talent development in danish elite athletes -...

Department of Exercise and Sport SciencesFaculty of Science, University of CopenhagenNørre Allé 51, DK-2200 Copenhagen NTel.: +45 3532 0829 Fax: +45 3532 0870E-mail: [email protected] – www.ifi.ku.dk

Talent Development in Danish Elite Athletes

Karin MoeschAnne-Marie ElbeMarie-Louise Trier HaugeJohan Wikman

A controversial question within elite sports is whether or not young athletes need to specialize at an early age, or if it is more benefi cial to follow the path of early diversifi cation. This path includes sampling different sport experiences during childhood and then specia lizing later during adolescence. To explore this question, the career paths of Danish elite athletes were investigated.

The main research question addressed the differences between elite and near-elite athletes using data concerning the amount of practice hours during the career, engagement in addi-tional sports, time of specialization into the main sport, as well as the sport-specifi c achieve-ment motive and volitional factors. A total of 722 Danish elite athletes from 34 different sports replied to the questionnaire. In order to prevent a too heterogeneous sample, all analyses were conducted for groups of sports with similar requirements.

The results concerning the career paths of athletes from cgs sports, team sports, precision sports, and racquet sports are presented and discussed. Moreover, fi ndings on the differences between elite, near-elite athletes, and dropouts are provided for cgs athletes and football players.


Report for the project financed by Team Danmark, 1/5/2009 – 30/9/2010

Karin Moesch, Anne‐Marie Elbe,

Marie‐Louise Trier Hauge and Johan Wikman

Institut for Idræt

Københavns Universitet

2011


Report for the project financed by Team Danmark, 1/5/2009 – 30/9/2010

© Karin Moesch, Anne‐Marie Elbe, Marie‐Louise Trier Hauge and Johan Wikman

Department of Exercise and Sport Sciences, University of Copenhagen 2011

Front page layout: Allis Skovbjerg Jepsen

Photos: Das Büro and Team Danmark.

Layout: Marie‐Louise Trier Hauge

Print: Det Samfundsvidenskabelige Fakultets ReproCenter


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Contents

1. Introduction and theoretical background ........................................................................................ 5

Elite Performance through Early Specialization ......................................................................... 5

Elite Performance through Early Diversification ........................................................................ 7

Career Development Stages .......................................................................................................... 8

2. Aim of the project .............................................................................................................................. 9

3. Method .............................................................................................................................................. 10

Design ........................................................................................................................................... 10

Procedure ..................................................................................................................................... 10

Sample .......................................................................................................................................... 11

Instruments .................................................................................................................................. 14

Data analyses ................................................................................................................................ 16

4. Results, discussion and practical implications of the different sport categories ....................... 17

Addendum I: Validation of data about practice hours .............................................................. 17

4.1. Cgs Sports .................................................................................................................................. 17

Sample Cgs Sports ........................................................................................................................ 17

Results Cgs Sports ........................................................................................................................ 18

Discussion Cgs sports .................................................................................................................. 19

Practical implications Cgs sports ................................................................................................ 21

4.2. Team Sports .............................................................................................................................. 23

Sample Team Sports .................................................................................................................... 23

Results Team Sports .................................................................................................................... 24

Discussion Team sports .............................................................................................................. 25

Practical implications Team Sports ............................................................................................ 29


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4.3 Precision Sports ......................................................................................................................... 30

Sample Precision Sports .............................................................................................................. 30

Results Precision Sports, ............................................................................................................. 30

4.4 Racket Sports, ............................................................................................................................. 31

Sample Racket Sports .................................................................................................................. 31

Results Racket Sports .................................................................................................................. 32

Addendum II: Limitations of the study ...................................................................................... 32

References ............................................................................................................................................ 34

Appendixes ........................................................................................................................................... 38

Appendix 1: Cgs Sports ................................................................................................................ 38

Appendix 2: Cgs Sports ................................................................................................................ 39

Appendix 3: Team Sports ............................................................................................................ 40

Appendix 4: Team Sports ‐ football ............................................................................................ 41

Appendix 5: Team Sports ............................................................................................................ 42

Appendix 6: Precision Sports ...................................................................................................... 43

Appendix 7: Racket Sports ......................................................................................................... 44


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1. Introduction and theoretical background

The question of how to achieve peak performance is central in elite sports. Researchers within

all domains of sport sciences hope to gain knowledge on which variables and processes lead to

winning international medals. Within social sciences, and from a developmental perspective, one

of the controversial questions concerns which career path leads to expert performance. Based

on the “Developmental Model of Sport Participation” (Côte, Baker & Abernethy, 2007), two ways

to reach elite performance are described. The path of early specialization focuses on early

involvement in the main sport, normally occurring in early to middle childhood, with very little

or no involvement in other sports. The importance of a high amount of deliberate practice,

defined as a highly‐structured and goal‐oriented activity aimed at improving the current level of

performance, is stressed during all ages (Ericsson, Krampe & Tesch Römer, 1993). Additionally,

emphasis is placed on constraint factors, including motivation and effort, which are considered

essential to maintaining the hard and sometimes monotonous training regime. In contrast, the

path of early diversification postulates that the first years of sport participation should be

characterized by the involvement in different sports, as well as a high amount of play‐like

practice that focuses little on deliberate practice activities. Following these sampling years,

around age 12, the young athlete gradually reduces his/her involvement in other sports and

shifts focus to the main sport, beginning a highly‐deliberate practice regime around age 16

(Côté, Baker & Abernethy, 2007). The next sections will describe the two paths in detail.

Elite Performance through Early Specialization

Emerging from Ericsson et al.´s (1993) theoretical framework, this path postulates that in order

to achieve expertise, one must engage in 10,000 hours of deliberate practice within the chosen

domain. The theory is based on a well‐documented, strong and positive relationship between

amount of practice hours and performance found in different domains (e.g. Ericsson et al., 1993).

Ericsson et al. (1993) also argue that the accumulation of these practice hours must correspond

with sensitive stages of the biological and cognitive development during childhood and

adolescence. A logical conclusion of the paradigm suggests that an early start in a given sport is a

necessary requirement to reach expertise and that not doing so will result in a practice delay

compared to peers who started their sport involvement earlier.

There is extensive scientific evidence from different sports that supports a positive

relationship between practice hours and expertise level (e.g. Baker, Côte & Deakin, 2005; Baker,

Deakin & Côte, 2005; Helsen, Starkes & Hodges, 1998; Hodges & Starkes, 1996; Hodges, Kerr,

Starkes, Weir & Nannanidou, 2004; Law, Côte & Ericsson, 2007). In order to persevere on the


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long and strenuous path to expertise, including deliberate practice that are not considered

inherently enjoyable, Ericsson et al. (1993) suggest three domains to be essential in developing

expertise. Aside from resource constraints (e.g. access to training facilities and coaches or

parental support) that assumingly play a crucial role in the development of elite sport

performance (Holt & Dunn, 2004; Van Yperen, 2009; Baker & Horton, 2004), Ericsson et al.´s

(1993) focus is on motivation and effort. The motivational constraint refers to an individual’s

goal commitment. The effort constraint refers to the ability of an individual to persist in high

amounts of deliberate practice; this constraint is comparable to the concept of volition, as

discussed in the Rubicon model of action phases (Heckhausen, 1989). This model stresses the

assumption that motivation needs to be complemented by volition or will‐strength in order for

an intention to be transformed into an action. In other words, motivation alone is not sufficient

to maintain athletic training over the long period of time required to achieve expertise.

Motivation needs to be reinforced with volitional processes that are responsible for initiating an

action, despite internal and external resistance, and for maintaining that action until the goal has

been reached (Kuhl, 1983). Several studies confirm the significant role that motivational and

volitional factors play in the involvement and performance level of elite‐sport athletes (e.g.

Beckmann & Kazén, 1994; Elbe, Beckmann & Szymanski, 2003; Holt & Dunn, 2004; Van Yperen,

2009; Wenhold, Elbe & Beckmann, 2009).

Even though the relationship between practice and performance is one of the most

robust in behavioral science (Baker, Deakin & Côte, 2005), criticism arose regarding Ericsson et

al.’s (1993) approach. Firstly, even though many studies revealed that elite performers trained

more than sub‐elite performers, the elite performers failed to reach the magic number of 10,000

practice hours (Van Rossum, 2000; Baker, Côté & Abernethy, 2003). Secondly, Baker and Côté

(2006) reveal that reducing the development of expertise in sport to simply deliberate practice

fails to acknowledge important developmental, psycho‐social, and motivational factors of young

athletes. Thirdly, there is no consensus stating that early onset and early specialization are

required for the development of expertise (e.g. Carlson, 1988; Barynina & Vaitsekhovskii, 1992;

Lidor & Lavyan, 2002). For example, the results of Vaeyens, Güllich, War and Phillippaerts

(2009) indicate that there is no evidence that an early onset and a higher amount of sport‐

specific training are associated with greater success at a later stage.

Additionally, a body of research emerged showing that early specialization can lead to

negative consequences for the athletes, such as attrition and negative health outcomes (e.g. Côté,

Baker & Abernethy, 2007). Law, Côté and Ericsson (2007) found that Olympic‐level rhythmic

gymnasts, who had acquired significantly more training hours in their career than their

international‐level peers, rated their health as lower and their participation experiences as less

fun. Gould, Udry, Tuffey and Loehr’s (1996) study revealed that early specialization and highly‐

structured training reduced intrinsic motivation and led to higher dropout and burnout rates


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among young athletes. Likewise, Wall and Côté (2007) found that athletes who dropped out of

sport, compared with athletes who continued their participation, had began off‐ice training

earlier in their careers. This indicates that early specialized training regimes that are not

inherently enjoyable can have a detrimental effect on the long‐term development of athletic

expertise. These results strengthen the assumption that in order to become a highly motivated,

self‐determined, and committed adult athlete, it is crucial to build a solid foundation of intrinsic

motivation at early stages (Deci & Ryan, 2000).

No individual involved in elite sports will negate deliberate practice as an important

pillar for reaching expertise, and the prominence of practice is generally agreed upon in

literature (Janelle & Hillman, 2003). However, the risks of an early and intense involvement in

sports as well as the evidence for late specializing experts need to be acknowledged. Therefore,

it has to be questioned whether or not early specialization is the exclusive path to expertise. It

also needs to be investigated if different paths that involve lower risks for the individual, can

lead to the same outcome (Baker, Coté & Deakin, 2005).

Elite Performance through Early Diversification

Based on the above‐mentioned results, the notion emerged that, in addition to early

specialization, expertise can be reached through early diversification (Côté, Baker & Abernethy,

2007).

Two underlying notions exist for that path. From a psycho‐social point of view, it can be

reasoned that engaging in a variety of different sports allows the young athlete to experience

different physical, cognitive, affective, and psycho‐social environments (Côté, Lidor & Hackfort,

2009). It is hypothesised that this path promotes the development of intrinsic motivation (Côté

et al., 2007), which again serves as a basis for a self‐regulated involvement in elite sport at a

later stage (Côté et al., 2009). From a performance point of view, it can be argued that

experiences in various environments provide the young athlete with important physical,

personal, and mental skills required to specialize in one sport at a later stage in his/her career

(Côté et al., 2009). The central notion of performance point of view is that motor, cardio‐

vascular, and mental skills can be transferred from one domain to another. Even with limited

scientific research (Feltovich, Prietula & Ericsson, 2006), there remains a general assumption

that talented athletes can transfer common skills across sports (Williams & Ford, 2008).

Moreover, current research suggests that the effect of skills transfer is most pronounced during

early stages of involvement (Schmidt & Wrisberg, 2000), corresponding with the timeframe of

the sampling years in the “Developmental Model of Sport Participation” (Côté et al., 2007).

Evidence shows that later specialization can prove more beneficial while training to

become an expert athlete. Carlson (1988) found that elite tennis players specialized later and


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practiced less than their sub‐elite peers between the ages of 13 and 15, but intensified their

training considerably more after age 15. Likewise, Lidor and Lavyan (2002) found that elite

athletes from various sports began specialisation later than sub‐elite athletes. Nevertheless, the

elite athletes had completed more training hours by the time they reached peak performance,

indicating that despite their late start, they still managed to compile ample hours to perform at

the top level. Barynina and Vaitsekhovskii (1992) found that swimmers who specialized early,

when compared with swimmers who specialized later, spent less time on the national team and

ended their sport career earlier. Güllich’s (2007) results showed that early intensification in

athletic development does not correlate with long‐term success, but that in contrast, particularly

successful careers are characterized by a deceleration of practice and competitive development.

Lidor and Lavyan´s result (2002) confirms the idea of sampling, finding that 70% of the

elite, compared to 58% of the sub‐elite athletes, performed more than one sport in their early

years of involvement. Likewise, Emrich and Güllich (2005) report that both, being active in

another sport besides the main sport as well as starting the sport career in another sport and

then switching to the main sport at a later age, are significantly more prevalent in German

athletes who were successful at the international level compared to their peers who competed at

only the national level. Evidence suggests a beneficial effect of early diversification, not only on

performance level, but also on other variables. Baker and Côté (2006) state that sampling and

deliberate play in the early years of sport participation may lead to more enjoyment and a lower

frequency of dropout, which indirectly contributes to the attainment of a high level of

performance in adult years. Moreover, they report that athletes who sample and diversify in

their young years may be less at risk for injuries than their peers that specialize early.

However, doubts arose concerning whether or not sampling is inherently beneficial for

all young athletes; in particular, several authors questioned the application of early

diversification to all sports (Baker, 2003; Williams & Ford, 2008). Furthermore, Côté et al.

(2009) conclude that early diversification is not beneficial for athletes in sports where peak

performance occurs before full maturation, such as gymnastics. Emrich and Güllich’s (2005)

study confirms this assumption.

Career Development Stages

In addition to the above mentioned “Developmental Model of Sport Participation” (Côté et al.,

2007), another approach to describe athletes’ career development exists. This approach takes

into account the age at which athletes pass through different transitions. Based on Bloom’s

(1985) stages of talent development, Wylleman and Lavallee (2004) designed a model that

focuses on the athletic development, as well as the psychological, psycho‐social, and academic

development of athletes. They describe three transitions which take place during a sport career:

a transition into organized sport (entering initiation stage), a transition to a more intense level


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of training and competition (entering developmental stage), and a transition into the elite level

(entering perfection stage). Along with suggesting timeframes in which athletes typically go

through these transitions, Wylleman and Lavallee (2004) also stress that there are sport specific

differences that should be taken into account when investigating career development.

2. Aim of the project

Currently there is no quantitative data concerning the career development of Danish elite

athletes available. In attempt to bridge this gap, the aim of this project seeks to gather and

compare data on the careers of Danish athletes from different levels. The main research question

addresses differences between elite and sub‐elite athletes within the following areas:

the amount of practice hours they sample during their career

their engagement in additional sports during their career

the time point of their specialization into the main sport

the sport‐specific achievement motive

volitional factors

Moreover, for the sport type categories that had an ample sample size (cgs and team), logistic

regressions were performed to investigate which of the above mentioned variables predict

membership in the elite group.

An additional research question was investigated in hopes of detecting differences

between elite, sub‐elite, and dropout athletes regarding their engagement in other sports, the

time point of their specialization into the main sport, as well as the sport‐specific achievement

motivation and volitional factors. Due to sample size, this research question was only

investigated within the cgs group and the football players.


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3. Method

Design

In order to gain additional information concerning the optimal path for reaching high‐level

athletic performance, it seems meaningful to identify variables that differentiate elite athletes

from sub‐elite athletes based on exposure to practice activities (Williams & Ford, 2008). Many

studies within the domain of talent development and expertise have been conducted based on

the seminal work of Bloom (1985), using a retrospective design. Even though this design bears

methodological risks (e.g. recall bias, see Hodges et al., 2007), it can provide interesting and

meaningful insights into the early experiences of elite and sub‐elite athletes when there is not

enough resources for longitudinal studies. Based on the above stated considerations, the

present study adopts a cross‐sectional, retrospective design.

Procedure

A link to a web‐based questionnaire was sent out to the target group by email. A web‐based

design was chosen because it seemed most suitable for a sample involving young persons. Web‐

based studies offer the advantage that the participants can choose individually when they want

to answer and are also a low‐cost method for obtaining responses from participants from

different parts of the country (Shaugnessy et al., 2006). Prior to starting the questionnaire, the

athletes were informed about the content and the aim of the research project, as well as being

told that all the data would be treated confidentially and that participation was voluntary. After

six weeks, a re‐test was sent out to the participating athletes with the aim of checking the

validity of some of the variables. In order to increase response rates, reminders were sent out

by mail and/or SMS after both surveys. To further check the data’s validity, some of the

participants who simultaneously took part in an interview study conducted by another Danish

research group were on that occasion asked the same questions again, offering the unique

opportunity for another validation check four months after data collection.

Unfortunately, there were only a few athletes from the first data group that could be

categorised as dropouts (see a description of dropout below). Because of this small number, it

was decided to conduct another data collection five months after the first one. E‐mail addresses

from potential dropout athletes were collected through the contact of various federations, clubs,

and coaches. All these athletes received an e‐mail with a link to the same questionnaire as the

athletes from the first data collection phase. Moreover, an e‐mail with the link to the

questionnaire was also sent to PE students at the Department of Sport and Exercise Sciences at

the University of Copenhagen, asking for their participation in the study, assuming they met the


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criteria for dropouts. A reminder email was sent to all the participants two weeks after the first

one was sent out.

Sample

All athletes that were registered in Team

Danmark’s database (Denmark’s elite sport

organization), and who were supported in the

year of the survey (2009) or had been supported

within the last six years were contacted. From the

initial 1,914 athletes, 743 replied (38,8%). 17

cases had to be deleted because they stopped

answering after only a few questions, another 4

cases had to be deleted due to unreasonable

answers, which left 722 athletes. 301 (41.7%)

were female and 421 (58.3%) male, ranging from

13 to 53 years of age with an average age of 23.77

(SD = 6.81). Amongst these athletes, 538 were

still involved in their main sport, while 185 had

retired before the survey took place. The athletes

were involved in 34 different sports. Table 1

shows the distribution of athletes (who filled out

the questionnaire to at least some extent) within

the different sports.

Unfortunately, many athletes did not fill in the

questionnaire completely, resulting in a data file with numerous missing variables. Because of

these gaps in the data, sport‐specific evaluations were not possible: reason being, if the sample

size is too small, the resulting power would have been too low, and/or because some statistical

analyses were not possible with small sample sizes at all. However, Emrich and Pitsch (1998)

propose that sports sharing similar structural conditions should lead to similar career paths,

which justifies analyzing such similar sports together. Other studies also followed that

approach, analyzing data of athletes from different sports with similar structural exigencies (e.g.

Güllich, 2007). Therefore, it was decided to group the sports into the following categories (table

2):

Sport N Sport N

Athletics 43 Pentathlon 1

Automobile 2 Motorsport 15

Badminton 38 Orienteering 19

Basketball 1 Riding 6

Table tennis 12 Rowing 47

Bowling 9 Sailing 52

Wrestling 6 Ski 1

Archery 7 Shooting 7

Curling 13 Sport dance 2

Cycling 37 Squash 1

Football 124 Swimming 68

Golf 25 Taekwondo 13

Gymnastics 4 Tennis 7

Handicap sport

3 Triathlon 12

Handball 63 Waterskiing 1

Ice hockey 45 Volleyball 9

Kano / Kayak 13 Others 16

Table 1: Distribution of athletes within the different sports


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Team sports: all sports that are performed in a team, opposing another team (e.g.

football, team handball).

Cgs sports: individual sports that are measured in centimetres, grams or seconds (e.g.

rowing, cycling).

Aesthetic sports: sports that are evaluated by external judges (e.g. gymnastics, sport

dance). Racket sports are game sports executed with rackets (e.g. badminton, table

tennis).

Combat sports: defined as individual sports where two opponents are, based on the

respective rules, fighting against each other (e.g. taekwondo, wrestling).

Precision sports: sports performed in a team or individually, where precision is the

decisive factor (e.g. golf, bowling, shooting).

Motor sports: sports performed with a motorised machine (e.g. motocross, speedway).

Others sports: contain sports that could not be assigned into one of the existing

categories.

Because only two categories included a sufficient

number of athletes for performing regression

analyses, the focus of the evaluations of the project

was placed on team sports and cgs sports. The two

next biggest categories (racket sports and precision

sports) will be briefly addressed. However, the other

sport categories were not analysed more in‐depth

due to the small number of athletes and the inability

of doing statistical analyses with such small sample

sizes.

The “elite” category (n = 295) was defined by a placement in the top 10 at a world level

championship (e.g. World Cup, Olympics) or by winning a medal at a championship at the

European level (e.g. European Championship) on a senior level. In order to eliminate an age

bias, athletes up to age 21 were also categorised as elite if they had won a medal at a junior

championship at a world level. All athletes who did not meet these criteria were labelled as sub‐

elite athletes (n = 275). Additionally, when dealing with the categorization of elite and sub‐elite

athletes, missing answers posed a problem: 152 athletes out of the whole sample did not fill in

the questions about sport success. Therefore, these athletes cannot be labelled as either elite or

sub‐elite athletes and their data cannot be used in the analyses.

Sport category N %

Team Sports 245 33.9

Cgs sports 295 40.9

Aesthetic sport 7 1.0

Racket sport 58 8.0

Combat sport 24 3.3

Precision sport 63 8.7

Motor sport 18 2.5

Other sports 12 1.7

Table 2: The number of athletes in the different sport categories.


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From the second data collection (dropouts), 76 athletes completed the questionnaire. 2

questionnaires were deleted because they only filled in the first question, and the data of

another 9 athletes was removed from the file because they answered that they were still

involved in their main sport, therefore not qualifying as a dropout. This led to a total of 65

dropout athletes that were then added to the main file. Two researchers blindly categorized the

athletes who indicated that they had stopped their engagement in their main sport into dropout

or non‐dropout; this was done based on their reasoning for retirement (which was formulated

as an open question). The cases in which the two researchers did not agree were discussed in

the research group, and from there a decisive categorization occurred. Throughout the process,

the following criteria served as a general guideline for the categorization of a dropout:

a) Lack of motivation for sport engagement

b) Performance results were not satisfying

c) Missed an important qualification

d) Educational / vocational reasons (started university, got a job offer, etc.)

e) Lack of time for a high training regime

f) Injuries categorized as not being serious enough for a career termination

g) Age: In general, athletes were only categorized as dropouts until the age of 20‐22. An

age range was used because different sports have different ages of peak performance

and therefore also different dropout ages.

As additional information, the answer to the following question,

“at which time point during your career did you retire from

sport” (answer possibilities: “When I retired, I had not yet

reached my personal peak performance.”, “When I retired, I was

at the peak of my performance.”, “When I retired, I had already

passed my personal peak performance.”), was taken into

account.

This procedure resulted in a total of 95 athletes being

categorized as a dropout. The distribution of the dropout

athletes within the different sport categories is shown in table 3.

Sport category N

Cgs sports 52

Team sports 25

Aesthetic sports 3

Racket sports 6

Precision sports 9

Total 95

Table 3


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Because the number of dropout athletes in aesthetics sports,

racket sports, and precision sports is too small for statistical

analyses, it was decided to conduct the dropout analyses for only

the cgs and team athletes. A closer look at the distribution of the

dropouts in the team sport athletes showed that most of these

athletes were involved in football (table 4). Therefore, it was

decided to conduct those analyses sport specifically (football) by

comparing elite players with sub‐elite and dropouts.

Instruments

The questionnaire covered information on the following topics:

1. Biographical information

2. Practice hours in the main sport: The athletes reported how many hours they trained on

average per week for every year in their main sport, starting with the current year and

then working backwards (see Hodges et al., 2007).

3. Involvement in other sports

4. Career development: The athletes stated the age they entered the “initiation stage1,” the

“developmental stage2” and the “perfection stage3” (Wylleman & Lavallee, 2004), the age

they participated in their first national, and international competition as well as how

many years they were a member of the junior and senior national team.

5. Weekly training schedule: For data validation purposes, the athletes reported their

average training schedule for every weekday during the current year or, alternatively,

for the last year they were involved in their main sport at an elite level.

6. Athletic success: The athletes gave their results from different international competitions

at the junior and senior levels.

1 Initiation stage starts when athletes first enter their sport in an organized setting (e.g. entering a club). During this stage, athletes are engaged in fun, playful sport and perceive sport as merely playing a game. 2 During the developmental stage, the amount of training increases, athletes specialize in one sport and start competing on a regional / national level. Typically during this stage, athletes narrow their focus to one or two sport disciplines that they are hooked by and committed to. 3 At the Perfection stage, athletes start competing at the highest level, at international competitions. During this stage, athletes become experts in their sport and feel responsible for their practices and competition performances.

Sport N

Basketball 1

Football 19

Handball 2

Ice hockey 1

Volleyball 1

Other (floorball) 1

Total 25

Table 4


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In order to gather data on the constraint factors, motivation and effort, it was decided to use the

Rubicon model of action phases (Heckhausen, 1989; see Introduction) as a theoretical base,.

This model appears to best represent the idea of motivation and effort that is proposed by

Ericsson et al. (1993). The following measurement instruments were chose based on the

availability of questionnaires in Danish as well as good reliability and validity in previous

projects:

7. The short version of the Achievement Motives Scale‐Sport (Elbe & Wenhold, 2005)

assesses the two achievement motive components, hope for success and fear of failure.

Each scale has 5 items, and uses a Likert‐scale answering format ranging from 0 (not

true for me at all) to 3 (exactly true for me). The values for the scales range from 0 (very

low) to 15 (very high). The two scales show high internal consistency with the current

sample (Hope for success: Cronbach’s alpha4 = .83, N = 573; Fear of failure: Cronbach’s

alpha = .85, N = 573).

8. The Volitional Components Questionnaire Sport (VCQ‐Sport; Wenhold et al., 2009c)

measures volitional skills and deficits in relationship to training and competitions. It

assesses 60 items through 20 scales within 4 main components (self optimization, self

impediment, lack of activation, and loss of focus). The questionnaire has a Likert‐scale

answering format ranging from 0 (very low, “not true for me at all”) to 3 (very high,

“exactly true for me”). The scales are formed by taking the average of all items, resulting

in scale values ranging from 0 (very low) to 3 (very high). Due to the length of the

questionnaire, the present study focuses on four scales: selfdetermination (Danish

version: 4 items), lack of energy (4), postponing training (3) and avoiding effort (4). The

scales were meaningful for the research question and showed good psychometric

properties in the Danish version (Cronbach’s alpha between .68 and .83; Test‐retest

reliability between .67 and .70; Wikman, 2007). The scales exhibited acceptable internal

consistency for the present sample (lack of energy: Cronbach’s alpha = .71, N = 563;

postponing training: Cronbach’s alpha = .78, N = 563; avoiding effort: Cronbach’s alpha =

.68, N = 563; and self‐determination: Cronbach’s alpha = .61, N = 563).

4 The Cronbach’s alpha are based on the analyses of the complete sample of the project, involving the sample from team sports as well as athletes from other sport categories


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Data analyses

As previously mentioned, missing data present a challenge in research. Due to the length of the

question on practice hours in the main sport, the present study unfortunately revealed a high

amount of missing values from that question. Since that information was the heart of the whole

project, it was decided to not estimate the missing data.

Outliers were detected and adapted to a more appropriate value based on the z‐value as

well as through discussions within the author team according to the suggestions of Tabachnick

and Fidell (2007).

After collecting data from the main survey and the two re‐tests, correlations were

performed to analyze the validity of the data on practice hours in the main sport. This was done

as retrospective; data can be biased, therefore checking the data before analyzing seems

indispensable.

In order to investigate differences between the elite and the sub‐elite samples, in terms

of the variables related to practice hours in the main sport, involvement in other sports, and

data on career development, T‐Tests were conducted with a significance level of .05.

For the categories that were big enough (cgs sports and team sports), additional

analyses regarding predictions could be included. A logistic regression was performed to

investigate whether practice hours in the main sport, involvement in other sports, data on

career development, as well as motivational and volitional variables (independent variables)

predicted membership in the elite athlete group (dependent variable). The enter method was

chosen because there are no hypotheses concerning the order of importance of predictor

variables. Assumptions regarding the distribution of the predictor variables are not required for

logistic regressions (Tabachnick & Fidell, 2007).


17

4. Results, discussion and practical implications of the different sport categories

Addendum I: Validation of data about practice hours

Three different measures were used to validate the data on practice hours. 1) A correlation was

performed between two measures given in separate sections of the questionnaire, both

intended to collect the same information (e.g. the amount of weekly training in the data on

practice hours history and the information about the average training amount per week from

the same year). The correlation between the two measures was .70 (N = 459). 2) The average

result of the written re‐test (4 weeks after the data collection), over the seven different time

points, was .75 for the weekly training amount. 3) The results of the re‐test (4 months after the

data collection) collected during the interview study with 16 athletes, showed a correlation of

.74 for the weekly training amount. All correlations can be categorized as strong (Brace et al.,

2009). Additionally, analyses revealed that the correlations in the elite and the sub‐elite athletes

(elite athletes: .76, sub‐elite athletes: .74) did not differ, indicating that the two groups have a

similar level of recall. It can be concluded that the data of the present study is reliable and also

comparable in quality to the data given from similarly structured studies.

4.1. Cgs Sports

Sample Cgs Sports

Out of the 295 athletes involved in cgs sports, 243

qualified as either elite or sub‐elite athletes and were

therefore entered into the analyses. 148 athletes belong

to the elite category and 95 to the sub‐elite. Table 5

displays the distribution of sport and success level in

the sample.

161 athletes were currently active in their main

sport at an elite level, while 82 athletes retired before

the survey was conducted. The mean age for the 96

female and 147 male athletes was 24.5 years (SD = 7.5),

ranging from 13 to 51 years of age. The elite athletes

were older (M = 26.58, SD = 7.49) than the sub‐elite

athletes (M = 21.16, SD = 6.16).

Sport Ntotal nelite nsubeliteCanoeing/kayak 12 11 1 Cycling 34 28 6

Orienteering 17 6 11

Rowing 40 35 5

Sailing 39 23 16

Skiing 1 1 0

Swimming 55 24 31

Track and field 33 11 22

Triathlon 11 8 3

Weightlifting 1 0 1

Total 243 148 95

Table 5: The distribution of sport and success levels in the sample.


18

Results Cgs Sports

T‐tests reveal significant differences between the elite and sub‐elite athletes in 11 of 26

variables (Appendix 1). Concerning the data on practice hours in the main sport, the results

show that the sub‐elite athletes have completed significantly more training hours, some as early

as age nine, and they continue to compile more hours throughout early adolescence, until age

15. The effect sizes are considered to be moderate (0.45 ≤ d ≤ 0.50; Cohen, 1969). At age 18, the

amount of practice hours is roughly the same for the two groups. After age 18, the elite athletes

complete more hours, resulting in a significant difference by age 21 from the sub‐elites, whose

training increase has not developed that intensively. Figure 1 shows the development of

practice hours at five different time intervals.

However, elite and sub‐elite athletes do not differ in their involvement in other sports.

Regarding different variables on career development, the following results can be found. Elite

athletes state that they pass important steps within their career (e.g. starting sport,

participation at first competition, etc.) at a significantly older age than the sub‐elite athletes

(0.40 ≤ d ≤ 0.63). Moreover, the elite athletes spend significantly fewer years on the junior

national team (d = 0.27), but more years on the senior national team (d = 0.97). No significant

differences between the two groups could be found regarding motivational and volitional

factors, indicating that the two groups do not show different characteristics within these

domains.

In a first logistic regression, six variables (membership on junior national team,

membership on senior national team, age, and training up to age 12, 15 and 18) were

0

1000

2000

3000

4000

5000

6000

7000

Elite

Sub‐elite

Figure 1: Development of practice hours at five different time points.


19

significant, and were re‐entered in a second logistic regression. In this analysis, a total of 175

cases were analyzed, with the full model significantly predicting membership in the elite group

(2 = 91.51, df = 6, p < .001). The model accounts for between 40.7% and 54.6% of the variance

of the membership in the elite group. Overall, 81.7% of group predictions are accurate.

Appendix 2 illustrates coefficients, the Wald statistics and associated degrees of freedom,

probability values, and the confidence interval for each predictor variable. The values of the

coefficients reveal that a shorter membership on the junior national team, an additional year on

the senior national team, less practice hours at age 15, and more practice hours at age 18

significantly predict international success.

Comparing elite athletes, sub‐elite athletes, and dropouts from cgs sports revealed some

interesting results (Appendix 1). The elite athletes start their sport engagement at a

significantly later age (11.91) than both dropouts (9.03) and sub‐elite athletes (8.63). Likewise,

the elite athletes are oldest (15.34) when entering the development stage, differing significantly

from the sub‐elite athletes (12.93) and the dropouts (12.11). The differences grow increasingly

larger concerning the transition to the perfection stage: the elite athletes state entering this

stage in average at age 18.63, which is significantly older than the sub‐elite athletes (16.47),

who are in turn significantly older than the dropouts (14.78). Concerning the first national

competition, the elite athletes are significantly older (14.77) than both the sub‐elite athletes

(12.51) and the dropouts (11.74). Regarding the time point of the first international

competition, we find significant group differences between all three groups: the elite athletes

have their first international competition in average at age 17.65, the sub‐elite athletes at age

15.65, and the dropouts as early as age 13.97. There are also significant group differences

between the three groups when examining the years of membership on the senior national team

(elite: 4.73; sub‐elite: 1.67; dropouts: 0.84). However, this result has to be considered carefully,

as the elite athletes are older than the sub‐elite athletes, and the dropouts did not have the

possibility to sample years in the membership due to their relatively early career termination.

Discussion Cgs sports

There is a general trend in cgs sport of elite athletes specializing later in their career than both,

sub‐elite athletes and dropouts. The sub‐elite athletes and dropouts pass through the three

transitions at a significantly earlier age than the elite athletes. Moreover, the elite athletes have

their first national and international competition at an older age. This finding corresponds with

the results of other studies (e.g. Emrich & Güllich, 2005; Vaeyens et al., 2009) that also report a

relatively delayed development of the more successful athletes when compared to their less

successful peers. Likewise, the elite athletes spend fewer years on the junior national team, but

more years on the senior national team. As can be seen in the practice amount data, sub‐elite


20

athletes spend more hours practicing at a young age. However, this trend seems to reverse in

early adulthood, with the elite athletes training more at age 21. There are no differences in the

current sample regarding the amount and time spent in other sports. This finding contradicts

others’ results (e.g. Vaeyens et al., 2009) that report successful athletes as having more

experiences in other sports.

The group comparison results were further strengthened by the logistic regression,

where four variables significantly predict the membership in the elite group. Both,

accumulated less training hours at age 15 and spending fewer years on the junior national team

(both measures of early specialization) negatively predict membership in the elite group. On the

other hand, variables that measure high involvement at a later career stage, namely training

hours at age 18 and years of membership on the senior national team, positively predict the

membership in the elite group. Neither variables on career development in early childhood, nor

the involvement in other sports, predict membership in the elite group.

The results of the present study confirm other studies (e.g. Carlson, 1988; Emrich &

Güllich, 2005; Güllich, 2007) that also report elite athletes intensifying their training regimes at

an older age when compared to their sub‐elite peers. Strengthening that approach of late

specialization, a recent publication of Côté et al. (2009) postulates that late adolescents have

developed the physical, cognitive, social, emotional, and motor skills needed to invest their

effort in a highly specialized training regimen in one sport.

Considering the “Developmental Model of Sport Participation” (Côté et al., 2007), the

results cannot fully confirm either of the two paths for cgs athletes. Regarding the early

specialization approach, the current results do support the underlying assumptions that elite

athletes complete more practice hours than sub‐elite athletes. After age 18, the elite athletes

report more practice hours than the sub‐elite, thus confirming the positive relationship between

practice hours and performance level found in the literature (see introduction). In contrast, the

assumption that late specialization may lead to a delay in athletic development that cannot be

overcome at a later stage is not supported. The current results clearly indicate that career

planning that involves less training at early ages and specializing later is more beneficial for

young athletes in cgs sports. The findings of the study confirm the idea of late specialization in

one sport, as suggested in the early diversification path postulated in the “Developmental Model

of Sport Participation” (Côté et al., 2007). However, the results concerning involvement in

different sports do not confirm the proposed advantage of sampling several sport experiences.

The groups do not differ in the number of other sports, nor in the number of months involved in

other sports, and the two variables have no predictive value. Baker et al. (2005a) found a similar

result in triathlons, a sport that can also be classified as a cgs sport. The idea of sampling a wide

variety of motor experiences at a young age does not seem to have a beneficial impact on cgs


21

sport athletes. This may possibly be related to the divergent demands placed on athletes: while

cgs sports require great physical effort, they are not as complex as e.g. gymnastics or ice‐skating,

nor do they place the same demands on tactical or decision‐making processes as team or

racquet sports. It can be hypothesised that the skill transfer of physical capabilities does not

have as an important effect on the development of expertise, and therefore sampling different

sport experiences does not prove crucial for cgs sport athletes.

The present study reveals that elite athletes in cgs sports specialize later than sub‐elite

athletes, supporting numerous studies within the domain (e.g. Vaeyens et al., 2009). However,

elite athletes begin intensifying their engagement much more in late adolescence, resulting in a

higher number of training hours in early adulthood. This confirms the idea of Ericsson et al.

(1993) that experts sample more training hours during their career than their less successful

peers. Another finding reveals that the involvement in other sports does not provide any

advantage to reach athletic expertise. Even though this finding contradicts other studies (e.g.

Lidor & Lavyan, 2002), it is logical when considering the requirements that are put on athletes

in cgs sports: large physical load, and relatively low exigencies in the technical or tactical

domain. From the current state of the art and the findings of the present study, it is summarized

that the optimal career path is not only a question of amount of training hours, but also a

question of when training regimes take place. The more recent adage “perfect practice makes

perfect” (Janelle & Hillman, 2003, p. 28) might need to be changed to “perfect training at the

perfect time makes perfect”.

Practical implications Cgs sports

Summary of the most important results:

Cgs – Result 1: Elite athletes show less years in practice until age 15, about the same

amount of practice at age 18, and more practice at age 21; in line, less training at age 15

and more training at age 18 predict membership in the elite group.

Cgs – Result 2: Elite and sub‐elite athletes do not differ in amount of involvement in

other sports; that factor also does not predict membership in the elite group.

Cgs – Result 3: Sub‐elite athletes enter all three stages (entering stage, development

stage, perfection stage) at an earlier age than elite athletes.

Cgs – Result 4: Elite athletes have their first national and international competition at a

later age than sub‐elite athletes.

Cgs – Result 5: Elite athletes spend less years on the junior, but more years on the senior

national team; both variables also predict membership in the elite group.

Cgs – Result 6: Elite and sub‐elite athletes do not differ in motivation and volition

characteristics.


22

The results led to the following practical implications:

Generally, it cannot be assumed that “more training is better”; at least not until the

athletes reach maturation: put only low to moderate training loads on young athletes up

to around age 15 (see cgs – R1).

Intensify the training load of the athletes during middle to late adolescence (see cgs –

R1).

Recruiting athletes for a specified and high‐volume training regime at a early age (early

to late childhood) is not beneficial for success at the senior level; do not push an early

start into the career and do not push athletes to specialize during childhood, instead

give the young athletes time for their skill development (see cgs – R3).

Pushing athletes at a young age toward national or international competitions is not

beneficial for later success: give them time before their first participation (see cgs – R4).

Being a member of the junior national team does not appear to be important, so do not

emphasise this too much or base selection criteria on this. Instead, emphasize a broad

and steady skill improvement (see cgs – R5).

An athlete’s sport specific development should be at high level by the time he/she meets

the age at which senior national team selections take place (see cgs – R5).

Reconsider the selection criteria for the junior national team (in terms of training age of

the athlete and physical maturation; see cgs – R5).

Be careful when it comes to the transition between junior and senior national team: it

does not seem beneficial to focus the selection for the senior national team based on an

ahtlete’s prior participation on the junior national team: instead, give all young athletes

the opportunity to qualify for the senior national team (see cgs – R5).

For success at the senior level, it seems crucial to increase the training amount intensively after

mid‐adolescence.

Further considerations, not directly results from the data, but based on current literature and

practical experiences in cgs sports:

A certain level of intellectual as well as psychological maturation is needed to

successfully endure an intensive training regime; these intellectual and psychological

skills need to be developed in the beginning of the career to better prepare the athletes

for intensive training regimes at a later age. Therefore, we suggest psychological skills

training especially for young elite athletes.


23

It can be assumed that every athlete has a certain amount of resources to put into sport;

and research suggests those resources are better spent at a later age (middle to late

adulthood).

It is unclear what role the engagement in an additional sport plays in the development of

sport expertise, but it is hypothesised that it is important for the child to make their own

choice concerning which sport he/she wants to specialize in., This leads to higher levels

of motivation through means of self‐determination: therefore, engaging in at least one

additional sport for a longer period of time, along with the main sport is recommended.

We are in no way suggesting that children should only start their engagement in the

main sport during early to middle adolescence. It appears to be beneficial for athletes to

have opportunities for a broad motor development in early childhood; moreover,

building an emotional bond with sports in general, as well as gaining intrinsic

motivation and feelings of competence are important in youth sports. Possessing this

background, children will most likely adjust better to an intense sport engagement at a

later stage of their career: therefore, encourage young children to participate in sports,

just do not put them through a rigid training regime before middle/late adolescence.

4.2. Team Sports

Sample Team Sports

175 athletes are categorized as team sport athletes. Taking into account that some sports are

more developed and specialized in certain countries compared to others and therefore

requiring a more intense engagement to reach the top, the current study focuses on team sports

with body contact that are defined as “elite sports” in Team Danmark’ s perspective. The group

involves athletes from football (n = 92), team handball (n = 45), and ice hockey (n = 38). The

volleyball players (n = 9) were excluded from these analyses. In this category, 63 athletes were

defined as elite athletes, and 112 as sub‐elite. Table 6 displays the distribution of sport and

success level in the sample.


24

151 athletes were currently active in their main sport

at the elite level; while 24 athletes had retired prior

to the survey being conducted. The mean age for the

90 female and 85 male athletes was 21.18 years (SD =

5.14), ranging from 15 to 50. The elite athletes were

older (M = 23.06, SD = 5.65) than the sub‐elite

athletes (M = 20.13, SD = 4.52).

Results Team Sports

T‐tests reveal significant differences between the elite and sub‐elite athletes in 3 of the 25

variables (Appendix 3). The data on career development showed elite athletes spending

considerably more years on the senior national team than their sub‐elite peers, with an effect

size that can be considered large (0.83; Cohen, 1969). In the remaining variables regarding

career development data, as well as all the variables regarding practice hours in the main sport

and involvement in other sports, no significant group differences could be found. Figure 2

shows the development of the practice hours of the two groups over five time frames in the

athlete’s career. As seen, the differences are minute between the two groups, suggesting that

training is comparable between the ages 9 and 21.

Concerning the data on motivational and volitional factors, the results revealed significant

group differences on the volitional scales, self‐determination and avoiding effect, with effect

0

1000

2000

3000

4000

5000

6000

7000

8000

Training up to age

9

Training up to age

12

Training up to age

15

Training up to age

18

Training up to age

21

Elite

Sub‐elite

Sport Ntotal nelite nsubelite

Football 92 23 69

Handball 45 24 21

Ice hockey 38 16 22

Total 175 63 112

Figure 2: Development of practice hours at five different time points in the career.

Table 6: The distribution of sport and success levels in the sample of team sports.


25

sizes that can be considered as small to medium (0.37 and 0.40, respectively; Cohen, 1969). Elite

athletes show higher values in self‐determination and lower values in avoiding effort when

compared to the sub‐elite athletes.

In a first logistic regression three variables (membership on the senior national team,

avoiding effort, and postponing training) proved significant and were, together with age as a

controlling variable, re‐entered into a second logistic regression. In this analysis, a total of 171

cases were analyzed, and the full model significantly predicted membership in the elite group

(2 = 29.99, df = 4, p < .001). The model accounts for between 16.1% and 22.1% of the variance

found in the elite group. Overall, 73.7% of group predictions are accurate. Appendix 5 illustrates

coefficients, the Wald statistics and associated degrees of freedom, probability values, as well as

the confidence interval for each predictor variable. The coefficients reveal that an additional

year on the senior national team and a lower value on the volitional scale, “avoiding effort”,

significantly predict international success. All variables regarding practice hours in the main

sport, involvement in other sports, and the achievement motive, as well as the remaining

variables concerning data on career development and volitional factors, do not significantly

predict the membership in the elite group.

The analyses of the dropout study (football) showed significant group differences in six

scales (see Appendix 4). The elite athletes are significantly older at the time of their first

national competition (14.04) when compared with the sub‐elite athletes (12.66); while the

dropout athletes experience their first national competition at age 13.93. The dropouts spend

significantly less years on the junior national team than both the sub‐elite (3.43) and the elite

(3.78) athletes. Years of membership on the senior national team reveal significant group

differences (elite athletes: 2.57; sub‐elite athletes: 0.58; dropouts: 0.67). However, it should be

noted that the elite athletes are older than the sub‐elite athletes, and that the dropout athletes

retired at an early age, therefore inhibiting them from spending more years on the senior

national team. Looking at the volitional scales, the results reveal that elite athletes have the

highest values in self‐determination (2.83) compared to the sub‐elite athletes (2.66) and the

dropouts (2.45). A significant difference between the elite athletes and the dropouts also

emerged in the scale, avoiding effort: elite athletes avoid significantly less effort (0.48) than

dropouts (1.00), with the sub‐elite athletes scoring in between (0.57). Finally, the three groups

differ significantly on the scale, postponing training: the elite athletes have the lowest value

(0.16), followed by the sub‐elite athletes, (0.32) and then the dropouts (0.76).

Discussion team sports

Investigating the career development of Danish elite and sub‐elite athletes in team sports

revealed that the career paths, in terms of accumulated practice hours, involvement in other


26

sports, age when entering different stages, age at first national and international competition, as

well as motivational factors, do not differ between the elite and the sub‐elite athletes, nor do

they predict membership in the elite group. Only two variables exhibited significant results in

both analyses. Spending more years on the senior national team is a characteristic of the elite

group and also predicts success in the logistic regression. Likewise, having lower values in the

volitional scale, avoiding effort, characterizes the elite athletes and predicts membership in the

elite group. Additionally, it was found that the elite group scored higher than their sub‐elite

peers on the scale, self‐determination.

Contrary to the predictions of Ericsson et al. (1993), the elite athletes of the current

study do not sample significantly more practice hours during their career than their sub‐elite

peers. Even though a whole body of research confirms the positive relationship between

practice hours and expertise level (see “introduction and theoretical background”), a closer look

at team sport athletes reveals controversies in the current state of the art. There are studies that

confirm the above mentioned positive relationship, stating that elite team sport athletes also

train more and therefore sample more practice hours during their career (Emrich & Pitsch,

1998; Helsen, Starkes & Hodges, 1998; Baker, Côté & Abernethy, 2003). However, Helsen et al.’ s

(1998) results for field hockey are more similar to the present findings. The authors also found

a main effect for skill and years into career for the field hockey players for all three groups.

However, the difference between international and national players (corresponding to the elite

resp. sub‐elite athletes in the present study) did not reach significance within the studied time

frame of 18 years and above in their career, even though the international players did train

slightly more from 15 years and above in their career. Güllich (2007) even found that the more

successful team sport athletes practice less in their main sport between the ages of 15 and 18

than their less successful peers.

It cannot be claimed that more practice simply leads to better performance in team

sports. Starkes (2000) hypothesised that the absolute amount of practice might not be

predictive of team athletes’ individual performance. This is because practice is normally

adapted to the skills of the best resp. worst player on the team, which might not correspond to

each players optimal practice level. Another reason for such unequivocal results could be that

the range of accumulated practice hours is highly variable both, within and between different

team sports, suggesting there may be factors other than total hours of practice influencing

expert attainment in team sports (Baker, Côté & Abernethy, 2003). Additionally, focusing solely

on hours of practice without taking into account the content and quality of practice is not

sufficient (Janelle & Hillman, 2003; Van Rossum, 2009), and could partly explain the

unequivocal results found so far. For example, Helsen et al. (1998) suggest that team and

individual practice might have different effects on performance. Supporting that view, Ward et


27

al. (2004) summarized that team practice and practice with others were often a more accurate

predictor of attained performance level, and Ward et al. (2007) found that team practice was

the most important discriminator between elite and sub‐elite athletes. Concerning the quality of

practice, it is questionable whether the practice hours stated by the athletes can be counted as

what Ericsson et al. (1993) call “deliberate practice”; deliberate practice is a form of highly goal

oriented practice that aims at maximizing improvement. Because information pertaining to the

quality and content of practice may be difficult to recall after several career years, different

methodological approaches (e.g. in‐depth interviews, longitudinal design) must be adopted.

However, such approaches would hinder the ability to have large sample sizes, which is a clear

strength of the approach adopted in the current study.

The results concerning membership on the senior national team reveal significant group

differences, with the elite athletes spending more years on the national team than their sub‐elite

peers. Moreover, the amount of years as a member of the senior national team also predicts

membership in the elite group. In line with the above‐mentioned discussion on the importance

of high quality training (deliberate practice), it is hypothesized that being a member of the

senior national team offers the opportunity for a high volume of deliberate practice with good

coaches, and that such high quality training increases the odds of reaching the top. This

argument is in line with Ericsson et al.´s (1993) prediction that resources (e.g. access to training

facilities, good coaches) can play a crucial role in the development of expertise. Furthermore, it

is assumed that practice hours on a senior national team are more specialized for different

playing positions and their respective requirements, and that even in team sports, practices take

a more individualized approach. In this approach the performance of the individual player is

more openly evaluated – a factor that seems important for volitional processes (see below).

The assumption that early onset and early specialization are needed in order to excel in

team sports, as suggested by the “elite performance through early specialization” path in the

“Developmental Model of Sport Participation” (Côté et al., 2007), cannot be supported by the

data of the current study, as both groups start and specialize at about the same age. However,

the current findings confirm data from Carlson (1991) showing that athletes from different

sports, including team sports, who reached the senior national team level, compared with

athletes that were similarly successful during adolescence but did not reach the national team

level, did not differ in the time point that they specialized in their main sport. Likewise, Ward et

al. (2009) found that the time of specialization did not differ between two groups of elite and

sub‐elite football players. Güllich (2007) reports a lower number of elite players, who had

already competed in their main sport between the ages of 11 and 14, showing tendencies that

late specialization can be more beneficial. In line with these results, Baker, Côté, and Abernethy


28

(2003) conclude that early specialization may not be a necessary requirement for expert level

performance in decision‐making sports (such as team sports).

Surprisingly, with regard to the demands that are placed on team athletes, no significant

results emerged from the variables concerning involvement in other sports. Sampling different

sport experiences, as suggested to be one factor in the “elite performance through sampling”

path (Côté et al., 2007), does not differ among elite and sub‐elite athletes. This is in line with

Ward et al. `s (2007) study, which found that athletic diversity did not differ among elite and

sub‐elite football players. In contrast, the results of Güllich (2007) revealed that there is a

higher number of successful team sport athletes who practiced consistently in another sport

besides the main sport, and did so until the end of their junior year. The accumulated time

practiced in other sports by successful team sport athletes is more than twice that of less

successful players. Seemingly, even when dealing with the impact of the engagement in other

sports on elite performance, no clear conclusions are drawn thus far.

The present study revealed no significant results regarding the sport specific

achievement motive. This is insofar surprising, as motivation is discussed as a core constraint in

Ericsson et al.´s (1993) approach, and the sport specific achievement motive has shown to

predict athletic success in a sample of young elite athletes (Elbe et al, 2003). In line, Reilly,

Williams, Nevill, and Franks (2000) conclude that motivational orientation is the most

important indicator of talent in football. Likewise, Holt and Dunn (2004) as well as Van Yperen

(2009) confirm that factors, such as commitment and discipline, form core competencies for

team sport athletes who want to reach expertise. Partially supporting this, the results of the

present study reveal the importance of specific volitional factors, mainly, avoiding effort. It is

hypothesized that athletes who, from an early age, do not avoid effort in practice, are more

likely to reach the top when compared to the athletes who tend to hide during practice. Team

sports can sometimes allow athletes to limit effort, as individual performance is not always

obvious and can be difficult to judge. The tendency to hold back effort when the individual

performance is not easily observable is called social loafing (Carron, Hausenblas & Eys, 2005),

and is a well‐known phenomenon in sport. Examining data from individual sports, where the

individual’s performance is immediately visible and athletes never have the opportunity to hide,

none of the volitional variables predict membership in the elite group nor do significant group

differences exist (Moesch, Elbe, Hauge & Wikman, in press).

To conclude, the findings of the present study reveal that the career paths of elite and

sub‐elite athletes are rather similar, and there are no clear signs if early specialization or early

diversification is more beneficial on the way to elite performance in team sports. From all

variables investigated, only membership on the senior national team and the volitional factor,


29

avoiding effort, significantly differentiate between the two groups as well as predict

membership in the elite group.

Practical implications Team Sports

Summary of the most important results:

Team – Result 1: There are limited differences between elite and sub‐elite athletes. For

the whole group, the following results emerged: The career in the main sport starts in

average at age 6‐6.5, they progress to the development stage5 around age 12, and then to

the perfection stage6 at around age 15.5; the first national competition takes place

shortly before the 13th birthday, while the first international competition takes place at

around age 15.5; on average, the athletes spend 3.5 years on the junior national team;

the athletes are involved in one to two additional sport(s) for about 75 months in total.

Team – Result 2: Both, being selected to the national team and spending as many years

as possible on the team, are beneficial for reaching the absolute top.

Team – Result 3: The volitional factor, self‐determination, is more pronounced in elite

athlete than in sub‐elite athletes.

Team – Result 4: Elite athletes are less likely to avoid effort than sub‐elite athletes.

The above results led to the following practical implications:

Obviously, there is no “right” or “wrong” regarding the career paths for athletes in team

sports (see team – R1): therefore, focus on the individual in order to design an optimal

way; e.g. don’t force developmentally (age) retarded athletes to engage too early, but

give them time for their development, do not pressure young athletes focus solely on

one sport. Remember that it is very important to develop high levels of volition, because

it is easier for athletes who perceive sports to be fun, who feel competent and have

made positive experiences during their early sport involvement. The only goal for the

athlete should be to have reached a high enough skill level by the time selections for the

senior national team are made.

Try to prepare the young athletes for the (relatively) late selection to the national team,

and do not focus too much on early success (see team – R2).

Support young athletes in developing beneficial volitional characteristics (high levels of

self‐determination and low levels of avoiding effort) during the early stages of their

5 During the developmental stage, the amount of training increases, athletes specialize in one sport and start competing on a regional / national level. Typically during this stage, athletes narrow their focus to one or two sport disciplines that they are hooked by and committed to 6 In the Perfection stage, athletes start competing at the highest level, at international competitions. During this stage, athletes become experts in their sport and feel responsible for their practices and competition performances


30

career: offer opportunities to do so by providing psychological skills training with

educated sport psychologists, and/or educate coaches in this domain so they can foster

those skills during daily practice (e.g. arrange practice activities in a way that make

them very specific for each individual; evaluate practice and match performance

individually in order to prevent athletes from hiding themselves within the team; see

team – R3 and R4).

4.3 Precision Sports

Sample Precision Sports

63 of the sampled athletes were engaged in a precision sport, whereof 46 could be classified as

either elite or sub‐elite. Table 7 displays the distribution between the athletes of the different

sport and the success categories.

40 athletes were currently active in their main sport at

the time of the survey, while 6 had retired before the

survey was conducted. The mean age of 23 female and

23 male athletes is 25.02, with a range from 15 to 40

years. The elite athletes were older (M = 26.57, SD =

6.38) than the sub‐elite athletes (M = 20.09, SD = 4.97).

Results Precision Sports7, 8

Group comparisons revealed only significant results in

two variables (Appendix 6): elite athletes spend more years on the junior and senior national

team than their sub‐elite peers. However, for the latter result, it needs to be kept in mind that

the elite group is significantly older than the sub‐elite group, which could explain the difference

in membership on the senior national team. The remaining variables reveal no statistical

significant results. This could be due to the small sample sizes, which, from a statistical point of

view, complicates the yielding of significant results. Therefore, it is recommended to consider

the results as a description of the two groups, which can lead to interesting insights into the

career paths of these athletes.

7 Due to the small sample size, the results have to be interpreted cautiously, and might best be considered as descriptive information of athletes in precision sports. 8 Please find attached to this report a Bachelor thesis that is based on data from the current study and focuses on the results of the athletes within precision sports.


Bowling 8 8 0

Archery 6 5 1

Curling 8 6 2

Golf 18 11 7

Shooting 5 4 1

Others 1 1 0

Total 46 35 11

Table 7: The distribution of sport and success levels in the sample of precision sports.


31

Concerning the amount of practice hours, it can be seen in figure 3 that – even though

not exceeding the significant threshold – elite athletes train more from the start of their career

than sub‐elite athletes.

4.4 Racket Sports9,10

Sample Racket Sports

46 athletes in the current study were involved in racket sports, and could be categorized as

either elite or sub‐elite athletes. 4 different sports were executed. The distribution of the sports

and the group category is illustrated in table 8.

The sample included 33 athletes that were currently

active in their sport at the time of the survey; while 13

had retired prior to the conduction of the survey. The 19

female and 27 male athletes were in average 22.57 years

old, ranging from 15 to 40 years. The elite athletes were

older (M = 27.38, SD = 7.17) than the sub‐elite athletes (M

= 20.00, SD = 3.31).

9 Due to the small sample size, the results have to be interpreted cautiously, and might best be considered as descriptive information of athletes in precision sports. 10 Please find attached to this report a Bachelor thesis that is based on data from the current study and focuses on the results of the athletes within precision sports.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Training up to age

9

Training up to age

12

Training up to age

15

Training up to age

18

Training up to age

21

Elite

Sub‐elite


Badminton 29 13 16

Table Tennis 9 2 7

Squash 1 1 0

Tennis 7 0 7

Total 46 16 30

Figure 3: Development of practice hours at five different time points in the career.

Table 8: The distribution of sport and group category.


32

Results Racket Sports

Comparisons between the two groups revealed significant results in only two of the investigated

variables (Appendix 7): elite athletes enter the perfection stage at a significantly later time point

in their career than sub‐elite athletes. Moreover, elites spend more years on the senior national

team than their sub‐elite peers. However, as was the case for athletes from precision sports, this

result needs to be interpreted with caution, as the elite athletes of this sample are much older

than the sub‐elite athletes (27.4 vs. 20), giving them more opportunities to be a member on the

senior national team. No other group comparison exceeded the significant threshold, which

again can be due to the fact that the sample size of the two groups is very small.

The development of practice hours of the elite and sub‐elite athletes is shown in figure 4.

As shown, the elite athletes train less during adolescence, but seem to “catch up” by the age of

21.

Addendum II: Limitations of the study

Even though interesting results emerged from the present study, some limitations must be

considered when making inferences:

1) The use of retrospective data collection, which is commonly considered to be prone to

inaccuracies in recall (Hodges et al., 2007), could have led to distorted data. However,

retrospective investigations will remain the main source of information when dealing with high

elite performance as long as it is not possible to foresee which athletes will succeed on their way

to the top (Côté et al., 2005). To check for biases, several steps were taken to validate the data,

which revealed satisfying results that are comparable with those from other studies (Helsen et

0

1000

2000

3000

4000

5000

6000

7000

8000

Training up to age 9





Elite

Sub‐elite

Figure 4: Development of practice hours at five different time points.


33

al., 1998; Hodges et al., 2004). Additionally, Côté et al. (2005, p. 16) concluded, that “athletes

were able to accurately recall many aspects of their development even after decades had

elapsed.” It can be hypothesised that training activities played such an important part in the

athletes´ lives that they recall accurate numbers.

2) It needs to be considered that, due to the cross sectional design of the study,

conclusions on the causal effect of practice cannot be made. To address that flaw, longitudinal

studies should be conducted in the future.

3) In the current study, no information concerning the content of practice was gathered.

Therefore, conclusions cannot be drawn about the importance of play‐like training activities

(e.g. deliberate play; Côté et al., 2007) nor about the importance of different forms of training

activities (e.g. solitary vs. group practice; Ericsson, 2003). Therefore, it cannot be excluded that

some athletes experienced a very multifaceted training and had opportunities to sample

different sport experiences during the practice hours in their main sport.

4) Likewise, no information concerning the quality of training is available. Therefore, it

cannot be assumed that the reported practice hours solely consist of what Ericsson et al. (1993)

consider as deliberate practice; which is characterised as a highly structured activity aiming at

further improving one’s current performance level.


34

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Appendixes

Appendix 1: Cgs Sports Mean, standard deviation and group comparison of athletes within cgs sports

Elite Subelite Dropouts Scale n M SD n M SD n M SD F df p Eta2 Posthoc1 Number of other sport 87 2.60 1.50 51 2.25 1.41 20 2.40 1.19 0.938 2, 155 0.394 0.012 ‐ Entering Initiation Stage 134 11.91 5.41 86 8.63 4.72 38 9.03 2.76 13.64 2, 255 <.001 0.097 e‐s, e‐d Entering Development Stage 135 15.34 4.49 87 12.93 4.38 38 12.11 2.41 13.41 2, 257 <.001 0.094 e‐s, e‐d Entering Perfection Stage 134 18.63 4.89 79 16.47 4.58 38 14.78 2.03 13.27 2, 248 <.001 0.097 All First national competition 139 14.77 5.22 87 12.51 4.38 38 11.74 1.98 10.01 2, 261 <.001 0.071 e‐s, e‐d First international competition 138 17.65 5.22 79 15.65 4.25 36 13.97 1.81 11.19 2, 250 <.001 0.082 All Years of membership in junior national team

139 2.30 1.84 87 2.90 2.38 38 2.76 1.81 2.51 2, 261 0.083 0.019 ‐

Years of membership in senior national team

139 4.73 3.30 87 1.67 2.52 38 0.84 1.28 45.93 2, 261 <.001 0.26 all

AMS – striving for success 139 9.60 3.16 87 10.18 2.78 35 10.40 3.28 1.524 2, 258 0.22 0.012 ‐ AMS – fear of failure 139 3.63 3.46 87 3.89 2.89 35 2.89 2.63 1.238 2,258 0.292 0.010 ‐ VCQ – self‐determination 137 2.65 0.39 87 2.59 0.43 35 2.50 0.59 1.858 2, 256 0.158 0.014 ‐ VCQ – avoiding effort 137 0.44 0.50 87 0.49 0.49 35 0.56 0.53 0.948 2, 256 0.389 0.007 ‐ VCQ – lack of energy 137 0.88 0.55 87 0.89 0.55 35 1.03 0.52 1.10 2, 256 0.335 0.009 ‐ VCQ – postponing training 137 0.32 0.45 87 0.34 0.48 35 0.53 0.57 2.885 2, 256 0.058 0.022 ‐


39

Appendix 2: Cgs Sports Results of the logistic regression from cgs athletes with data on practice hours, involvement in other sports and career development as predictor and athletic success as dependent variable Variable Coefficient SE Wald df p Exp(B) Confidence Interval Low High Membership Junior National Team ‐.54 .14 15.74 1 <.001 .59 0.45 0.76 Membership Senior National Team .44 .11 16.04 1 <.001 1.56 1.26 1.94 Age ‐.08 .05 2.13 1 .14 .93 0.84 1.02 Training up to age 12 .00 .00 1.42 1 .23 1.00 1.00 1.00 Training up to age 15 ‐.00 .00 16.31 1 <.001 1.001 1.001 1.001 Training up to age 18 .00 .00 24.05 1 <.001 1.001 1.001 1.001

1 These values have been rounded to 1.00, but do reflect a significant result and no Confidence Interval includes the exact number of 1.00.


40

Appendix 3. Team Sports Mean, standard deviation and group comparison of athletes within team sports

Elite Subelite Scale Sport career

n M SD n M SD T df p |d|

Accumulated practice hours up to age 9 34 664.53 651.53 61 791.08 753.03 0.82 93 .41 0.18Accumulated practice hours up to age 12 34 1469.76 1218.23 61 1748.39 1364.58 0.99 93 .33 0.22Accumulated practice hours up to age 15 34 3048.88 1913.72 61 3328.43 2047.32 0.65 93 .52 0.14Accumulated practice hours up to age 18 34 5248.94 2681.29 61 5452.33 2793.21 0.35 93 .73 0.07Accumulated practice hours up to age 21 34 6905.29 3198.96 61 6826.49 3462.40 ‐0.11 93 .91 0.02Number of other sports 63 1.62 1.83 112 1.54 1.60 ‐0.31 173 .75 0.05Months of involvement in other sports 63 84.94 116.83 111 61.45 80.04 ‐1.57 172 .12 0.23Entering Initiation stage 63 6.48 2.68 111 6.06 2.33 ‐1.08 172 .28 0.17Entering Development stage 63 12.13 2.54 111 12.09 2.06 ‐0.10 172 .92 0.02Entering Perfection stage 62 15.83 2.23 111 15.28 1.64 ‐1.86 171 .07 0.28First national competition 63 12.97 3.80 112 12.70 2.76 ‐0.50 99 .62 0.08First international competition 63 15.70 2.15 112 15.31 2.16 ‐1.14 173 .26 0.18Years in junior national team 63 3.62 1.65 112 3.23 1.94 ‐1.33 173 .18 0.22Years in senior national team 63 3.06 3.63 112 0.68 1.78 ‐4.90 79 <.05 0.83AMS – hope for success 63 10.95 2.60 112 10.20 2.81 ‐1.75 173 .08 0.28AMS – fear of failure 63 3.35 2.51 61 791.08 753.03 1.09 173 .28 0.17VCQ – self‐determination 60 2.76 0.33 61 1748.39 1364.58 ‐2.18 169 <.05 0.37VCQ – avoiding effort 60 0.43 0.46 61 3328.43 2047.32 2.46 169 <.05 0.40VCQ – lack of energy 60 0.73 0.53 61 5452.33 2793.21 0.84 169 .40 0.13VCQ – postponing training 60 0.33 0.52 61 6826.49 3462.40 0.58 169 .56 0.09


41

Appendix 4: Team Sports ‐ football Means, standard deviations and group comparisons of athletes in football

Elite Subelite Dropouts Scale n M SD n M SD n M SD F df p Eta2 Posthoc11 Number of other sports 16 2.69 2.30 44 2.59 1.30 10 1.70 0.82 1.55 2, 67 0.22 0.044 ‐ Entering Initiation Stage 23 6.91 2.61 65 5.95 2.35 15 5.93 1.94 1.49 2, 100 0.23 0.029 ‐ Entering Development Stage 23 12.35 2.48 65 12.28 1.81 15 12.73 1.16 0.35 2, 100 0.71 0.007 ‐ Entering Perfection Stage 23 15.41 2.10 65 15.18 1.65 14 15.07 0.83 0.221 2, 99 0.80 0.004 ‐ First national competition 23 14.04 2.03 65 12.66 2.41 15 13.93 1.87 4.22 2, 100 <.05 0.078 e‐s First international competition 23 15.78 1.44 65 15.21 1.92 15 15.20 1.32 0.945 2, 100 0.39 0.019 ‐ Years of membership in junior national team

23 3.78 1.65 65 3.43 2.12 15 2.13 1.51 3.54 2, 100 <.05 0.066 d‐e, d‐s

Years of membership in senior national teams

23 2.57 3.26 65 0.58 1.36 15 0.67 0.26 11.41 2, 100 <.001 0.186 All

AMS – striving for success 23 11.00 2.63 65 10.08 2.91 14 9.14 3.86 1.74 2, 99 0.181 0.034 ‐ AMS – fear of failure 23 3.04 2.69 65 3.60 2.96 14 4.64 2.84 1.34 2, 99 0.267 0.026 ‐ VCQ – self‐determination 21 2.83 0.25 65 2.66 0.39 14 2.45 0.53 4.16 2, 97 <.05 0.079 ‐ VCQ – avoiding effort 21 0.48 0.48 65 0.57 0.48 14 1.00 0.66 5.06 2, 97 <.01 0.094 e‐d VCQ – lack of energy 21 0.80 0.59 65 0.76 0.50 14 1.11 0.62 2.42 2, 97 0.095 0.047 ‐ VCQ – postponing training 21 0.16 0.33 65 0.32 0.43 14 0.76 0.87 6.58 2, 97 <.01 0.119 ‐

11 Games‐Howell


42

Appendix 5: Team Sports Results of the logistic regression with data on practice hours, involvement in other sports, career development and motivation and volition as predictor and athletic success as dependent variable

Variable Coefficient SE Wald df p Exp(B) Confidence Interval Low High

Membership Senior National Team 0.35 0.11 9.91 1 <.005 1.42 1.14 1.77

VCQ – avoiding effort ‐0.90 0.46 3.87 1 <.05 .41 0.17 0.99

VCQ – postponing training 0.15 0.40 0.14 1 .71 1.16 0.53 2.56

Age ‐0.04 0.06 0.39 1 0.53 0.96 0.85 1.09


43

Appendix 6: Precision Sports Comparison between the elite and the sub‐elite group on data about practice hours, involvement in other sports, career development and motivation and volition (Means, standard deviation, mean differences and effect sizes)

Elite Subelite

Sport career n M SD n M SD T df p |d|

Training up to age 9 22 47.27 96.16 6 0.00 0.00 ‐2.31 21 <.05 0.70 Training up to age 12 22 442.00 547.44 6 182.00 318.43 ‐1.10 26 .28 0.58

Training up to age 15 22 1855.45 1510.80 6 1482.00 697.46 ‐0.58 26 .57 0.32



Number of other sports 35 1.40 1.79 11 2.27 2.10 1.36 44 .18 0.45

Number of other sports on recreational level 35 1.09 1.70 11 1.45 1.51 .64 44 .52 0.22

Number of other sports on regional / national 35 0.29 0.67 11 0.82 0.98 2.05 44 <.05 0.63

Numbers of other sports on international level 35 0.03 0.17 11 0.00 0.00 ‐0.56 44 .58 0.25

Months of involvement in other sports 35 83.37 105.09 11 151.82 259.83 1.28 44 .21 0.35

Months of involvement in other sports up to age 35 58.94 81.53 11 132.27 241.95 0.99 11 .35 0.41


Entering Initiation stage 35 10.23 2.79 11 9.82 2.99 ‐0.42 44 .68 0.14

Entering Development stage 35 13.34 2.70 11 13.45 2.54 0.12 44 .90 0.04

Entering Perfection stage 35 16.54 2.91 11 15.55 2.16 ‐1.05 44 .30 0.39

First national competition 35 12.34 3.14 11 12.82 2.75 0.45 44 .66 0.16

First international competition 35 15.83 2.66 11 15.64 2.34 ‐0.22 44 .83 0.08

Years in junior national team 35 3.31 1.49 11 1.82 1.08 ‐3.08 44 <.005 1.15

Years in senior national team 35 5.54 3.66 11 1.09 1.14 ‐6.29 44 <.001 1.64

AMS – hope for success 35 10.09 3.03 11 8.55 3.64 ‐1.40 44 .17 0.46

AMS – fear of failure 35 3.71 3.42 11 5.64 3.01 1.67 44 .10 0.60

VCQ – self‐determination 35 2.58 0.36 11 2.39 0.57 ‐1.33 44 .19 0.40

VCQ – avoiding effort 35 0.56 0.39 11 0.73 0.45 1.16 44 .25 0.40

VCQ – lack of energy 35 1.09 0.55 11 1.20 0.59 0.62 44 .54 0.19

VCQ – postponing training 35 0.35 0.40 11 0.55 0.79 0.78 12 .45 0.32


44

Appendix 7: Racket Sports Comparison between the elite and the sub‐elite group on data about practice hours, involvement in other sports, career development and motivation and volition (Means, standard deviation, mean differences and effect sizes)

Elite Subelite

Sport career n M SD n M SD T df p |d|

Training up to age 9 9 629.78 429.15 24 479.92 508.68 ‐0.78 31 .44 0.32 Training up to age 12 9 1612 824.25 24 1526.42 1081.36 ‐0.21 31 .83 0.09

Training up to age 15 9 3200.89 1418.16 24 3391.92 1701.28 0.30 31 .77 0.12



Number of other sports 16 .94 1.34 30 1.73 1.66 1.65 44 .11 0.52

Number of other sports on recreational level 16 0.38 0.89 30 0.90 1.37 1.38 44 .18 0.45

Number of other sports on regional / national 16 0.56 0.89 30 0.80 0.96 0.82 44 .42 0.26

Numbers of other sports on international level 16 0.00 0.00 30 0.03 0.18 0.73 44 .47 0.24

Months of involvement in other sports 16 57.06 79.92 30 100.93 129.87 1.23 44 .23 0.41



Entering Initiation stage 15 6.53 1.68 30 7.97 2.48 2.01 43 .05 0.68

Entering Development stage 15 12.27 2.25 30 11.60 2.28 ‐0.28 43 .36 0.30

Entering Perfection stage 15 17.67 2.74 30 14.53 2.42 ‐3.92 43 <.001 1.21

First national competition 16 11.00 3.01 30 10.83 2.20 ‐0.22 44 .83 0.06

First international competition 16 14.69 2.24 30 13.43 2.05 ‐1.92 44 .06 0.59

Years in junior national team 16 4.25 2.44 30 4.87 1.87 0.96 44 .34 0.33

Years in senior national team 16 4.37 3.88 30 1.00 1.89 ‐3.28 18.90 <.01 1.10

AMS – hope for success 16 10.31 2.94 30 9.63 3.42 ‐0.67 44 .51 0.21

AMS – fear of failure 16 3.13 2.39 30 3.83 3.28 0.76 44 .45 0.24

VCQ – self‐determination 16 2.75 0.33 30 2.70 0.36 ‐0.47 44 .64 0.14

VCQ – avoiding effort 16 0.44 0.45 30 0.54 0.47 0.72 44 .47 0.22

VCQ – lack of energy 16 0.63 0.51 30 0.91 0.61 1.58 44 .12 0.50 VCQ – postponing training 16 0.21 0.40 30 0.39 0.51 1.22 44 .23 0.39

Department of Exercise and Sport SciencesFaculty of Science, University of CopenhagenNørre Allé 51, DK-2200 Copenhagen NTel.: +45 3532 0829 Fax: +45 3532 0870E-mail: [email protected] – www.ifi.ku.dk


Karin MoeschAnne-Marie ElbeMarie-Louise Trier HaugeJohan Wikman

A controversial question within elite sports is whether or not young athletes need to specialize at an early age, or if it is more benefi cial to follow the path of early diversifi cation. This path includes sampling different sport experiences during childhood and then specia lizing later during adolescence. To explore this question, the career paths of Danish elite athletes were investigated.

The main research question addressed the differences between elite and near-elite athletes using data concerning the amount of practice hours during the career, engagement in addi-tional sports, time of specialization into the main sport, as well as the sport-specifi c achieve-ment motive and volitional factors. A total of 722 Danish elite athletes from 34 different sports replied to the questionnaire. In order to prevent a too heterogeneous sample, all analyses were conducted for groups of sports with similar requirements.

The results concerning the career paths of athletes from cgs sports, team sports, precision sports, and racquet sports are presented and discussed. Moreover, fi ndings on the differences between elite, near-elite athletes, and dropouts are provided for cgs athletes and football players.

talent development in danish elite athletes -...

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