12        13.8 [+ or -] 0.5

                    U13        14.2 [+ or -] 0.6
                    U14        13.7 [+ or -] 0.8
                    U15        13.8 [+ or -] 0.6
                    U16        13.8 [+ or -] 0.5
                    U17        14.0 [+ or -] 0.6

Years from/to       U12       -2.07 [+ or -] 0.5
APHV [year]         U13        -1.7 [+ or -] 1.7
                    U14        0.0 [+ or -] 0.8
                    U15        0.9 [+ or -] 0.6
                    U16        2.0 [+ or -] 0.5
                    U17        2.8 [+ or -] 0.6

Height [cm]         U12       151.0 [+ or -] 7.5
                    U13       157.3 [+ or -] 8.1
                    U14       167.7 [+ or -] 7.5
                    U15       174.7 [+ or -] 6.5
                    U16       178.2 [+ or -] 5.4
                    U17       181.2 [+ or -] 4.9

Body mass [kg]      U12        39.5 [+ or -] 5.9
                    U13        45.5 [+ or -] 6.9
                    U14        54.0 [+ or -] 8.8
                    U15        61.2 [+ or -] 8.5
                    U16        65.7 [+ or -] 5.5
                    U17        73.0 [+ or -] 6.3

Ranking             U12      1838.0 [+ or -] 742.7
[national U18]      U13      981.5 [+ or -] 463.3
                    U14      726.1 [+ or -] 514.5
                    U15      325.7 [+ or -] 240.0
                    U16       120.2 [+ or -] 69.3
                    U17       72.8 [+ or -] 91.1

                 Age Group           2nd Q

APHV [year]         U12        13.8 [+ or -] 0.3

                    U13        13.8 [+ or -] 0.5
                    U14        13.8 [+ or -] 0.6
                    U15        13.9 [+ or -] 0.6
                    U16        13.8 [+ or -] 0.7
                    U17        13.9 [+ or -] 0.5

Years from/to       U12        -2.3 [+ or -] 0.3
APHV [year]         U13        -1.4 [+ or -] 0.5
                    U14        -0.3 [+ or -] 0.6
                    U15        0.5 [+ or -] 0.7
                    U16        1.7 [+ or -] 0.7
                    U17        2.6 [+ or -] 0.5

Height [cm]         U12       148.0 [+ or -] 5.4
                    U13       157.1 [+ or -] 8.6
                    U14       165.5 [+ or -] 6.5
                    U15       170.4 [+ or -] 8.2
                    U16       179.3 [+ or -] 8.7
                    U17       180.6 [+ or -] 3.8

Body mass [kg]      U12        36.3 [+ or -] 3.0
                    U13        43.9 [+ or -] 8.7
                    U14        50.8 [+ or -] 6.4
                    U15        57.5 [+ or -] 9.4
                    U16        64.9 [+ or -] 8.0
                    U17        67.9 [+ or -] 4.8

Ranking             U12      1918.0 [+ or -] 889.8
[national U18]      U13      1132.8 [+ or -] 557.3
                    U14      833.4 [+ or -] 927.8
                    U15      405.6 [+ or -] 542.5
                    U16      153.6 [+ or -] 112.3
                    U17      192.6 [+ or -] 372.7

                 Age Group           3rd Q

APHV [year]         U12        13.4 [+ or -] 0.4

                    U13        13.9 [+ or -] 0.5
                    U14        13.8 [+ or -] 0.6
                    U15        14.0 [+ or -] 0.8
                    U16        13.4 [+ or -] 0.3
                    U17        13.9 [+ or -] 0.6

Years from/to       U12        -2.2 [+ or -] 0.4
APHV [year]         U13        -1.7 [+ or -] 0.5
                    U14        -0.5 [+ or -] 0.6
                    U15        0.1 [+ or -] 0.9
                    U16        1.8 [+ or -] 0.3
                    U17        2.3 [+ or -] 0.6

Height [cm]         U12       150.0 [+ or -] 8.0
                    U13       155.6 [+ or -] 6.4
                    U14       164.4 [+ or -] 8.1
                    U15       167.2 [+ or -] 10.0
                    U16       179.6 [+ or -] 4.9
                    U17       178.4 [+ or -] 6.2

Body mass [kg]      U12        39.8 [+ or -] 6.3
                    U13        41.3 [+ or -] 4.9
                    U14        49.9 [+ or -] 8.4
                    U15       55.0 [+ or -] 11.2
                    U16        69.7 [+ or -] 4.1
                    U17        71.4 [+ or -] 7.0

Ranking             U12      2255.4 [+ or -] 917.1
[national U18]      U13      1195.8 [+ or -] 751.7
                    U14      887.0 [+ or -] 968.3
                    U15      367.2 [+ or -] 157.3
                    U16      360.1 [+ or -] 338.9
                    U17      152.0 [+ or -] 163.6

                 Age Group           4th Q            P     Post hoc

APHV [year]         U12        13.2 [+ or -] 0.5     .001   Q1>Q3;Q4
                                                             Q2>Q4
                    U13        13.7 [+ or -] 0.3     .358     n.d
                    U14        14.1 [+ or -] 0.5     .532     n.d.
                    U15        13.8 [+ or -] 0.7     .843     n.d.
                    U16        13.3 [+ or -] 0.6     .170     n.d.
                    U17        13.5 [+ or -] 0.4     .576     n.d.

Years from/to       U12        -2.2 [+ or -] 0.5     .294     n.d.
APHV [year]         U13        -1.8[+ or -] 0.3      .731     n.d.
                    U14        -1.1 [+ or -] 0.4     .001   Q1;Q2>Q4
                    U15        0.2 [+ or -] 0.6      .013   Q1>Q3;Q4
                    U16        1.6 [+ or -] 0.6      .619     n.d.
                    U17        2.4 [+ or -] 0.5      .334     n.d.

Height [cm]         U12       151.2 [+ or -] 9.7     .641     n.d.
                    U13       154.0 [+ or -] 4.7     .604     n.d.
                    U14       157.5 [+ or -] 7.7     .024    Q1>Q4
                    U15       168.8 [+ or -] 7.0     .044     n.d.
                    U16       180.5 [+ or -] 6.1     .898     n.d.
                    U17       180.0 [+ or -] 6.3     .677     n.d.

Body mass [kg]      U12        40.8 [+ or -] 5.6     .192     n.d.
                    U13        40.2 [+ or -] 3.6     .067     n.d.
                    U14        42.2 [+ or -] 4.6     .009    Q1>Q4
                    U15        56.7 [+ or -] 8.2     .292     n.d.
                    U16        67.5 [+ or -] 9.7     .565     n.d.
                    U17        72.4 [+ or -] 9.1     .243     n.d.

Ranking             U12      1445.0 [+ or -] 932.1   .177     n.d.
[national U18]      U13      891.0 [+ or -] 286.6    .341     n.d.
                    U14      646.3 [+ or -] 317.7    .888     n.d.
                    U15      455.7 [+ or -] 460.7    .799     n.d.
                    U16      201.6 [+ or -] 206.6    .104     n.d.
                    U17      102.5 [+ or -] 134.6    .664     n.d.

                 Age Group   ES [+ or -] (90%CI)

APHV [year]         U12       1.28 (-.25; .97)

                    U13       .36 (-0.48; 1.41)
                    U14       .56 (-0.95; 0.11)
                    U15       .06 (-0.29; 0.39)
                    U16       .70 (-0.07; 0.77)
                    U17       .74 (-0.14; 1.08)

Years from/to       U12       .31 (-0.21; 0.51)
APHV [year]         U13       .05 (-0.64; 1.18)
                    U14       1.58 (0.72; 1.75)
                    U15       1.06 (0.30; 1.10)
                    U16       .60 (-0.12; 0.82)
                    U17       .53 (-0.42; 0.82)

Height [cm]         U12       .03 (-6.25; 5.77)
                    U13       .40 (-1.17: 7.78)
                    U14      1.31 (4.55; 15.79)
                    U15       .88 (1.84; 10.13)
                    U16       .39 (-7.32; 2.56)
                    U17       .22 (-4.09; 6.51)

Body mass [kg]      U12       .22 (-5.77; 3.15)
                    U13       .73 (1.50; 9.09)
                    U14      1.43 (5.82; 17.80)
                    U15       .52 (-0.75; 9.63)
                    U16       .22 (-8.16; 4.68)
                    U17       .09 (-6.43; 7.73)

Ranking             U12      .49 (-197.8; 983.9)
[national U18]      U13      .16 (-167.5; 348.5)
                    U14      .16 (-274.8; 434.4)
                    U15      .38 (-339.5; 79.6)
                    U16      .55 (-203.4; 40.7)
                    U17      .28 (-133.4; 74.0)

APHV: estimated age from-to peak height velocity; U12-U17: under 12 to
under 17: Q1-Q4: first quarter to fourth quarter; ES: effect size;
90%CI: 90% confidence intervals

Table 4. Fitness characteristics of youth elite tennis players
according to birth distribution.

                   Age Group          1st Q

Grip strength         U12       22.0 [+ or -] 3.4
D [kg]                U13       26.5 [+ or -] 3.9
                      U14       33.1 [+ or -] 6.7
                      U15       38.9 [+ or -] 7.7
                      U16       44.8 [+ or -] 7.3
                      U17       51.8 [+ or -] 6.4
CMJ [cm]              U12       28.5 [+ or -] 3.3
                      U13       30.7 [+ or -] 3.8
                      U14       32.7 [+ or -] 3.1
                      U15       35.3 [+ or -] 3.5
                      U16       36.6 [+ or -] 4.9
                      U17       38.4 [+ or -] 4.5

20m Sprint [s]        U12      3.60 [+ or -] 0.15
                      U13      3.51 [+ or -] 0.16
                      U14      3.35 [+ or -] 0.14
                      U15      3.29 [+ or -] 0.17
                      U16      3.18 [+ or -] 0.11
                      U17      3.09 [+ or -] 0.11

Serve                 U12      123.7 [+ or -] 10.9
velocity [km/h]       U13      134.2 [+ or -] 8.4
                      U14      148.2 [+ or -] 10.7
                      U15      161.7 [+ or -] 14.7
                      U16      172.2 [+ or -] 5.1
                      U17      176.7 [+ or -] 10.0

Hit & Turn            U12       13.3 [+ or -] 1.8
Test [level]          U13       13.5 [+ or -] 2.1
                      U14       14.7 [+ or -] 2.3
                      U15       16.0 [+ or -] 1.8
                      U16       17.5 [+ or -] 1.8
                      U17       17.7 [+ or -] 1.8

                   Age Group          2nd Q

Grip strength         U12       21.7 [+ or -] 2.8
D [kg]                U13       25.2 [+ or -] 5.2
                      U14       30.9 [+ or -] 7.2
                      U15       36.9 [+ or -] 8.2
                      U16       44.0 [+ or -] 8.7
                      U17       46.1 [+ or -] 7.3
CMJ [cm]              U12       29.1 [+ or -] 4.4
                      U13       30.9 [+ or -] 4.1
                      U14       32.4 [+ or -] 4.9
                      U15       35.2 [+ or -] 3.4
                      U16       37.3 [+ or -] 4.3
                      U17       36.3 [+ or -] 3.8

20m Sprint [s]        U12      3.61 [+ or -] 0.18
                      U13      3.50 [+ or -] 0.16
                      U14      3.46 [+ or -] 0.16
                      U15      3.33 [+ or -] 0.15
                      U16      3.18 [+ or -] 0.13
                      U17      3.18 [+ or -] 0.14

Serve                 U12      117.0 [+ or -] 8.1
velocity [km/h]       U13      130.6 [+ or -] 10.0
                      U14      146.3 [+ or -] 13.4
                      U15      155.2 [+ or -] 13.3
                      U16      168.0 [+ or -] 10.7
                      U17      169.2 [+ or -] 9.5

Hit & Turn            U12       11.6 [+ or -] 2.5
Test [level]          U13       13.7 [+ or -] 1.8
                      U14       15.2 [+ or -] 1.7
                      U15       15.6 [+ or -] 1.6
                      U16       16.9 [+ or -] 0.7
                      U17       16.6 [+ or -] 2.0

                   Age Group          3rd Q

Grip strength         U12       22.4 [+ or -] 4.1
D [kg]                U13       23.1 [+ or -] 3.7
                      U14       30.3 [+ or -] 5.6
                      U15       33.0 [+ or -] 7.2
                      U16       49.7 [+ or -] 7.8
                      U17       49.1 [+ or -] 6.1
CMJ [cm]              U12       29.3 [+ or -] 4.5
                      U13       29.1 [+ or -] 2.9
                      U14       30.9 [+ or -] 4.6
                      U15       33.6 [+ or -] 3.2
                      U16       39.0 [+ or -] 3.2
                      U17       41.5 [+ or -] 3.7

20m Sprint [s]        U12      3.61 [+ or -] 0.13
                      U13      3.59 [+ or -] 0.10
                      U14      3.45 [+ or -] 0.21
                      U15      3.34 [+ or -] 0.18
                      U16      3.19 [+ or -] 0.07
                      U17      3.10 [+ or -] 0.10

Serve                 U12      120.2 [+ or -] 7.4
velocity [km/h]       U13      132.7 [+ or -] 8.9
                      U14      136.8 [+ or -] 16.5
                      U15      148.00[+ or -] 9.4
                      U16      171.20[+ or -] 7.4
                      U17      177.9 [+ or -] 13.5

Hit & Turn            U12       12.6 [+ or -] 2.1
Test [level]          U13       14.3 [+ or -] 1.4
                      U14       14.4 [+ or -] 2.5
                      U15       16.4 [+ or -] 1.3
                      U16       16.6 [+ or -] 1.1
                      U17       16.3 [+ or -] 2.4

                   Age Group          4th Q           P     Post hoc

Grip strength         U12       23.8 [+ or -] 5.0    .786     n.d.
D [kg]                U13       23.3 [+ or -] 2.6    .030    Q1>Q3
                      U14       26.7 [+ or -] 3.3    .186     n.d.
                      U15       35.0 [+ or -] 8.5    .251     n.d.
                      U16       47.3 [+ or -] 5.3    .483     n.d.
                      U17       49.0 [+ or -] 3.9    .233     n.d.
CMJ [cm]              U12       30.5 [+ or -] 4.9    .702     n.d.
                      U13       29.0 [+ or -] 4.0    .271     n.d.
                      U14       34.8 [+ or -] 2.6    .261     n.d.
                      U15       32.8 [+ or -] 3.9    .193     n.d.
                      U16       36.9 [+ or -] 3.3    .658     n.d.
                      U17       37.2 [+ or -] 1.2    .061     n.d.

20m Sprint [s]        U12      3.52 [+ or -] 0.12    .544     n.d.
                      U13      3.51 [+ or -] 0.17    .277     n.d.
                      U14      3.45 [+ or -] 0.15    .152     n.d.
                      U15      3.37 [+ or -] 0.15    .616     n.d.
                      U16      3.18 [+ or -] 0.13    .996     n.d.
                      U17      3.06 [+ or -] 0.13    .234     n.d.

Serve                 U12      130.9 [+ or -] 4.5    .012    Q2<Q4
velocity [km/h]       U13      132.4 [+ or -] 8.9    .456     n.d.
                      U14      140.3 [+ or -] 5.1    .088     n.d.
                      U15      156.0 [+ or -] 11.6   .106     n.d.
                      U16      170.7 [+ or -] 10.8   .794     n.d.
                      U17      175.1 [+ or -] 11.0   .269     n.d.

Hit & Turn            U12       13.0 [+ or -] 2.6    .080     n.d.
Test [level]          U13       14.2 [+ or -] 2.9    .522     n.d.
                      U14       14.5 [+ or -] 2.2    .799     n.d.
                      U15       15.9 [+ or -] 1.4    .723     n.d.
                      U16       16.5 [+ or -] 1.0    .380     n.d.
                      U17       17.4 [+ or -] 1.6    .422     n.d.

                   Age Group   ES [+ or -] (90%CI)

Grip strength         U12       48 (-4.60; 1.02)
D [kg]                U13        85 (.36; 4.80)
                      U14       01 (1.88; 10.98)
                      U15       48 (-1.05; 8.87)
                      U16       36 (-8.14; 3.16)
                      U17       45 (-3.24; 8.87)
CMJ [cm]              U12        56 (-4.19; .09)
                      U13        44 (-.59; 3.99)
                      U14       69 (-4.32; 0.11)
                      U15        66 (.21; 4.77)
                      U16        07(-4.03; 3.41)
                      U17       29 (-2.88; 5.36)

20m Sprint [s]        U12        56 (-,03; .20)
                      U13        16 (-.10; .09)
                      U14         67 (-.21;.01)
                      U15        47 (-.18; .02)
                      U16        02 (-.10; .10)
                      U17        28 (-.09; .15)

Serve                 U12        69 (14,9; .56)
velocity [km/h]       U13       11 (-3.14; 6.86)
                      U14        80 (.76; 15.18)
                      U15       41 (-2.69; 14.20)
                      U16       17 (-5.33; 8.34)
                      U17       15 (-8.79; 12.03)

Hit & Turn            U12       18 (-1.11; 1.82)
Test [level]          U13       28 (-1,46; 1.23)
                      U14       06 (-1.54; 1.83)
                      U15        06 (-.95; 1.15)
                      U16        63 (-.30; 2.27)
                      U17       20 (-1.43; 2.21)

D: dominant hand; CMJ: countermovement jump; U12-U17: under 12 to
under 17: Q1-Q4: first quarter to fourth quarter; ES: effect size;
90%CI: 90% confidence intervals

Figure 1. Representation of the different populations analyzed
(tennis players and German population) born in the first half of
the year.

German Senior players           56,0
National selection of players   70,2
Regional selection players      65,1
Ranked players                  54,4
Licensed players                49,0
German population               49,4

Note: Table made from bar graph.
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Title Annotation:Research article
Author:Ulbricht, Alexander; Fernandez-Fernandez, Jaime; Mendez-Villanueva, Alberto; Ferrauti, Alexander
Publication:Journal of Sports Science and Medicine
Date:Sep 1, 2015
Words:8739
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