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Electronic Theses and Dissertations Theses, Dissertations, and Major Papers

2017

Developmental Activities of Ontario Hockey League Players Developmental Activities of Ontario Hockey League Players

William Joseph Garland University of Windsor

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Developmental Activities of Ontario Hockey League Players

By

William Joseph Garland

A Thesis

Submitted to the Faculty of Graduate Studies through the Department of Kinesiology

in Partial Fulfillment of the Requirements for the Degree of Master of Human Kinetics

at the University of Windsor

Windsor, Ontario, Canada

2017

© 2017 William Joseph Garland

Developmental Activities of Ontario Hockey League Players

by

William Joseph Garland

APPROVED BY:

______________________________________________ D. Bussière

Odette School of Business

______________________________________________ C. Greenham

Department of Kinesiology

______________________________________________ J. Dixon, Co-Advisor

Department of Kinesiology

______________________________________________ S. Horton, Co-Advisor

Department of Kinesiology

December 11, 2017

iii

DECLARATION OF ORIGINALITY

I hereby certify that I am the sole author of this thesis and that no part of this thesis

has been published or submitted for publication.

I certify that, to the best of my knowledge, my thesis does not infringe upon

anyone’s copyright nor violate any proprietary rights and that any ideas, techniques,

quotations, or any other material from the work of other people included in my thesis,

published or otherwise, are fully acknowledged in accordance with the standard

referencing practices. Furthermore, to the extent that I have included copyrighted material

that surpasses the bounds of fair dealing within the meaning of the Canada Copyright Act,

I certify that I have obtained a written permission from the copyright owner(s) to include

such material(s) in my thesis and have included copies of such copyright clearances to my

appendix.

I declare that this is a true copy of my thesis, including any final revisions, as

approved by my thesis committee and the Graduate Studies office, and that this thesis has

not been submitted for a higher degree to any other University or Institution.

iv

ABSTRACT

Theoretical frameworks such as the Developmental Model for Sport Participation

encourage multi-sport participation at a young age, and many practitioners warn that

early sport specialization may be associated with several negative physical and

psychosocial consequences. Despite this advice, the lure of extrinsic rewards has caused

children to specialize in one sport at the expense of other activities at an alarming rate.

The purpose of this study was to understand the developmental histories of current and

former Ontario Hockey League players. Fifteen participants, completed quantitative

retrospective interviews, detailing their past sport and recreational activities. The

accumulated hours of deliberate practice reported by participants increased throughout

the ages of 6 to 16, as did the number of hours competing in organized hockey games.

The reported number of deliberate play hours peaked at 9 years of age and decreased

thereafter. Additionally, participants played a combined 16 sports other than hockey, and

accumulated the most hours in these sports at 12 years old. Using a three-point scale,

participants were considered ‘highly-specialized’ at 14 years old, however quantitative

indicators suggest that this may have occurred at the age of 12 years old. Relative to

previous research, participants of the current study appear to spend more time practicing

hockey, while ceasing hockey-specific play and other sports at a younger age.

Furthermore, despite a diverse sport history, participants initiated hockey competition

prior to the age suggested by Hockey Canada, and show high levels of commitment to

hockey as young as 9 years old.

v

DEDICATION

I would like to dedicate my work to the unrelenting support of my wife, Amanda,

and my son, Liam. Without their understanding, encouragement, and countless sacrifices,

this undertaking would not have been possible.

vi

ACKNOWLEDGEMENTS

I would first like to thank my co-advisors, Dr. Jess Dixon and Dr. Sean Horton,

for all their help and support throughout this process. I really did not know what I was

getting myself into when I started down this path, but they were extremely helpful in

guiding me, and making sure that I was always on track.

Secondly, I am grateful for Dr. Greenham’s and Dr. Bussière’s comments and

advice, which helped shape the research into the final product that it became.

Additionally, the interview script provided by Dr. Jean Côté from Queen’s University

allowed for the data to be easily collected and organized.

As well, I would like to thank Dr. Vicky Paraschak for encouraging me to pursue

my Master’s degree, and always showing interest in my work.

Finally, I am indebted to all of those who participated in the study. Despite the

length of the interviews, the participants were enthusiastic and open with all their

responses. Without their full cooperation, this research would have not been possible, and

an opportunity to help future generations of Canadian hockey players would have been

missed.

vii

TABLE OF CONTENTS

DECLARATION OF ORIGINALITY iii

ABSTRACT iv

DEDICATION v

ACKNOWLEDGEMENTS vi

LIST OF TABLES x

LIST OF FIGURES xii

LIST OF APPENDICES xiii

LIST OF ABBREVIATIONS/SYMBOLS xiv

RESEARCH ARTICLE 1

Introduction 1

Literature Review 2

Potential Impacts of Early Specialization 4

Potential Benefits of Sport Sampling 5

Purpose 7

Methods 8

Participants 8

Questionnaire/Instruments 9

Procedure 10

Reliability 11

Data analyses 12

Results 13

viii

Participants 13

Accumulated activity hours 14

Developmental Milestones 15

Effort, Concentration, and Fun 17

Other Sports 18

Supplemental Questions 20

Discussion 21

Hockey-Specific Deliberate Play 22

Hockey-Specific Deliberate Practice 23

Organized Hockey Games 24

Other Sports 25

Supplemental questions 27

Conclusion 30

References 33

LITERATURE REVIEW 87

Definitions 87

Background 89

Current Trends 91

Developmental Models 93

Deliberate Practice 93

Developmental Model of Sports Participation 94

ix

Long-Term Athletic Development 96

Optimisation of Sport and Athlete Development 97

Position Statements 98

Potential Benefits of Early Specialization 99

Potential Adverse Effects of Early Specialization 100

Potential Benefits of Sport Sampling 104

Possible Mechanisms for Benefits of Sampling 109

Purpose 111

References 116

APPENDICES 137

Appendix A 137

Appendix B 145

Appendix C 151

Appendix D 153

Appendix E 157

Appendix F 168

Appendix G 172

Appendix H 178

VITA AUCTORIS 180

x

LIST OF TABLES

Table 1. Descriptive statistics of accumulated activity hours by age 59

Table 2. Descriptive statistics for sub-divided 'sport' categories 60

Table 3. Descriptive statistics for the emergence of developmental milstones (in years) 61

Table 4. Descriptive statistics for the first emergence of developmental milestones (in

years) 62

Table 5. Number of participants at each level of hockey for each age 63

Table 6. Participant' ages relative to that of other players in their respective leagues 64

Table 7. Qualitative information regarding injuries for hockey activities for each age 65

Table 8. Descriptive statistics for intensity, reseources, hockey activity hours reported,

and hockey activities calculated 66

Table 9. Subjective ratings of effort, concentration, and fun for organized games

(subjective ratings on a scale of 0-100) 67

Table 10. Subjective ratings of effort, concentration, and fun for practices (subjective

ratings on a scale of 0-100) 68

Table 11. Subjective ratings of effort, concentration, and fun for self-initiated activities

(subjective ratings on a scale of 0-100) 69

Table 12. Descriptive statistics for hours of invasion sports by age (excluding hockey) 70

Table 13. Descriptive statistics for hours of run scoring/field sports by age 71

Table 14. Descriptive statistics for hours of net sports by age 72

Table 15. Table 15. Descriptive statistics for hours of target sports by age 73

Table 16. Descriptive statistics for hours of individual sports by age 74

Table 17. Specific sport participation by age 75

xi

Table 18. Answers to questions regarding the degree of specialization 76

Table 19. Answers to qualitative questions of issues regarding specialization 77

Table 20. Responses to supplemental questions regarding specialization 78

xii

LIST OF FIGURES

Figure 1. The percentage of the total accumulated hours divided by category for each age.

79

Figure 2. The combined accumulated hour totals for hockey-specific deliberate practice,

hockey-specific deliberate play, organized hockey games and sports other than

hockey at each age 80

Figure 3. The maximum, minimum, and median accumulated hour values of hockey-

specific deliberate practice activities by age 81

Figure 4. The maximum, minimum, and median accumulated hour values of hockey-

specific deliberate play activities by age 82

Figure 5. The maximum, minimum, and median accumulated hour values of organized

hockey games by age 83

Figure 6. The maximum, minimum, and median accumulated hour values of sports other

than hockey by age 84

Figure 7. Percentage of total sport hours divided by hockey-specific deliberate practice

activities, hockey-specific deliberate play, organized hockey games and sports other

than hockey at each age 85

Figure 8. The combined accumulated hours for all participants 86

xiii

LIST OF APPENDICES

Appendix A. Interview script 137

Appendix B. Interview charts 145

Appendix C. Expanded discussion for deliberate play 151

Appendix D. Comparison of results to accepted developmental models 153

Appendix E. Expanded discussion for developmental milestones and effort,

concentration, and fun 157

Appendix F. Expanded discussion for deliberate practice 168

Appendix G. Expanded discussion for other sports 172

Appendix H. Expanded discussion for requirement to specialize 178

xiv

LIST OF ABBREVIATIONS/SYMBOLS

ASA American Sports Act

CHL Canadian Hockey League

DMSP Developmental Model of Sports Participation

FAI Femoroacetabular Impingement

IOC International Olympic Committee

Jr. B Junior B

Jr. C Junior C

LTAD Long-Term Athletic Development

Max Maximum

Min Minimum

NCAA National Collegiate Athletic Association

NHL National Hockey League

OHL Ontario Hockey League

S.D. Standard Deviation

TV Television

USA United States of America

USSR United Soviet Socialist Republics

USSSA United States Specialty Sports Association

1

RESEARCH ARTICLE

Introduction

Sport specialization is the selection of one sport at the expense of other sports or

activities (Brenner, 2016; Ferguson & Stern, 2014). The optimal age for specialization

has not been agreed upon in the literature despite the topic being covered extensively

(e.g., Feeley, Agel, & LaPrade, 2016; Myer et al., 2015a). Some sources state that

accumulated hours of deliberate practice are a necessary component to attain excellence

(Ericsson, Krampe, & Tesch-Römer, 1993; Williams & Hodges, 2005), whereas other

sources argue that investing in one sport too early can be detrimental (Côté, 1999; Myer

et al., 2015b). While artistic and kinesthetic sports may require specialization before

puberty, participating in a range of activities can foster positive experiences, such as

prosocial behaviour and the acquisition of social capital (Balyi, Way, & Higgs, 2013;

Brenner, 2016; Côté, Lidor, & Hackfort, 2009; Wall & Côté, 2007). Moreover, studies

have shown that specializing after puberty does not negatively affect one’s ability to

develop expertise (Bridge & Toms, 2013; Soberlak & Côté, 2003).

LaPrade et al. (2016) stated that early specialization occurs when athletes train

eight months a year, and are limited to one sport as prepubescents. Jayanthi, LaBella,

Fischer, Pasulka and Dugas (2015) quantified the definition by creating a continuum,

classifying athletes as low, moderately, or highly specialized. Athletes in this study were

asked ‘if they could identify one sport as a main sport,’ ‘if they had quit other sports for

their main sport,’ and ‘if they trained for their main sport for eight or more months a

year.’ Answering ‘yes’ to one of these questions qualified for a low score, two

2

affirmative answers was considered moderately specialized, and a score of three was

considered highly specialized.

While empirical research regarding specialization and long-term athletic

development (LTAD) is found for several sports (e.g., soccer (Livingston, Schmidt, &

Lehman, 2016), volleyball (Coutinho, Mesquita, Fonseca, & Côté, 2015), athletics

(Moesch, Elba, Hauge, and Wikman, 2011)), relatively little is available for hockey. The

purpose of this study is to analyze the developmental activities of elite hockey players to

investigate elements of specialization, and enhance LTAD literature for one of Canada’s

national sports.

Literature Review

In the 1950s, Eastern European countries introduced extensive training programs

in the quest for political power through Olympic success (Gonçalves, Rama, &

Figueiredo, 2012; Washburn, 1956). Media portrayals of these athletes promoted year-

round participation in a single sport at a young age, and led to coaching philosophies

migrating West and privatized training programs being developed (Malina, 2010).

Political changes in the 1980s then led to further privatization and commercialization of

sport programs in an attempt to cut government spending (Coakley, 2010).

The belief that intense, year-round participation is the best route to an athletic

scholarship or a professional contract has resulted in a drastic rise in privatized programs,

year-round participation, and a decrease in the number of multi-sport athletes (Coakley,

2010; Côté & Hancock, 2016; Jayanthi, Pinkham, Dugas, Patrick, & LaBella, 2012;

Lloyd, Faigenbaum, Howard, De Ste Croix, & Williams, 2012). A National Collegiate

Athletic Association (NCAA) study found that more than 50% of male soccer and hockey

3

players had specialized by 12 years of age (Schwarb, 2016). Parents do not want to

acknowledge that their children may fall victim to the pitfalls of early specialization

when chasing these extrinsic rewards. This has resulted in the parents pushing their

children into the adult-centric environment seen in youth sport (Laprade et al., 2016).

Ericsson et al. (1993) explained a monotonic relationship between practice and

performance, which has garnered support for deliberate practice in sport (Helsen, Starkes,

& Hodges, 1998; Williams & Ford, 2008). The study supported the 10-year model

(Simon & Chase, 1973), suggesting that 10 years of meaningful practice is needed to

demonstrate expertise in a given domain. This rule has been demonstrated in sport as

well; however, the hours of deliberate practice accumulated within these years may vary

(Baker, Côté, & Abernethy, 2003). While Ericsson et al. (1993) studied musicians, the

authors suggested that superior athletes were created due to physiological differences

from a higher volume of training, not from any innate ability.

In contrast to the deliberate practice model, Côté (1999) explained an athlete’s

development through the three-stage developmental model of sport participation

(DMSP). The first stage, referred to as ‘sampling’ (i.e., ages of 6 to 13), is when athletes

experience several different sports. During the ‘specialization’ stage (i.e., ages of 13 to

15), athletes commit to one or two sports and achievement becomes more important.

Finally, during the ‘investment’ stage, athletes (over 15 years of age) commit to one sport

with the hope of attaining an elite level of performance. There does exist the possibility

of an additional fourth stage, if athletes have attained an elite level, and are working

towards perfecting the skills of their sport.

4

Rather than using specific ages, Balyi et al. (2013) proposed the use of

developmental progressions in a seven-stage model, which advances from fundamental

motor skills to competition and retirement. The first stages promote the skills required for

physical literacy, and are recommended to be completed before adolescence. High

performance athletes may progress through stages by showing key psychosocial and

physical indicators. Hockey Canada (2013) has adapted this model, making the stages

specific to minor hockey in Canada. This program is intended to increase physical

literacy, promote life-long engagement in hockey, and encourage players to increase the

specificity of training towards hockey at 12 years of age.

Potential Impacts of Early Specialization

There is a positive correlation between practice and performance, although the

recommended amount and type of training differs among experts (Baker et al., 2003;

Barker, Barker-Ruchti, Rynne, & Lee, 2014; Helsen et al., 1998; Livingston et al., 2016;

Williams, Ward, Bell-Walker, & Ford, 2011). It is important to be identified as an elite

athlete at a young age because early decisions about his or her talents may influence

participation opportunities throughout development (Carlson, 1993). However,

prepubescent success attained by specializing is often due to physical factors (e.g.,

relative weight, height), and may not be sport-specific (Baker et al., 2003; Barreiros,

Côté, & Fonseca, 2014).

Given that accumulating more practice by age 15, or being selected for elite junior

teams, does not necessarily predict success at higher levels, specializing too early may

cause a player to reach his or her highest level of play prematurely (Barreiros et al., 2014;

Moesch et al., 2011). Moreover, intense training without adequate rest has been identified

5

as an independent risk factor for injury (Bell et al., 2016; Jayanthi et al., 2012). This

increased injury rate may be due to repeating the same movement, strength and flexibility

imbalances, inadequate neuromuscular skills to prevent injury, and specialized athletes

may push themselves with more intensity (Jayanthi et al., 2012; Myer et al., 2015a; Myer

et al., 2015b). A common overuse injury in hockey players is femoroacetabular

impingement (FAI)1 which occurs due to the repetitive biomechanics of skating (Stull,

Philippon, & LaPrade, 2011). Risk factors for FAI include on-ice exposure and younger

start ages, and the prevalence increases with age showing that hockey-specific

movements may lead to the development of FAI (Stull et al., 2011). This is noteworthy,

as hockey reports a high incidence of early specialization (McFadden, Bean, Fortier, &

Post, 2016). Psychosocially, early specialization may alter relationships, create socially

maladaptive behaviour, increase anxiety, depression, burnout and decrease intrinsic

motivation. These are attributed to the pressure created by overly structured, and adult-

driven, training schedules (Brenner, 2016; Myer et al., 2015a; Russell, 2014;

Weissensteiner, Abernethy, & Farrow, 2009).

Potential Benefits of Sport Sampling

Deliberate play is characterized by voluntary and pleasurable participation in

sport, providing immediate gratification and is a major component of sampling (Côté,

1999). These activities facilitate performance by helping to build resistance to stress,

1 Femoroacetabular impingement is characterized by ‘cam’ and ‘pincer’ bony

abnormalities to the femoral neck or acetabular rim respectively (Philippon, Ho, Briggs,

Stull, & LaPrade, 2013).

6

heighten one’s sense of competence, and develop intrinsic motivation (Brylinsky, 2010;

Côté & Fraser-Thomas, 2008; Masters, 2008; Weissensteiner et al., 2009). Successful

athletes may be more willing to participate in deliberate practice later in their

development from having gained this intrinsic motivation (Vink, Raudsepp, & Kais,

2015). Furthermore, sampling in the developmental years may decrease the time required

to attain expertise, and is a key determinant of life-long engagement in sport (Baker et al.,

2003; Berry, Abernethy, & Côté, 2008; Fransen et al., 2012; Güllich & Emrich, 2014;

Lidor & Lavyan, 2002; Mostafavifar, Best, & Myer, 2013). Elite hockey players have

been found to engage in deliberate play from the ages of 6 to 12, and then increase

deliberate practice and competitive activities as they progress through their careers

(Carlson, 1993; Robertson-Wilson, 2002; Soberlak & Côté, 2003). Of note, sampling

may be enjoyable for athletic children because they may have the ability to be successful

in several sports (Bridge & Toms, 2013; Capranica & Millard-Stafford, 2011).

The transfer of learning involves applying previous experiences to current

circumstances, which is considered ‘near’ transfer if the sports are similar, or ‘far’

transfer if the sports are different (Rosalie & Müller, 2012). Near and far transfer have

garnered support because it is believed that global movements serve as building blocks to

more sport-specific movements due to the neural plasticity of pre-adolescent athletes

(Allard & Starkes, 1992; Güllich & Emrich, 2014; Lloyd et al., 2012; Smeeton, Ward, &

Williams, 2004). These movements may be taught through physical education classes, or

a structured training program (Lloyd et al., 2012; Pesce, Faigenbaum, Crova, Marchetti,

& Bellucci, 2012). However, initiating off-ice training too young may cause players to

7

drop out, showing that structured training may not eliminate all the detrimental effects of

early specialization (Wall & Côté, 2007).

Purpose

Following sport sampling recommendations for hockey in Canada has its

challenges. The Canadian Hockey League (CHL) is the primary developmental league for

the National Hockey League (NHL), with more players drafted to the NHL than any

other junior league (“About the CHL”, n.d; National Hockey League, 2015). Players

enter the CHL as early as 14 years old; therefore, players may feel pressure to show a

superior ability prior to that age (Brenner, 2016; Western Hockey League, n.d.). The

Hockey Canada (2013) developmental model recommends initiating hockey-specific

training at the age of 12, leaving a limited window of opportunity for players to elevate

their skills above the competition. The existing developmental literature involving CHL

players (e.g., Robertson-Wilson, 2002; Soberlak & Côté, 2003) was published before the

1999 Molson Open Ice Summit on player development. As a consequence of this summit,

Hockey Canada changed its grassroots programs, and recommended the implementation

of ‘skills academies’ throughout the country (Hockey Canada, 2005). These changes led

to as many as 5,000 elementary and secondary schools offering hockey as part of their

curricula, allowing minor hockey players as young as 12 years old, to focus on activities

specific to hockey (Hockey Canada, 2005; Traikos, 2015).

In the current study, the developmental activities of CHL players potentially

affected by changes occurring after the Open Ice Summit were analyzed (Hockey

Canada, 2005). Accumulated hours of activity were categorized in accordance with the

DMSP, along with the number and types of sports that were participated in during

8

childhood. Finally, Jayanthi et al.’s (2015) continuum was utilized to determine the age at

which the athletes became highly specialized.

I hypothesized that the athletes who volunteered to participate in this study would

have a diverse athletic background early in their careers (e.g., sampling years); however,

‘specialization’ and ‘investment’ would occur earlier than suggested by the DMSP. I

further hypothesized that the athletes would be considered highly specialized, according

to Jayanthi et al.’s (2015) continuum, prior to the age of 14.

Methods

Participants

Potential participants were current or former Ontario Hockey League (OHL)2

players who were born no earlier than January 1st, 1994, and have spent a portion of their

developmental years in the Kent or Essex counties of Ontario, Canada. This inclusion

criterion was chosen to create a sample of hockey players that have played at an elite

level. All participants initiated their developmental activities after the Open Ice Summit

on player development (Hockey Canada, 2005). Participants were from a relatively small

geographical area in Ontario giving similar proximity to training resources. Patton (2002)

refers to this type of criterion-based sampling as purposeful sampling, which is intended

to create an information-rich and homogeneous sample for an in-depth analysis of cases

that have been determined to be important.

2 All participants were male due to the inclusion criteria involving current and former

OHL players.

9

Questionnaire/Instruments

The retrospective semi-structured interview used in this study (see Appendix A)

was an adapted version of the guide developed by Côté, Ericsson, and Law (2005) to

obtain quantitative information regarding the developmental activities of participants in

various stages throughout their childhood up to their time in the OHL. Versions of this

interview have been used in several previous studies for other sports, including triathlon

(e.g., Baker, Côté, & Deakin, 2005), volleyball (e.g., Barreiros, Côté, & Fonseca, 2013),

soccer (e.g., Ford et al., 2012), and swimming (e.g., Johnson, Tenenbaum, Edmonds, &

Castillo, 2008). The information collected from the interviews were recorded on four

separate charts (see Appendix B) based on questions from three sections: (a) ‘early

activities,’ (b) ‘maturation and performance in main sport,’ and (c) ‘relevant practice

activities in main sport.’ These charts resulted in longitudinal, methodical, and

comprehensive histories of the participants’ developmental activities. The interview also

involved closed-ended questions to add context by rating each activity on several

subjective measures, such as effort, mental concentration, and enjoyment.

The ‘early activities’ section created a list of activities that participants engaged in

throughout their development. Participants were asked to recall the number of hours per

week, and the number of months per year, for every year that they took part in each

activity. One adaptation from Côté et al.’s (2005) interview guide was the designation for

peak and off-peak months. This was included to account for the changes in training hours

between seasons. The ‘maturation and performance in main sport’ section asked each

participant for specific ages of developmental milestones, information regarding injuries,

subjective feelings towards training intensity and resources, and about his personal and

10

team accomplishments. Finally, the ‘relevant practice activities’ section regrouped the

information previously collected into periods of similar training quality and quantity.

These periods helped to quantify the time and effort required for the activities identified

in previous sections of the interview. All sections of the interview were interested in the

participants’ relevant activities from the age of 6 and throughout their development to

include those pursued at the time of the interview.

To identify the age at which participants became highly-specialized, the scale

suggested by Jayanthi et al. (2015) was utilized. Participants were asked when they chose

hockey as their main sport, when they engaged in hockey for more than eight months per

year, and when they first dropped out of other activities to focus their attention on

hockey. Participants were also asked if they had ever been told that they were not

permitted to play other sports; those who answered in the affirmative were also asked

who made the request, and what rationale was provided. The objective of this question

was to gain an understanding of why participants specialized in hockey. Participants were

then asked if they had ever attended a hockey academy during their development. Finally,

participants were asked if they had ever relocated to play hockey. This was to determine

the rationale for the relocation, and if there were any trends among the participants.

Procedure

I approached current and former OHL players, and explained the study along with

its objectives. When participants volunteered for the study, the informed consent forms

were reviewed and signed before data collection began. Each interview was one-on-one,

took an average of approximately two hours to complete, and took place at a private

location convenient to both the participant and me. Confidentiality was maintained by

11

using pseudonyms for each participant. I assisted the participants by helping them

complete the charts during the interview which helped them maintain focus, and ensured

the validity and reliability of their data. These procedures received clearance from the

Research Ethics Board of the University of Windsor.

Reliability

Côté et al. (2005) first proposed their interview guide to reduce the limitations of

athletes’ recall accuracy. The interview procedure describes how to collect verifiable

information, trace longitudinal changes in developmental activities, and assess the

validity and reliability of the information given. Questions focus on the objective recall of

specific episodic experiences (e.g., training hours), which are considered reliable, in part,

because of the large impact that they have on athletes’ lives. Côté et al. (2005) found the

test re-test reliability to be between .70 and .84 for these questions, and the convergent

validity between athletes’ recall and that of others with knowledge of their training

quantity was found to be statistically reliable (e.g., parents, coaches, training partners).

The test-retest reliability was determined to be between .83 and .98 (Barreiros et al.,

2013; Ford, Low, McRobert, & Williams, 2010; Ford & Williams, 2008). Convergent

validity involving similar methods in other studies was between .76 and 1.00 (Berry et

al., 2008; Ford et al., 2010; Fraser-Thomas, Côté, & Deakin, 2008). In this study, I had

prior knowledge of the developmental histories of each participant, which encouraged

them to be more accurate in their recall of specific events, in comparison to a researcher

unfamiliar with the athletes’ past activities (Côté et al., 2005).

12

Data analyses

As the interview systematically explored all the activities that each participant had

undertaken during his development, I compiled all information into charts. I used these

charts to record the number of hours per week and the number of months per year for

each activity, allowing for the calculation of the total number of hours per year. As per

the interview guide, these hours were then separated into ‘sport,’ ‘artistic,’ and ‘other’

(e.g., watching TV) categories. Consistent with the methodology used in Soberlak and

Côté (2003), sporting activities were divided into ‘deliberate practice activities,’

‘deliberate play activities,’ ‘organized games,’ and ‘other sports.’ The hours accumulated

in each category were divided in accordance with the age groups suggested in the DMSP,

sampling (i.e., ages 6 to 13, and specializing (i.e., ages 13-15). Descriptive statistics (i.e.,

mean, median, standard deviation, maximum and minimum hours) were calculated and

graphed to identify trends, and analyzed to determine if transitions were consistent with

those proposed in the DMSP. ‘Deliberate practice activities’ were defined as any

activities that had the purpose of improving the participants’ performances in hockey

(Ericsson et al., 1993). ‘Deliberate play activities’ were hockey-related activities that had

the goal of providing enjoyment to the participants (Côté, 1999). ‘Organized games’ were

any organized hockey competitions, and ‘other sports’ were sports other than hockey that

were identified by participants. The study’s results focused on descriptive statistics

derived from all four charts. Similar analyses were calculated for the other quantitative

sections of the interview charts and follow up questions. These included answers to

questions regarding ages for developmental milestones, amount of effort, concentration,

fun, along with information specific to injuries.

13

I was particularly interested in the sporting activities in which each participant

had been involved. Therefore, sporting activities were categorized using Thorpe, Bunker,

and Almond’s (1986) games classification model, similar to the methodology used in

Berry et al. (2008). The Thorpe et al. (1986) model classifies all games into one of five

categories based on the basic strategies and principles: invasion (e.g., hockey, soccer),

net/wall (e.g., tennis/volleyball), field/run scoring (e.g., baseball), target (e.g., golf), and

individual (e.g., athletics). Hockey qualifies as an invasion sport because the purpose is to

invade the opponent’s territory and score on a goal. Descriptive statistics of the

accumulated hours of structured and unstructured involvement in each of these sport

classifications were calculated, as well as the frequency of the various activities identified

by participants.

According to the scale developed by Jayanthi et al. (2015), all of the participants

in this study were considered highly specialized at the time of the interview. Therefore,

the age at which each athlete first qualified as highly-specialized was determined and

reported with descriptive statistics. The remaining questions about playing other sports,

relocation, and hockey academy attendance were assessed for affirmative answers and the

rationale given for having done so.

Results

Participants

I interviewed 15 participants for this study, all of whom are current or former

OHL players. These athletes had a mean age of 19.6 years (as of September 1st, 2017),

with the oldest athlete being 22 years of age, and the youngest being 16. The mean

number of OHL games played by participants was 145, with a maximum of 294, a

14

minimum of 51, and a median of 139. Since all athletes had commenced playing in the

OHL by 17 years of age, I calculated my results up to and including when each athlete

was 16 years old.

Accumulated activity hours

The number of activity hours that each athlete accumulated per year was

subdivided into ‘sports,’ ‘artistic,’ and ‘other’ categories (Table 1). For each age, the

participants reported accumulating a greater number of total hours in the ‘sport’ category,

and attained greater mean, median, maximum and minimum values than the ‘artistic’ and

‘other’ categories combined (Figure 1). The maximum number of activity hours reported

by participants occurred at the age of 13 for ‘sports’3 (16,338), 9 years old for ‘artistic’

(774), and 13 years of age for ‘other’ (3,508).

The ‘sports’ hours accumulated were further broken down into ‘deliberate

practice,’ ‘deliberate play,’ and ‘organized games’ for hockey activities, as well as for

‘other sports.’ Descriptive statistics for each of these categories are presented in Table 2,

and the total accumulated hours of all participants at each age are graphed in Figure 2.

The maximum number of deliberate practice hours reported by any one participant was

1,452, which occurred at the ages of 12, 13 and 14. Moreover, the combined total hours

of all participants were lowest at 6 years old (2688 hours) and steadily increased until

participants were 16 years of age, when deliberate practice achieved its highest combined

maximum (9,892), and median (568) number of hours (Figure 3). The maximum number

of hours of deliberate play reported by any one participant was 716 hours at the ages of 6,

7, 8, and 9 (Figure 4). The highest combined total of deliberate play hours was achieved

3 ‘Sports’ refers to all sports, including hockey.

15

at 9 years of age (3,160 hours), which decreased sharply thereafter. Despite the decline in

combined accumulated hours of deliberate play, participants had the highest median

(156) hours at 11 years of age. The number of hours that participants engaged in

organized games gradually increased at each age. At 16 years of age, organized games

attained its maximum value reported by any one participant (296 hours), highest

combined total (3,668 hours), and median (256 hours) (Figure 5). Finally, when

considering ‘other sports,’ participants had the highest combined total of hours (5342

hours), median hours (282 hours), and the individual maximum hours at 12 years old

(1300), at which point the commitment to ‘other sports’ decreased steadily (Figure 6).

To illustrate the proportion of time participants spent in each of the four sport

categories, Figure 7 shows the percentage of time that participants were engaged in each

activity for each year during the developmental period. The proportion of time that

participants allocated to deliberate practice increased steadily from a low of 30.75% at 9

years of age to a high of 61.31% at 16 years old. In contrast, the proportion of time that

participants allocated to deliberate play decreased each year from the high of 34.06% at 6

years old to 4.71% at 16. The proportion of time that participants allocated to organized

games shows small increases from 6 (10.48%) to 12 (13.93%) years of age, and then

steadily increases from ages 13 (14.85%) to 16 (22.73%). At 12 years old, participants

allocated the greatest proportion of time to other sports (34.08%), but the time committed

to these other activities decreased sharply to its lowest value (11.24%) at 16 years old.

Developmental Milestones

In the second section of Côté et al.’s (2005) interview script, athletes are asked

questions related to 31 developmental milestones throughout their hockey careers.

16

Participants indicated that they were, on average, approximately 3 years old when they

first started playing hockey, and 4 years old when they became regularly involved in the

sport (Table 3). At an average of approximately 5 years of age, participants became

involved in competitive hockey, and were recognized among the best five players at that

level. By 9 years old, most participants were playing competitive hockey, and most were

recognized as being among the best five players by 10 years of age (Table 4). Seven of 15

participants had competed at the national level, with only five taking part in international

competition. The idea of becoming an elite athlete emerged between 3 and 14 years of

age, while the decision to become an elite athlete was made between 7 and 16 years of

age. Most participants first engaged in regular team training by 8 years old, and started

non-sport specific training by 12 years old. By the age of 13, most participants had

completed their first off-season training camp, and by 14 years old, most acknowledged

that they spent all of their available leisure time training for hockey. When looking

toward their athletic futures, participants thought they would reach their maximum

athletic potential, on average, at approximately 23 years of age, and they thought they

would retire from competitive hockey at an average age of 33 years old.

Qualitative information was also obtained from the interviews regarding the

participants’ developmental histories. This information was compiled and presented for

each year of the survey. Of note, participants engaged in various levels of hockey at

younger ages, but all athletes were playing at the ‘AAA’ level by the age of 12 (Table 5).

While five participants in the sample played against older players at the ages of 6 and 7,

only two were doing so at 8 and 9 years of age, and only one remained in an older age

group thereafter (see Table 6). Only one injury was reported before the age of 12, but a

17

total of 22 injuries were reported between the ages of 12 and 16. Three of these injuries

were considered chronic, or overuse, while the other 19 were acute in nature (Table 7).

Fourteen years old was the age at which a participant first reported an injury affecting his

current performance, despite being able to participate with the condition. Additionally,

this portion of the interview collected subjective information regarding the perceived

intensity of training, and the available resources at each age (Table 8). Both steadily

increased throughout participants’ development from the lowest values at the age of 7,

until attaining maximum values at 16 years old. Finally, this section of the interview

asked participants to calculate the number of hours spent on hockey activities at different

ages. At each age, players under-stated their involvement in hockey activities, compared

to the following section when they analyzed each age more thoroughly.

Effort, Concentration, and Fun

In addition to calculating the number of hours in each of the hockey activity

categories, I asked participants to subjectively rate the ‘physical effort,’ ‘mental

concentration,’ and ‘fun’ for each hockey activity that they identified throughout their

development. Table 9 outlines the descriptive statistics of the responses for organized

games. The median value of the participants’ responses regarding physical effort

increased steadily from 6 years old (82.5%) to 16 years old (100%), with the exception of

when the participants were 10 years old, which decreased slightly (81.67%) from when

they were 9 years old (85%). The reported ‘mental concentration’ of participants

followed a similar trend of increasing median values from ages 6 (85%) through 16

(100%). ‘Organized games’ were seen as enjoyable by the participants from 6 to 15 years

18

of age, with a median ‘fun’ rating of 100% during this timeframe. However, the median

(80%) decreased considerably once participants turned 16 years of age.

Similarly, participants rated ‘practices’ (Table 10) with increasingly higher levels

of ‘physical effort’ and ‘mental concentration’ from ages 6 until 16, with both having

maximum (100%) median ratings at 16 years of age. Much like organized games, the

median of reported ratings regarding ‘fun’ for practices was 100% from 6 years old until

the median decreased substantially to 75% at 16 years of age. Finally, participants rated

self-initiated activities (Table 11) as relatively constant throughout their development,

with the ‘physical effort’ median ratings reported as being from 80% to 90%. The

‘mental concentration’ reported by participants had a median rating of 70% until the age

of 10, when it increased steadily to a median of 95% at 16 years old. Lastly, self-initiated

‘fun’ ratings were highest early in participants’ developmental histories, with a median of

100% at 6 years old. The reported ‘fun’ ratings then decreased to a median of 85% at 15

years old, and remained the same at 16.

Other Sports

Information regarding the other sports that the participants engaged in while

growing up was compiled and categorized by sport type. At 10 years old, all participants

involved in this study played at least one invasion sport other than hockey (Table 12), and

at 12 years old, 14 of the participants amassed the greatest combined total hours (1,942),

mean hours (129.47), and median hours (108) in invasion sports. At 15 years old,

participation in invasion sports dropped substantially, with only six athletes accumulating

any hours whatsoever. Eleven participants engaged in run scoring, or field, sports (Table

13) at the ages of 10 and 11, but the greatest combined total of hours from all participants

19

came at 12 years old (800 hours). By 14 years of age, only a minority of participants were

involved in run scoring sports, and there were only two athletes participating in these

sports by the age of 16. Net sports (Table 14) had the highest combined total hours

(1,110), median hours (528), and number of participants at age 13, but by age 15 most

participants did not report any involvement. Involvement in target sports followed a

different trend as the highest combined total hours (984), median (32), and maximum

number of participants (11) occurred at the age of 16 (Table 15). The highest number of

participants in individual sports (Table 16) was 10 occurring from the ages of 10 to 13,

and only six participants reported any involvement by the age of 14. Additionally, at 12

years old, participants accumulated the highest combined number of hours in individual

sports (528). Invasion sports accrued the most hours at each age until 15 years, when

target sports became the most prominent, according to the total number of hours

accumulated (see Figure 8). The gain in the relative popularity of net sports is second

only to invasion sports at age 13, and were the second most popular sports at the ages of

15 and 16. When considering specific sports (Table 17), soccer was played by all the

athletes that were interviewed at some point during their development. From the ages of

six through 12, at least 10 athletes participated in soccer; however, only one participant

was involved in soccer at the age of 15. Baseball, basketball, golf, and volleyball shared

the second most popular spot, with 12 participants taking part at some point during their

development. Golf gained in popularity from five participants at 6 years old and reaching

its maximum of 12 participants at 15 years old. At 12 years old, five sports (i.e., soccer,

baseball, basketball, golf, and volleyball) had 10 or more participants, while at 14 years

20

of age, only golf had more than seven participants. By the age of 15, no sport other than

golf had more than four participants.

Supplemental Questions

To supplement the Côté et al. (2005) interview, several questions were asked to

identify other aspects of sport specialization. Three of these questions were proposed by

Jayanthi et al. (2015), which helped assign a degree of specialization to participants based

on their responses. Participants in this study stated that they chose ‘hockey as their main

sport’ by approximately 12 years old, on average, with a median of 13 years old. The

mean for ‘quitting other activities to play hockey’ was 12.47, with a median of 12 years

old. Finally, these participants responded that they ‘trained for hockey for more than

eight months a year’ at 12.40 years of age, on average, with a median of 13 years old.

Due to participants reaching each of the three milestones at different ages, the responses

from individual participants were analyzed to determine the combined degree of

specialization. The reported answers indicate that the participants in this study are highly

specialized in hockey at a mean of 13.33, and a median of 14 years of age. However,

upon calculating the duration of annual training via answers to the ‘hockey activity’

portion of the interview, it appears that participants underestimated their annual

commitment to hockey. Based on these calculations, the mean age at which training

occurred for more than eight months in a given year was 7.8 years, with a median of 7

years. These calculations indicate that the mean age at which the participants became

highly specialized in hockey may be slightly younger at 12.87 years of age, although the

median age remains at 14 years (Table 18).

21

Finally, four questions regarding issues of specialization were asked of each

participant. Eight participants were instructed to not participate in other sports during the

hockey season at some point during their development (Table 19). In five of these cases,

coaches made the requests independently, while both coaches and parents made the

requests in the remaining cases. In each instance, participants were prevented from

participating in other sports to prevent potential injuries. Only two participants were

instructed not to play other sports during the off-season, and both the parents and coaches

made these requests, again, to prevent injuries from occurring. Only one participant had

ever attended a hockey academy, and he attended this academy for a total of one year

(Table 20). Of the 15 athletes interviewed for the study, 11 had played for more than one

minor hockey association, with nine participants having relocated to play against higher

levels of competition; one to avoid conflict with a coach, and one was cut from his

previous team.

Discussion

The scope of this study focuses on the developmental activities of OHL players.

Thus, the results concentrate on the years prior to participants’ involvement in this

league, which may commence as young as 16 years old.4 The median of 139 OHL games

played shows that the majority of participants have played more than two complete

seasons in the league at the time of their interviews. These participants create a

homogeneous sample of skilled hockey players by having maintained OHL status,

4 Players can begin competing in the OHL at 16 years old with the rare instance of

‘exceptional status’ being granted to players who are 15 years old (RSEN, 2013).

22

thereby showing a high level of proficiency, which is a common determinant of expert

status (Coutinho, Mesquita, & Fonseca, 2016).

Hockey-Specific Deliberate Play

Collectively, participants of the current study spent approximately 22% of their

total sports hours in the sampling stage engaged in deliberate play activities (see

Appendix C for an expanded discussion of deliberate play). In contrast, Soberlak (2001)5

indicated that athletes in his study reported 55% of their total sport hours in the sampling

stage were allocated to deliberate play. Similarly, during the specializing stage

participants of the current study spent only 8% of their hours in deliberate play, while the

Soberlak (2001) athletes reported spending 30% of their hours in similar activities.

Furthermore, most participants of the Soberlak and Côtê (2003) study did not show a

substantial decrease in deliberate play hours until 14 years of age. In the current study,

participants showed a large decrease in the combined total of deliberate play hours

between the ages of 9 and 10, as well as between 14 and 15. Using the same criteria as

Soberlak and Côté (2003), participants of the current study may have been considered

specialized as early as 10 years old, and invested by 15 years of age. These ages are

younger than those suggested by both Hockey Canada (2013) and the DMSP (Côté,

1999) (for a detailed comparison to previous developmental models see Appendix D).

5 Soberlak and Côté (2003) refers to the published work that was derived from the more

detailed Soberlak (2001) Master’s thesis. Both are referred to in the current study,

depending on where the information was provided.

23

Barreiro and Howard (2017) indicated that when parents were surveyed, a

significant number admitted that they view unstructured play as a waste of time.

However, these same parents viewed structured play (e.g., competitive sports) as an

important aspect of childhood. Due to parents making the majority of decisions regarding

free-time activities, the replacement of unstructured play by structured practice and

competition may explain the reduction in sport-specific play activity hours (Barreiro &

Howard, 2017; Coakley, 2010). Furthermore, research has shown that parents from a

lower socio-economic status may be more likely to submit their children to early

specialization due to the appeal of extrinsic rewards (Ballard, 2017; NPR, 2015). Twenty

six percent of parents with children in high school sports admit that they hope that their

child will play professionally, and the percentage increases to 39% in low-income

families (Ballard, 2017). Likewise, NPR (2015) found that 9% of college educated

parents hoped that their children would play a sport professionally, whereas, 44% of

parents with less than a high school diploma hoped for the same outcome.

Hockey-Specific Deliberate Practice

While the difference between practice and play may not always be clear (Côté,

Erickson, & Abernethy, 2013), deliberate practice in the current study included any

activity (e.g., self-initiated practice, team practice, individual coaching) that was done to

improve performance (Côté et al., 2005). The increase in accumulated hours of deliberate

practice reported from 9 to 10 years of age may be explained by the increase in the

number of players competing at the AAA level (see Appendix E for a detailed evaluation

of ‘developmental milestones’ and ratings of ‘effort, fun, and concentration’). The

difference in accumulated hours of deliberate practice between 12 and 13 years old may

24

be explained by the advancement of participants from the ‘peewee’ to ‘bantam’ age

categories. Similarly, between the ages of 15 and 16, participants were transitioning to

the OHL, where time commitments are much greater. Corresponding with this increased

investment was the reduction of fun reported by participants, showing that by the age of

16 hockey was not as enjoyable as previous years.

In the sampling phase, only 10% of the hours collected by the junior hockey

players in the Soberlak and Côté (2003) study were dedicated to deliberate practice. In

the sampling stage of the current study, 35% of all sports hours were spent on deliberate

practice. Deliberate practice during the specializing stage accounted for 18% of the

Soberlak and Côté (2003) participants’ sport hours, and 50% of the hours reported by the

participants of this study (see Appendix F for an expanded discussion of deliberate

practice). When calculating the total of all the individual activities, there were more than

double the number of hours reported, compared to when asked to summarize the time

spent participating in hockey, showing that participants underestimate their commitment

to hockey. One explanation for the early accumulation of deliberate practice hours in

youth sports is that some coaches pressure young athletes into training at higher levels

than are needed due to an overemphasis on winning (Kutz & Secrest, 2009). Valuing

winning at the expense of player development creates an adult-centric environment that

rationalizes specialization (Vealey & Chase, 2016).

Organized Hockey Games

The hours that participants in the current study spent competing in organized

hockey games increased throughout their developmental years in similar proportions to

that found by Soberlak and Côté (2003). This finding demonstrates that the proportion of

25

time spent competing in organized games has not changed substantially for athletes of

this level since Hockey Canada implemented changes to its grassroots programs after the

Open Ice Summit. However, the majority of participants in the current study started

playing hockey by the time they were 3 years old, began regular involvement by 4 years

old, and were competing in an organized league when they were 5 years of age. This is

considerably younger than the AAA players in the Wall and Côté (2007) study where, on

average, players started playing after they were 5 years old, and initiated competition

when they were 6 years of age. The younger ages found in the current study provides

evidence that Canadian hockey players are starting to specialize at younger ages than has

been documented in previous research.

Other Sports

The total number of accumulated hours that participants played sports other than

hockey increased until the age of 12, and decreased every year thereafter. The number of

sports played other than hockey increased from an average of 2.0 at the age of 6, to a

maximum average of 5.6 at 12 years old, and then decreased each year to 2.3 at 15 years

old (see Appendix G for an expanded discussion of other sports). Comparatively,

Soberlak (2001) found that the junior hockey players in his study did not decrease the

amount of time spent playing other sports until the age of 14. The athletes in the Soberlak

(2001) study played an average of three sports other than hockey between 6 and 8 years

of age, and an average of six sports between 9 and 12 years. Therefore, the average

number of accumulated hours engaged in other sports is higher in the current study, but

the average number of sports played is similar to Soberlak and Côté (2003). The

participants of the current study begin to eliminate sports other than hockey at an age

26

which is consistent with Hockey Canada’s (2013) LTAD program, and the DMSP’s

(Côté, 1999) recommendations regarding participating in other sports.

Participants of the current study began to spend less time playing other sports

when they were 12 years old, whereas in the Soberlak and Côté (2003) study the decline

began at 14 years old. Differences existed proportionately as well, with participants of the

current study spending a higher proportion of their total sport hours playing other sports

during the sampling stage compared the specializing stage. Soberlak (2001) reported that

the athletes in his study spent a lower proportion of the sampling stage participating in

sports other than hockey relative to the specializing stage. Thus, it appears that although

the participants in the current study may have spent more time playing sports other than

hockey early in their development, they ceased playing these other sports sooner than

previous research on junior hockey players has indicated.

The 15 participants in the current study indicated that they competed in a

combined 16 sports, other than hockey, from all five sport categories, displaying the

diverse developmental pathways noted by other authors (Huxley, O’Connor, & Larkin,

2017). While the extent to which skills may transfer occurs across sports is not

universally accepted, there is evidence to suggest that similar sports have elements of

positive skill or tactical transfer (e.g., Chow, Davids, Button, & Renshaw, 2016; Gorman,

2015; Santos, Mateus, Sampaio, & Leite, 2017; Vealey & Chase, 2016). Consistent with

previous research, the number of hours that participants of the current study engaged in

invasion sports is greater than the hours accumulated in other categories (Berry et al.,

2008). The similarity of tactics employed in invasion sports may increase the likelihood

for transfer because athletes can ‘chunk’ information, allowing them to broaden their

27

focus and process more stimuli (Chow et al., 2016; Santos et al., 2017; Tsal & Lavie,

1993). Therefore, participants may have enhanced hockey performance due to

participation in these sports by a mechanism known as lateral, or near, transfer (Rosalie

& Müller, 2012), where the training outcomes in one sport can be used in other sports

(Issurin, 2013).

Supplemental questions

In the current study, most participants considered hockey their main sport by the

age of 13, quit other activities for hockey by the age of 12, and reported training more

than eight months per year for hockey at 13 years old. Due to athletes reaching each of

these milestones at different ages, the degree of specialization was calculated

individually, and then analyzed. Accordingly, most of the participants in the current study

were considered moderately specialized at 13 years old, and highly specialized at 14

years of age. Using the detailed information derived from the Côté et al. (2005) interview

script, the age at which participants in the current study trained for eight or more months

per year could be empirically calculated. Based on these calculations, most participants in

the current study had trained for eight or more months per year by 7 years of age, as

opposed to their estimation of 13 years old. Moreover, the calculated age at which

participants trained for more than eight months per year indicates that they were

moderately specialized at 12 years of age, which is one year earlier than the 13-year-old

average that participants reported in their responses to the supplemental questions. In

reference to the age divisions for minor hockey in Canada, this one-year disparity is the

difference between a player competing at the ‘bantam’ or ‘peewee’ level (OMHA, 2017).

The calculated median age of participants being categorized as highly specialized was 14

28

years, which is consistent with the average reported in their answers to the supplemental

questions. However, most of the participants had answered that they had the idea to

become an elite athlete by 8 years old, and decided to become one by 12 years of age.

These qualitative indications suggest that participants may have been highly specialized

prior to 14 years of age.

Research has shown that being classified as highly specialized is an independent

risk factor for reporting an injury, regardless of training volume and age (Bell et al.,

2016; Jayanthi et al., 2015; Jayanthi & Gugas, 2017). Jayanthi et al. (2015) also stated

that athletes who exceed a 2 to 1 ratio of organized training (i.e., deliberate practice and

organized games) to free play (i.e., deliberate play) were more likely to develop an injury.

Accordingly, this ratio was achieved by most of the participants of the current study by

12 years of age, which corresponds to when the first participant reported playing through

an injury. Empirically calculating the age at which these participants became highly

specialized may provide insight into the injuries that they reported starting this year.

It has been reported that some youth coaches prohibit athletes from participating

in sports other than their primary sport (Coakley, 2010). In all eight instances of

participants in this study being instructed to not play other sports, the rationale was to

prevent injuries that may inhibit their performance in hockey (see Appendix H for an

expanded discussion of the requirement to specialize). However, the recommendation

may be ill-advised as specialization has been closely associated with overuse injuries, and

athletes may suffer acute injuries at any time in their main sport as well (Axelrod, 2016;

Jayanthi et al., 2015; Post, Thein-Nissenbaum, Stiffler, Brooks, & Bell, 2017; United

Press International, 2002). Youth sport organizations also place pressure on parents and

29

coaches to promote early specialization by placing value in the result of competition, and

devaluing the developmental process (Vealey & Chase, 2016). Pressure is placed on

coaches to win by these organizations by advertising national rankings as young as 4

years old (Gregory, 2017; UFSPRC, 2013). Likewise, parents feel pressured to push

specialization as third-party entities attempt to forecast their children’s professional

outlook as early as 7 years old (Gregory, 2017).

Only one of the participants in the current study attended a hockey academy, and

only for a single year. This participant attended an academy at the age of 16, prior to

attaining his position on an OHL team. Despite the proliferation of hockey academies

throughout Canada, and specifically Ontario (Traikos, 2015), they did not appear to have

a major influence on the development of the OHL players in the current study. While

hockey academies are becoming increasingly popular among minor hockey players, the

four academies located within the region selected for the inclusion criteria did not begin

operating until approximately 2014 (Hockey Canada Skills Academy, n.d.; Thorne, 2014;

Traikos, 2015). However, all participants in the current study reported using private

instructors, such as skating and goalie coaches, as an element of their deliberate practice.

Participants chose instructors for these sessions based on the specific skill(s) they were

trying to enhance. Most participants in the current study began utilizing the services of

individual coaches by 10 years old, while four participants started as early as 6 years of

age. This means that participants’ parents or guardians were responsible for paying these

coaches for up to 10 years of individual coaching in an attempt to excel above their peers.

30

Conclusion

Wayne Gretzky has said that requiring children to play summer hockey does them

no benefit, and prevents them from acquiring positive development from other sports

(Cole, 2015). In his National Post article entitled ‘An illness, possibly untreatable:

Hockey in Canada has become terrifyingly expensive and dangerously elitist,’ Cole

(2015) stated that Hockey Canada perpetuates an elitist system by supporting programs to

get kids involved at a young age, while promoting specialized, adult-driven training

throughout their development. However, Hockey Canada Skills Academies do appear to

be financially viable, with local academies charging less than $500 for over 100 hours of

training, as opposed to the $50,000 private schools that Cole (2015) discussed (Waddell,

2015). Families being responsible for the cost of player development eliminates many

children in Canada, and contrasts the successful German soccer talent identification

system. The ‘Bundesliga’ required a $100 million investment from its teams to create

academies, reducing the cost to families, and setting a ‘wide net’ to capture as many

talented individuals as possible (Vealey & Chase, 2016).

As hypothesized, the participants’ athletic backgrounds featured a diverse

sampling stage. While participants of this study continued to play other sports until 12

years of age, the amount of structured practice reported was higher than unstructured free

play at every age. Furthermore, when compared to Soberlak and Côté (2003), the

participants of this study showed an increased commitment to hockey at a younger age.

The dedication to practice showed that elements of specialization were evident at 10

years old when considering the characteristics of this stage of the DMSP. At 14 years old,

most participants had abandoned playing other sports (with the exception of golf),

31

decided to become elite athletes, and spent virtually all of their available time training for

hockey, thereby showing that they had invested in hockey. Finally, according to Jayanthi

et al.’s (2015) definition of specialization, most participants reported being highly-

specialized by the age of 14. However, further evaluation showed that this milestone may

have occurred as early as 12 years old, which is contrary to Hockey Canada’s (2013)

LTAD suggestion of merely ‘increasing’ hockey-specific activity at this age.

A proposed motive for coaches encouraging specialization is less athlete-driven,

and more entrepreneurially based, by pushing their own agendas on athletes and their

families (Coakley, 2010). Popular news sources have done much to expose this side of

youth sport, reporting it as a $15 billion business in the United States (Good Morning

America, 2017; Gregory, 2017). Accordingly, minor hockey coaches are one of the many

beneficiaries of the business of youth sport, rumoured to be paid as much as $70,000, and

have significant influence to cut players who do not fully commit to the team because of

playing other sports (Coakley, 2010; Harwood & Knight, 2014; Kutz & Secrest, 2009;

Rutherford, 2008; Vealey & Chase, 2016). Likewise, these coaches benefit from the cost

associated with years of individual coaching sessions. In the current study, athletes and

families were responsible for the expense of these sessions for up to 10 years.

The retrospective recall methodology used in this study provided invaluable

insight into the developmental histories of these participants. However, most participants

initiated hockey activities by 3 years of age, and started competition at 5 years of age.

The interview script prescribed that information be collected beginning at the age of 6

years; therefore, for future research focused on the developmental histories of elite

Canadian hockey players, it is advisable to account for the hours collected prior to 6 years

32

of age to give a more accurate portrayal of athletes’ sport involvement. Additionally, the

retrospective answers that participants gave to the questions in Jayanthi et al.’s (2015)

three-point scale did not always substantiate the answers to similar questions in the Côté

et al. (2005) script. While the scale has demonstrated to be a valid tool in the study of

specialization, it may not provide reliable information when used retrospectively.

The results of this study show that the developmental pathways of elite hockey

players may have changed since Hockey Canada changed its grassroots programs.

Despite the increase of structured activities, participants consistently reported having

enjoyed all of their hockey activities. The fun that they experienced at a young age

presumably allowed them to commit to the high levels of physical effort and

concentration needed to develop into elite hockey players. Parents of children who aspire

to play hockey at a high level should acknowledge that these participants consistently

played sports other than hockey, and enjoyed their time spent in hockey activities.

Therefore, finding ways to promote fun at a young age, regardless of the type of activity,

appears to be instrumental in the development of elite hockey players.

The current model of youth sports has caused adults to rationalize chasing results

over the development of the children involved (Brunt, 2017; Vealey & Chase, 2016).

Year-round commercial programs have created primary income sources for adult coaches

and entrepreneurs, all of whom ‘sell specialization’ to justify their businesses (Coakley,

2010). To explain the conundrum that parents and athletes face, Ballard (2017)

analogized the rationalization with poaching ivory, by stating that the elusive trophy of

professional success comes at expense of positive psychosocial development, much like

owning a small piece of ivory comes at the great expense of losing an elephant forever.

33

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Table 1 Descriptive statistics of accumulated activity hours by age

Age

Category 6 7 8 9 10 11 12 13 14 15 16

All Sports Total 7290 8872 11070 11734 12772 13896 15362 16338 15744 14246 14918 Mean 486.00 591.47 738.00 782.27 851.47 926.40 1024.13 1089.20 1049.60 949.73 994.53 S.D. 433.96 387.87 459.80 497.77 548.09 560.07 572.19 525.61 468.55 338.97 296.18 Median 458 492 548 552 576 652 896 936 856 912 896 Max 1592 1592 1620 1756 2156 2156 2284 2200 2084 1454 1838 Min 60 144 264 304 352 312 312 448 372 420 556

Artistic Total 12 144 678 774 646 630 666 542 606 240 100 Mean 0.80 9.60 45.20 51.60 43.07 42.00 44.40 36.13 40.40 16.00 6.67 S.D. 3.10 37.18 129.85 131.30 130.07 130.37 129.87 83.71 103.86 33.97 25.82 Median 0 0 0 0 0 0 0 0 0 0 0 Max 12 144 504 504 504 504 504 320 400 100 100 Min 0 0 0 0 0 0 0 0 0 0 0

Other Total 1984 2284 2380 2236 3484 3356 3356 3508 3024 2744 3064 Mean 132.27 152.27 158.67 149.07 232.27 223.73 223.73 233.87 201.60 182.93 204.27 S.D. 239.43 233.36 230.50 226.26 410.70 407.51 407.51 402.53 225.85 217.14 245.65 Median 0 0 64 64 128 84 84 96 160 80 144 Max 800 800 800 800 1600 1600 1600 1600 800 800 800 Min 0 0 0 0 0 0 0 0 0 0 0

60

Table 2 Descriptive statistics for sub-divided ‘sport’ categories

Age Category 6 7 8 9 10 11 12 13 14 15 16 Hockey-specific Deliberate Practice

Total 2688 3064 3548 3652 5028 5508 6108 7512 8002 8434 9892 Mean 179.20 204.27 236.53 243.47 335.20 367.20 407.20 500.80 533.47 562.27 659.47 S.D. 202.89 183.92 185.95 185.69 359.71 345.81 345.37 329.37 331.02 269.93 284.76 Median 120 120 168 172 172 252 284 444 444 516 568 Max 744 744 744 744 1452 1452 1452 1452 1452 1088 1344 Min 0 24 64 64 64 88 88 124 124 192 256

Hockey-specific Deliberate Play Total 2516 3044 3156 3160 2424 2428 2040 1712 1600 972 760 Mean 167.73 202.93 210.40 210.67 161.60 161.87 136.00 114.13 106.67 64.80 50.67 S.D. 230.58 218.83 219.91 219.69 141.10 137.35 124.83 118.69 120.00 68.84 86.01 Median 80 136 124 124 124 156 96 72 72 48 8 Max 716 716 716 716 476 476 476 476 476 224 252 Min 0 8 8 8 0 0 0 0 0 0 0

Organized Hockey Games Total 774 1030 1262 1358 1572 1800 2184 2486 2666 2974 3668 Mean 51.60 68.67 84.13 90.53 104.80 120.00 145.60 165.73 177.73 198.27 244.53 S.D. 41.43 30.42 57.03 53.65 54.30 54.53 71.58 63.62 63.86 69.95 36.06 Median 48 72 72 72 96 96 144 160 172 198 256 Max 144 144 264 264 264 264 288 288 288 296 296 Min 0 24 24 48 48 48 48 72 72 72 168

Sports other than Hockey Total 1408 1910 3248 3708 4084 4472 5342 5028 4028 2470 1814 Mean 93.87 127.33 216.53 247.20 272.27 298.13 356.13 335.20 268.53 164.67 120.93 S.D. 77.77 99.17 226.83 256.90 249.90 260.72 286.93 266.27 211.41 120.64 85.43 Median 68 108 160 190 196 232 282 276 196 212 128 Max 296 376 948 1108 1108 1172 1300 1080 812 332 300 Min 0 0 0 36 72 64 64 60 12 0 0

61

Table 3 Descriptive statistics for the first emergence of developmental milestones (in years)

Statistic

Milestone Question Mean S.D. Median Mode Max Min

Regular involvement in hockey 3.87 0.74 4 4 5 3

Involvement in supervised training 5.47 1.81 5 4/5 10 3

Involvement in unsupervised training 8.13 3.23 8 4/7 14 4

Started playing hockey 3.40 0.74 3 3 5 2

Played in an organized league 4.80 0.94 5 4 7 4

Sport specific training regularly 12.33 2.35 13 14 15 7

Non-sport specific training regularly 12.07 2.02 12 12 15 8

Idea for becoming an elite athlete 9.53 4.21 8 14 14 3

Engages in the regular training of a team 8.47 3.76 8 4/12 14 4

Decision was made to become an elite athlete 11.73 2.69 12 14 15 6

All available leisure time was spent on training 12.93 2.74 14 14 16 7

Off-season training camp 12.87 2.13 13 14 16 9

Relocated for hockey 16.07 0.83 16 16/17 17 15

Established an extended relationship with a coach 13.14 2.48 12 12 18 9

You will reach your maximum potential 23.4 3.29 24 20/28 28 19

You will retire from competitive hockey 33.2 4.74 35 35 38 19

62

Table 4 Descriptive statistics for the first emergence of developmental milestones (in years) Statistic

Competition level n Mean S.D. Median Mode Max Min

Competition at the recreational level

Age for first participation 15 5.00 0.93 5 5 7 4

Recognized as a top 5 player 14 5.43 1.28 5 4/5 8 4

Recognized as the best player 14 6.50 2.14 6 6 12 4

Competition at the competitive level

Age for first participation 15 9.00 2.33 9 7 13 6

Recognized as a top 5 player 15 9.87 2.70 10 12 14 6

Recognized as the best player 15 11.20 3.28 12 12/15 15 6

Competition at the all-star level

Age for first participation 13 13.15 2.12 14 14 15 8

Recognized as a top 5 player 9 13.78 1.39 15 14 15 11

Recognized as the best player 3 13.67 1.53 14 12/14/15 15 12

Competition at the national level

Age for first participation 7 15.57 0.79 16 16 16 14

Recognized as a top 5 player 5 15.40 0.89 16 16 16 14

Recognized as the best player 3 15.67 0.58 16 16 16 15

Competition at the international level

Age for first participation 5 15.40 0.89 16 16 16 14

Recognized as a top 5 player 3 15.67 0.58 16 16 16 15

Recognized as the best player 2 15.50 0.71 15.50 0.94 16 15

63

Table 5 Number of participants at each level of hockey for each age

Number of Participants at Each Age

Level 6 7 8 9 10 11 12 13 14 15 16

House 4 3 1 0 0 0 0 0 0 0 0

Novice 1 2 1 0 0 0 0 0 0 0 0

Selects 2 2 0 0 0 0 0 0 0 0 0

Travel 1 2 2 2 1 0 0 0 0 0 0

Rep 1 1 1 1 1 1 0 0 0 0 0

A 1 3 3 3 3 0 0 0 0 0 0

AA 1 1 3 4 1 0 0 0 0 0 0

AAA 1 1 4 5 9 14 15 15 15 10 0

Academy 0 0 0 0 0 0 0 0 0 0 1

Jr. C 0 0 0 0 0 0 0 0 0 0 1

Jr. B 0 0 0 0 0 0 0 0 0 1 4

O.H.L. 0 0 0 0 0 0 0 0 0 4 9 Note: Jr. B = Junior B. Jr. C = Junior C. O.H.L. = Ontario Hockey League

64

Table 6 Participants’ ages relative to that of other players in their respective leagues

Number of Participants at Each Age

Age of other players 6 7 8 9 10 11 12 13 14 15 16

2 years older 1 1 0 0 0 0 0 0 0 0 0

1 year older 5 5 2 2 0 0 1 1 1 1 1

Same age 6 9 13 13 15 15 14 14 14 14 14

65

Table 7 Qualitative information regarding injuries for hockey activities for each age Age

Injury question 6 7 8 9 10 11 12 13 14 15 16

Injuries reported 0 0 0 1 1 0 4 4 3 3 8 Acute mechanism NA NA NA 1 1 NA 3 2 3 3 8

Overuse mechanism NA NA NA 0 0 NA 1 2 0 0 0

Combined time away NA NA NA 30

days 30

days NA 154 days 210 days 111 days 111 days 328 days

Combined time affected NA NA NA 30

days 30

days NA 288 days 285 days 5 years

5 years

5 years

66

Table 8 Descriptive statistics for intensity, resources, hockey activity hours reported, and hockey activities calculated

Age

Category 6 7 8 9 10 11 12 13 14 15 16

Intensity (subjective ratings on a scale of 0-100) Mean 53.33 51.33 53.67 54.67 61.33 67.00 70.67 76.00 78.33 82.67 96.33 S.D. 25.35 26.69 27.42 27.80 23.34 16.78 16.99 13.12 14.72 16.78 6.67 Median 50 50 60 60 60 65 65 75 80 85 100 Max 100 100 100 100 100 100 100 100 100 100 100 Min 10 10 10 10 20 40 50 60 60 60 80

Resources (subjective ratings on a scale of 0-100) Mean 59.17 57.33 58.67 61.33 65.33 71.33 74.33 78.33 78.67 85.33 93.00 S.D. 26.78 26.04 26.69 24.46 22.00 17.37 18.11 16.55 16.53 14.20 9.96 Median 55 50 60 60 60 70 70 80 80 85 100 Max 100 100 100 100 100 100 100 100 100 100 100 Min 10 10 10 30 30 50 50 50 50 50 70

Hours reported Mean 157.67 154.13 185.60 196.80 258.40 288.27 327.20 388.00 405.87 616.27 832.00 S.D. 90.23 77.90 115.63 112.65 175.60 164.98 190.10 170.47 198.03 443.92 365.97 Median 126 120 140 140 192 240 256 336 336 476 800 Max 336 336 480 480 660 660 768 768 880 1920 1920 Min 48 72 96 96 96 88 88 144 192 192 336

Hours calculated Mean 614.00 711.33 772.93 786.53 913.87 951.20 990.93 1077.20 1065.33 1062.13 1183.20 S.D. 521.28 499.27 491.50 484.02 697.86 706.38 684.20 712.35 616.49 514.10 496.65 Median 504 504 612 612 660 660 756 824 824 868 980 Max 1792 1792 1816 1816 2544 2544 2544 2544 2356 2208 2716 Min 0 184 192 312 312 312 344 344 416 416 736

67

Table 9 Subjective ratings of effort, concentration, and fun for organized games (subjective ratings on a scale of 0-100)

Age

Category 6 7 8 9 10 11 12 13 14 15 16

Physical Effort

Mean 80.00 81.33 82.00 83.67 81.67 85.53 88.87 91.53 91.67 94.00 98.00

S.D. 22.26 22.56 19.62 20.22 19.97 18.69 14.07 11.81 11.90 11.83 5.61

Median 82.5 85 85 90 85 98 98 98 100 100 100

Max 100 100 100 100 100 100 100 100 100 100 100

Min 40 40 40 40 40 40 60 60 60 60 80

Mental Concentration

Mean 71.67 76.00 76.67 78.00 78.00 81.33 84.33 88.33 89.67 93.33 97.33

S.D. 24.80 22.61 18.39 19.35 20.07 16.85 14.00 11.90 12.02 11.75 7.99

Median 85 80 80 80 80 80 80 90 90 100 100

Max 100 100 100 100 100 100 100 100 100 100 100

Min 40 40 50 50 50 50 60 60 60 60 70

Fun

Mean 97.50 97.00 97.67 97.67 94.33 98.00 97.00 95.67 94.33 90.00 81.67

S.D. 5.00 7.97 7.76 7.76 14.50 7.75 8.41 8.63 9.42 14.14 18.29

Median 100 100 100 100 100 100 100 100 100 100 80

Max 100 100 100 100 100 100 100 100 100 100 100

Min 85 70 70 70 50 70 70 70 70 60 50

68

Table 10 Subjective ratings of effort, concentration, and fun for practices (subjective ratings on a scale of 0-100)

Age

Category 6 7 8 9 10 11 12 13 14 15 16

Physical Effort

Mean 78.33 78.67 75.67 77.33 79.67 80.33 84.33 87.67 88.33 91.67 96.67

S.D. 21.78 22.40 21.78 22.19 22.40 21.25 17.82 15.68 16.00 16.00 10.47

Median 77.5 80 80 80 80 80 90 90 90 100 100

Max 100 100 100 100 100 100 100 100 100 100 100

Min 40 40 40 40 40 40 40 40 40 40 60

Mental Concentration

Mean 61.25 63.33 62.33 64.00 71.33 72.67 80.00 83.33 84.00 88.33 93.00

S.D. 29.70 30.10 29.57 30.89 28.50 28.65 18.90 17.59 17.24 18.09 13.34

Median 65 70 60 60 80 80 80 80 80 90 100

Max 100 100 100 100 100 100 100 100 100 100 100

Min 0 0 0 0 0 0 30 30 30 30 50

Fun

Mean 97.50 93.00 92.33 92.33 95.00 96.00 95.33 92.33 91.00 88.67 77.33

S.D. 5.84 16.01 15.91 15.91 13.23 12.98 13.02 13.48 13.65 15.17 19.35

Median 100 100 100 100 100 100 100 100 100 100 75

Max 100 100 100 100 100 100 100 100 100 100 100

Min 85 50 50 50 50 50 50 50 50 50 50

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Table 11 Subjective ratings of effort, concentration, and fun for self-initiated activities (subjective ratings on a scale of 0-100)

Age

Category 6 7 8 9 10 11 12 13 14 15 16

Physical Effort

Mean 79.44 78.18 78.08 78.85 80.00 82.33 84.67 88.67 87.00 88.33 87.50

S.D. 14.24 13.09 22.96 23.02 23.18 22.75 22.00 15.52 16.01 12.77 13.57

Median 80 80 80 85 80 85 90 90 90 90 90

Max 100 100 100 100 100 100 100 100 100 100 100

Min 60 60 20 20 20 20 20 50 50 60 70

Mental Concentration

Mean 71.11 69.09 70.77 71.54 71.92 77.33 78.67 80.67 79.33 85.00 85.00

S.D. 22.61 23.43 25.65 26.09 27.04 21.12 19.86 18.60 18.60 16.58 21.11

Median 70 70 70 70 75 80 80 80 80 90 95

Max 100 100 100 100 100 100 100 100 100 100 100

Min 30 20 20 20 20 40 40 40 40 40 30

Fun

Mean 90.00 84.09 84.23 84.62 83.85 84.00 83.67 82.33 82.00 79.33 83.33

S.D. 15.00 20.59 19.77 19.84 20.63 20.28 20.22 18.98 19.71 19.07 16.00

Median 100 90 100 90 90 90 90 90 90 80 85

Max 100 100 100 100 100 100 100 100 100 100 100

Min 60 50 50 50 50 50 50 50 50 50 50

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Table 12 Descriptive statistics for hours of invasion sports by age (excluding hockey)

Age

Invasion Hours 6 7 8 9 10 11 12 13 14 15 16

Total hours 632 822 998 1262 1498 1586 1942 1476 1074 382 198

Mean 42.13 54.80 66.53 84.13 99.87 105.73 129.47 98.40 71.60 25.47 13.20

S.D. 48.1 54.4 79.8 96.9 89.9 93.7 112.4 102.3 62.1 45.5 27.2

% Sport hoursa 8.57% 9.23% 9.47% 11.37% 12.21% 11.87% 13.13% 9.26% 7.11% 2.74% 1.31%

Median 32 32 40 40 96 96 108 80 72 0 0

Max 168 168 288 384 384 384 488 392 168 168 96

Min 0 0 0 0 16 0 0 0 0 0 0

# of athletes 11 12 11 14 15 14 14 14 11 6 4 Note: aAll sports including hockey

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Table 13 Descriptive statistics for hours of run scoring/field sports by age

Age

Run Hours 6 7 8 9 10 11 12 13 14 15 16

Total hours 348 420 644 704 692 772 800 700 560 240 40

Mean 23.20 28.00 42.93 46.93 46.13 51.47 53.33 46.67 37.33 16.00 2.67

S.D. 51 50 61 61 52 70 73 58 59 36 7

% Sport hoursa 4.72% 4.72% 6.11% 6.34% 5.64% 5.78% 5.41% 4.39% 3.71% 1.72% 0.26%

Median 0 24 24 32 36 36 32 24 0 0 0

Max 200 200 200 200 200 280 280 200 200 120 24

Min 0 0 0 0 0 0 0 0 0 0 0

# of athletes 6 8 10 10 11 11 10 9 7 4 2 Note: aAll sports including hockey

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Table 14 Descriptive statistics for hours of net sports by age

Age

Net Hours 6 7 8 9 10 11 12 13 14 15 16

Total hours 0 80 80 168 320 500 710 1110 580 412 216

Mean 0.00 5.33 5.33 11.20 21.33 33.33 47.33 74.00 38.67 27.47 14.40

S.D. 0 21 21 37 41 54 55 130 71 54 29

% Sport hoursa 0.00% 0.90% 0.76% 1.51% 2.61% 3.74% 4.80% 6.97% 3.84% 2.95% 1.42%

Median 0 0 0 0 0 24 32 32 12 0 0

Max 0 80 80 144 144 208 208 528 272 208 96

Min 0 0 0 0 0 0 0 0 0 0 0

# of athletes 0 12 11 2 5 8 11 12 8 7 4 Note: aAll sports including hockey

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Table 15 Descriptive statistics for hours of target sports by age

Age

Target Sports 6 7 8 9 10 11 12 13 14 15 16

Total hours 176 208 448 416 452 484 696 696 712 808 984

Mean 11.73 13.87 29.87 27.73 30.13 32.27 46.40 46.40 47.47 53.87 65.60

S.D. 28 28 79 76 76 77 79 79 78 79 84

% Sport hoursa 2.39% 2.34% 4.25% 3.75% 3.68% 3.62% 4.71% 4.37% 4.71% 5.79% 6.49%

Median 0 0 0 0 0 0 12 12 32 32 32

Max 96 96 300 300 300 300 300 300 300 300 300

Min 0 0 0 0 0 0 0 0 0 0 0

# of athletes 4 5 5 6 7 7 9 9 10 10 11 Note: aAll sports including hockey

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Table 16 Descriptive statistics for hours of individual sports by age

Age

Individual Hours 6 7 8 9 10 11 12 13 14 15 16

Total hours 240 236 400 384 476 500 528 504 336 228 116

Mean 16.00 15.73 26.67 25.60 31.73 33.33 35.20 33.60 22.40 15.20 7.73

S.D. 24 24 32 33 31 34 36 33 32 27 19

% Sport hoursa 3.25% 2.65% 3.80% 3.46% 3.88% 3.74% 3.57% 3.16% 2.22% 1.63% 0.76%

Median 8 0 16 16 24 24 24 24 0 0 0

Max 80 80 96 96 80 104 104 84 84 80 72

Min 0 0 0 0 0 0 0 0 0 0 0

# of athletes 8 7 9 8 10 10 10 10 6 5 3 Note: aAll sports including hockey

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Table 17 Specific sport participation by age

Age

Total

Sport 6 7 8 9 10 11 12 13 14 15 16 -

Soccer 11 11 10 10 11 11 11 7 6 1 1 15

Baseball 7 9 9 10 10 11 10 9 7 3 2 12

Basketball 2 2 3 7 8 9 11 12 6 2 0 12

Golf 5 6 6 7 8 8 11 11 12 12 11 12

Volleyball 0 0 0 2 4 7 10 11 6 3 2 12

Athletics 2 2 5 5 6 6 7 7 4 3 1 9

Badminton 0 7 0 0 2 4 7 7 3 1 1 7

Lacrosse 0 0 1 5 6 4 4 3 2 0 0 6

Swimming 5 6 6 5 4 3 2 2 2 2 2 6

Football 0 1 3 3 3 4 4 4 4 4 4 5

Cross Country 1 1 1 1 4 3 3 2 2 0 0 4

Tennis 0 0 0 0 0 1 2 2 2 2 2 3

Gymnastics 2 0 0 0 0 0 0 0 0 0 0 2

Dodgeball 0 1 1 1 1 1 1 1 1 1 1 1

Inline Hockey 0 1 1 1 1 1 1 0 0 0 0 1

Ultimate Frisbee 0 0 0 1 1 1 1 1 1 0 0 1

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Table 18 Answers to questions regarding the degree of specialization

Statistic

Mean S.D. Median Mode Max Min

Specialization questions

Consider hockey your main sport? 11.67 3.39 13 14 15 5

Quit other activities to play hockey? 12.47 1.85 12 12 15 8

Train for hockey more than 8 months a year? 12.40 2.92 13 13/14/15 16 7

Calculated training for more than 8 months 7.80 2.46 7 6 14 6

Reported specialization degree

Moderately specialized 12.33 2.53 13 14 15 7

Highly Specialized 13.40 1.76 14 15 16 10

Calculated specialization degree

Moderately specialized 11.00 3.09 12 13 15 6

Highly Specialized 12.87 1.88 14 14 15 8

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Table 19 Answers to qualitative questions of issues regarding specialization Questions Response

Not allowed or permitted to play other sports during the season? 8 - Yes

Person making the request 5 - Coach, 3 - Coach and parent

Reason given 8 - Prevent Injury

Not allowed or permitted to play other sports during the off-season? 2 - Yes

Person making the request 2 - Coach and parent

Reason given 2 - Prevent injury

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Table 20 Responses to supplemental questions regarding specialization Questions Responses

Did you attend a 'hockey academy'? 1 -Yes

Did you play for more than one city/association? 11 - Yes

Reason for changing 9 - Play higher competition, 1 - Avoid conflict with a coach, 1 - Not selected for previous team

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Figure 1. The percentage of the total accumulated hours divided by category for each age.

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Figure 2. The combined accumulated hour totals for hockey-specific deliberate practice, hockey-specific deliberate play, organized hockey games and sports other than hockey at each age

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Figure 3. The maximum, minimum, and median accumulated hour values of hockey-specific deliberate practice activities by age

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Figure 4. The maximum, minimum, and median accumulated hour values of hockey-specific deliberate play activities by age

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Figure 5. The maximum, minimum, and median accumulated hour values of organized hockey games by age

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Figure 6. The maximum, minimum, and median accumulated hour values of sports other than hockey by age

85

Figure 7. Percentage of total sport hours divided by hockey-specific deliberate practice activities, hockey-specific deliberate play, organized hockey games and sports other than hockey at each age

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Figure 8. The combined accumulated hours for all participants

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LITERATURE REVIEW

Sport specialization is described as the selection of one sport at the expense of

participating in other sports or activities (Brenner, 2016; Ferguson & Stern, 2014), and

has become a popular and contentious topic in recent years (Hornig, Aust, & Güllich,

2016; The Associated Press, 2016). If an athlete attempts to reach elite status in any sport,

the athlete would likely have to choose to focus on only one sport at some point in his or

her development, with just a few notable exceptions (Hill & Simons, 1989); however, the

optimal age for this specialization has not been agreed upon in the literature (e.g.,

Brenner, 2016; Myer et al., 2015a; Williams & Ford, 2008). Both academic journals

(Brenner, 2016) and popular media sources (Finley, 2006; Nichols, 2016; Perry, 2016)

have covered the topic extensively, with different messages and results (Côté, 1999;

Ericsson, Krampe, & Tesch-Römer, 1993; Ford, Ward, Hodges, & Williams, 2009).

Some sources state that accumulated hours of deliberate practice are the necessary

component to attain sport excellence (Ericsson et al., 1993; Williams & Hodges, 2005;

Ward, Hodges, Starkes, & Williams, 2007; Williams, Ward, Bell-Walker, & Ford, 2011),

whereas other sources state that the negative effects of dedicating oneself to a sport too

early can be detrimental, if not catastrophic, for development (Côté, 1999; Myer et al.,

2015b).

Definitions

Even though this topic has garnered considerable attention, some of the

terminology is not well defined (Brenner, 2016; Jayanthi, Pinkham, Dugas, Patrick, &

LaBella, 2012). Jayanthi et al. (2012) define specialization as an “intense, year-round

participation in one sport with the exclusion of other sports” (p.252). Ferguson and Stern

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(2014) stated that when the exclusion of other activities occurs at a young age it is

considered early specialization. These definitions, although concise, are somewhat vague.

Strachan, Côté, and Deakin (2009) used athletes aged 12 to 16 who invested at least 15

hours per week to their sport when studying early specialization, and noted that other

studies have used 10 hours per week as the specialization threshold. The age groups used

in Strachan et al. (2009) are directly related to Côté’s (1999) developmental model for

sport participation (DMSP), which made the determination that many successful athletes

‘invest’ into only a select few sports during these ages. Lloyd, Faigenbaum, Howard, De

Ste Croix, and Williams (2012) used the threshold of 16 hours per week to label an

athlete as a specializer; showing a lack of congruence between studies.

Some have criticized Côté’s (1999) DMSP stating that the ages are over-

simplified, and do not account for individual differences at each age (Gulbin, Croser,

Morley, & Weissenteiner, 2013). LaPrade et al. (2016) added that early specialization

occurs when athletes train eight months a year and are limited to one sport as

prepubescents. Jayanthi, LaBella, Fischer, Pasulka and Dugas (2015) quantified the

definition by creating a continuum to classify athletes as being highly specialized,

moderately specialized, or having a low degree of specialization. This continuum was

based on answers that athletes provided when asked if they could identify one sport as a

main sport, if they had quit other sports for their main sport, and if they trained for their

main sport for eight or more months a year. An answer of ‘yes’ to a question was given a

value of one, and the total of all three questions created a score of three, two, or one;

relating to a high, moderate, or low score, respectively. This continuum has been also

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used by Bell et al. (2016) and McFadden, Bean, Fortier, and Post (2016) in separate early

specialization studies involving high school athletes.

Background

Early specialization is not a new approach to sport talent development; it was first

popularized when the Soviet Union and other Eastern European countries introduced

year-round training programs in the quest for political power through Olympic success

starting in the 1950s (Gonçalves, Rama, & Figueiredo, 2012; Myer et al., 2015a;

Washburn, 1956). Washburn (1956) detailed how the former U.S.S.R. garnered support

for their own regimen by creating propaganda which stated that the Americans were

using the Olympics as a training platform for the U.S. military. This propaganda

coincided with the U.S.S.R. lowering the iron curtain to allow select athletes and teams to

travel internationally for competition. The resulting success was seen in the 1956 winter

Olympics and was touted by the U.S.S.R. government as a triumph in the Cold War

(Washburn, 1956). While the international media focused on the year-round participation

and specialization of some elite young Eastern European athletes leading to their

Olympic success, these countries did emphasize early sport diversification for most

sports. Nonetheless, the perception was that specialization led to athletic success, and this

concept then started to migrate West leading to elite training programs in various sports

for professional gain (Malina, 2010).

Those responsible for athlete development in the United States were

understandably reluctant to accept the centralized ways of the East Bloc. However, it was

apparent that changes were needed with the success of the Soviets at the 1972 summer

Olympics. The Soviet’s athlete development model produced more medals than the

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USA’s decentralized system, including the coveted men’s basketball gold medal. Marred

by controversy, losing the men’s basketball final caused outrage that may have been the

catalyst which ultimately prompted major reforms to American amateur sport polices

(Bowers, 2017; Vealey & Chase, 2016). Instead of unilaterally adopting Eastern

philosophies, and thus admitting inferiority to the Soviet Union’s talent identification

models, the U.S. introduced the Amateur Sports Act (ASA) in 1978 (Bowers, 2017). The

ASA attempted to address Cold War insecurities by giving power to the U.S. Olympic

committee to control all Olympic sport governing bodies, but due to poor enforcement

mechanisms, the law has created a system which lacks co-ordination of developmental

principles, and lends itself to the promotion of privatized and commercialized coaching

(Bowers, 2017). Likewise, in the 1980s political changes led to further privatization and

commercialization of sport due to cuts in youth programs in an attempt to reduce

government spending (Coakley, 2010). These cuts were based on polices that were

implemented based on the ideological assumptions that social order is founded on

personal responsibility, economic growth is best established by unregulated self-interest,

and that financial inequalities fuel personal motivations (Coakley, 2010). From these

policies emerged year-round commercial programs that created primary income sources

for many adult coaches and entrepreneurs, all of whom had to ‘sell specialization’ to

justify their businesses (Coakley, 2010).

From these early stages, various sports (e.g., tennis (Carlson, 1988), field hockey

(Güllich & Emrich, 2014), golf (Hayman, Polman, Taylor, Hemmings, & Borkoles,

2011), soccer (Livingston, Schmidt, & Lehman, 2016; Ward et al., 2007), volleyball

(Coutinho, Mesquita, Fonseca, & Côté, 2015), ice hockey (Soberlak & Côté, 2003),

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cricket (Weissenteiner, Abernethy, & Farrow, 2009), and gymnastics (Law, Côté, &

Ericsson, 2007)), have been studied for the relevance of various developmental activities.

In agreement with Eastern Bloc philosophies, it has been shown that sports such as figure

skating, gymnastics, and diving may require specialization before puberty (Brenner,

2016; Malina, 2010; Wall & Côté, 2007); however, sufficient evidence exists to show

that a range of activities before specializing can foster the positive experiences that sport

promotes, such as prosocial behaviour and acquiring social capital (Côté, Lidor, &

Hackfort, 2009). Although perceptions existed that the Soviet Union showed athletic

superiority due to specialization, most training involved many different elements, and

athletes had diverse backgrounds (Malina, 2010). Moreover, studies have shown that

specializing after puberty does not negatively affect an athlete’s ability to develop into an

elite athlete in a given sport (Bridge & Toms, 2013; Carlson, 1993; Soberlak & Côté,

2003).

Current Trends

Athletes in the 21st century have many motives to excel in their given domains,

and the belief that intense, year-round participation is the best route to excellence has

caused early specialization to be at an all-time high (American Academy of Pediatrics,

2000; Lloyd et al., 2012). For many sports throughout North America, the appeal of being

awarded an athletic scholarship is highly motivating given the value attributed to post-

secondary education (Myer et al., 2015a). Lucrative, yet elusive, professional contracts

and the prestige that comes with playing at the professional level also motivate athletes to

excel (Feeley, Agel, & LaPrade, 2016). These extrinsic influences have resulted in many

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coaches privatizing commercial programs, and promoting longer seasons, with year-

round participation (Coakley, 2010; Feeley et al., 2016).

Due to the political shifts in the 1980s which promoted personal responsibility,

parents’ moral worth became directly related to their children’s success. These new

cultural norms caused parents to nurture their children’s goals, and become obsessed with

seeking out professionally supervised and culturally visible activities (Coakley, 2010). As

well, with the developing relationship between parents’ worth and children’s success,

parents embraced exclusive and specialized training as a method to control and shape

their children (Coakley, 2010). Therefore, parents invest both time and money in these

for-profit programs as they see them as the optimal path to their children succeeding in

sport because of their perceived cultural relevance (Coakley, 2010; Harwood & Knight,

2015). The alleged benefits from these private, or commercial, programs manipulate

parents to have their children specialize early, despite evidence supporting playing a

variety of sports during the developmental years for improved performance (Bridge &

Toms, 2013; Bruce, Farrow, & Raynor, 2013; Coutinho et al., 2015; Soberlak & Côté,

2003), injury reduction (Valovich McLeod et al., 2011), continued participation (Wall &

Côté, 2007), and social asset accumulation (Mostafavifar, Best, & Myer, 2013).

Even with so many proponents for diversification, there is an alarming trend

which shows a decrease in the number of multi-sport athletes due to private entities

promoting specialization as the only mechanism to reach an elite status (Aspen Institute,

2016). The National Collegiate Athletic Association (NCAA) has studied the trend of

early specialization and has found that more than 50% of male soccer and hockey players

have specialized by 12 years of age (Schwarb, 2016). The increase in the number of

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athletes ignoring the advice of practitioners (Gentilcore, 2014; Gulbin et al., 2013),

researchers (Côté & Hancock, 2016; Jayanthi et al., 2012), and even professional athletes

(Branstad, 2016; Domi, 2015; McCarty, 2013; Nichols, 2016; Scott, 2017), shows the

extent to which early specialization remains problematic.

Developmental Models

Deliberate Practice

One reason specialization has been popularized recently is due to the work of

Ericsson et al. (1993). In their study, the authors explained that a monotonic relationship

existed between practice and performance; or simply, the more one practices, the better

one will perform. They compared practice habits of accomplished violinists, showing that

those who accumulate more hours of relevant practice are more likely to be considered

elite violinists. The study has garnered much support for deliberate practice and early

specialization across various domains, including sport (Bruner, Erickson, McFadden, &

Côté, 2009; Helsen, Starkes, & Hodges, 1998). The Ericsson et al. (1993) study of

musicians supports the 10-year model first reported in the Simon and Chase (1973) study

of chess players; suggesting that 10 years of meaningful practice is needed for success.

Ericsson et al. (1993) stated that for athletes, activities such as recovery and sleeping

must be considered to produce the physiological adaptations needed from the intense

training. The authors’ findings suggest that there is a negligible effect from innate

abilities, that superior athletes are created from physiological differences generated from

a higher volume of appropriate training, not from a genetic advantage.

The deliberate practice pathway to elite performance has also been supported in

other studies. For example, Helsen et al. (1998) suggested that international-level field

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hockey and soccer players had accrued more hours of deliberate practice than lesser

skilled peers. However, this finding came with a retooling of the term deliberate practice

from Ericsson et al.’s (1993) original definition. The authors found that in the sport

domain, practice is often seen as enjoyable and highly relevant by athletes; this

contradicts Ericsson et al. (1993) because the musicians in that study found that

deliberate practice is inherently unenjoyable in comparison to other activities that they

rated. The retooled definition for sport was also supported in Baker, Côté, and Abernethy

(2003a) when studying elite Australian national team athletes.

Hodges and Starkes (1996) also showed that international-level wrestlers had

collected more hours of practice than club level wrestlers; and Williams and Hodges

(2005) credit the amount of practice as the driving force behind developing expertise in

soccer. Simon and Chase (1973) stated that experts store complex problems as ‘chunks’

of memory, which allows for quick recall and allows for better performance. These

‘chunks’ are obtained by gathering experience and deliberate practice hours. Diogo and

Gonçalves (2014) found that elite male soccer and volleyball players accumulated more

hours of training than a comparison group of less accomplished athletes. However,

Barker, Barker-Ruchyi, Rynee, and Lee (2014) contend that it is not the accumulation of

hours that may have the most impact on development, but that the ability and willingness

to meaningfully reflect on previous training is what drives expertise.

Developmental Model of Sports Participation

Contrary to the deliberate practice model, which focuses on accumulated practice

hours, Côté (1999) developed the stage-based DMSP to explain an athlete’s development.

Côté noted that there are three stages in an elite athlete’s development. First, from the

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ages of 6 to 13, the athlete samples several sports and is given opportunities to enjoy each

of them. Second, from the ages of 13 to 15, the athlete specializes with a commitment to

one or two sports, where achievement becomes more important. After this stage, elite

athletes are older than 15 years of age and invest into one sport with the hope of attaining

success. There is the possibility of a fourth stage if a high degree of accomplishment has

been attained; this is referred to as the performance, or perfection, stage. During this time,

athletes maintain their elite status, and attempt to perfect his/her domain. Côté’s (1999)

model builds on Bloom’s (1985) three-stage pathway of initiation, development, and

perfection, which also encourages young athletes to sample a variety of sports during

their early development (Ford et al., 2009; Harwood & Knight, 2015).

Côté’s (1999) DMSP stages coincide with the levels of psychosocial development

presented in Kushner et al. (2015). These authors recommend a variety of games and

activities during the preoperational stage (ages 2 to 6), which may enhance motor

capacity when the athlete is older. From the ages of 7 to 9, children are in the early

concrete operational stage, which includes movement refinement. The authors emphasize

that sport diversity at this stage may reduce sport-related injury and augment strength and

motor skills. At the age of 10, athletes can perform more advanced exercises and skills

given they have been engaged in previous activities and are approaching mastery of such

movements. At approximately 12 years of age, children enter a formal operational stage,

which aligns with the DMSP switching to the specialization stage. Physically, hormonal

changes cause athletes to have more of a positive outcome from an increase in intensity

than in other stages. Emotionally, athletes can better process their own strengths and

weaknesses, and compare their efforts to those of their peers (Kushner et al., 2015).

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Long-Term Athletic Development

The age-based stages of players’ evolution (e.g., DMSP) have been criticized as

being too simplified due to the cultural limitations and physiological differences between

individual athletes (Gulbin et al., 2013; Martindale et al., 2010). Bloom (1985) found

that, in his own three-stage model, graduating from one step to the next was

accomplished by completing specific tasks, as opposed to a chronological age

(Martindale et al., 2010). Balyi, Way, and Higgs (2013) proposed a seven-stage model to

optimize long-term athlete development (LTAD). Balyi et al.’s (2013) model differs

because it is based on developmental stages, and is not anchored by ages like the DMSP.

Some authors believe that the seven stages, which progress from building ‘FUNdamental’

motor skills through to competition and, ultimately, retirement are more appropriate and

comprehensive than the DMSP (Gould, 2009; Gulbin, Weissensteiner, Oldenziel, &

Gagné, 2013).

The goal of the model proposed by Balyi et al. (2013) is to create a healthier and

more active general population. The first stages of ‘active start,’ ‘fundamentals,’ and

‘learn to train’ promote the skills required for physical literacy, and are recommended to

be completed before the onset of adolescence. High performance athletes may then

progress through the stages of ‘train to train,’ ‘train to compete,’ and ‘train to win’ by

showing key psychosocial and physical indicators. As with high performance, tiered

systems, increasingly difficult challenges cause athletes to be filtered out at each level,

allowing fewer to move on to the subsequent level or tier. As athletes are filtered out of

the high performance system, they would ideally enter the ‘active for life’ stream with the

goal of having them stay active and healthy. These stages have been adapted by Hockey

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Canada (2013) by making the stages specific to the contextual and logistical constraints

of hockey in Canada. The program is designed to increase physical literacy and life-long

engagement in hockey activities by encouraging young players to participate in other

activities until the age of 12, when it is recommended to increase the specificity of

training towards hockey.

Optimisation of Sport and Athlete Development

Gulbin et al. (2013) also created a developmental framework, this one using four

stages to describe the progression from beginner to master: ‘foundation,’ ‘talent,’ ‘elite,’

and ‘mastery.’ Individuals progress through the stages by attaining specific goals at each

of the stages with three potential outcomes: ‘active lifestyle,’ ‘sport,’ or ‘sport

excellence.’ In the ‘foundation’ stage, athletes advance through three micro-stages:

learning basic movement patterns (F1), movement refinement (F2), and sport specific

commitment and competition (F3). After a foundation of movement has been established,

athletes would differentiate into one of the three main outcomes. Those attempting to

reach an elite status move into the high-performance pathway, which starts with the

‘talent’ phase highlighted by four micro-stages. These micro-stages act as a filter with

fewer athletes passing through each phase because each micro-stage is more difficult than

the previous. The ‘talent’ micro-stages start with demonstrating talent (T1), then one’s

talents are verified by a coach (T2), which leads to sustained and purposeful practice

(T3), and, for successful athletes, this phase culminates in gaining professional support

(T4). For those that pass through the ‘talent’ phase, they progress to the ‘elite’ stage, with

two micro-stages. The first of these is ‘representing’ their country at a senior elite

(Olympic or professional) level, while the second micro-stage is attaining ‘success’ at the

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senior elite level. The framework culminates with the mastery stage, which would equate

to sustained success at the highest senior (i.e., professional, Olympic) levels.

Gulbin et al. (2013) claim that this framework is multidisciplinary for use across

sports, and does not attach ages to each of the stages to avoid limiting the classification

system. Although the aged-based stages could be considered constraints in other models,

there are contextual considerations, such as draft age, that would require stages to be

completed by a certain age. These authors provide yet another example that physical

literacy is a vital ingredient to successful athleticism, and although the authors do not

refer to other sports specifically, playing a variety of sports is one method of attaining

movement competencies (Brylinski, 2010; Pesce, Faigenbaum, Crova, Marchetti, &

Bellucci, 2012).

Position Statements

The cultural shift toward sport specialization has prompted several organizations

to release position statements advising against the potentially harmful trend. The National

Athletic Trainers’ Association advocates that delayed specialization may reduce sports

injuries, and states that injuries caused by specialization may ultimately lead to a

sedentary lifestyle (Valovich McLeod et al., 2011). The International Olympic

Committee (IOC) recognizes that early specialization has become a problem (Bergeron et

al., 2015), and recommends a balanced approach to athlete development, including age

appropriate training volume and intensity with proper nutrition, sleep, and emotional

support throughout a child’s development (Mountjoy et al., 2008). The IOC consensus

statement also calls upon individual sport governing bodies to monitor and track injury

rates, coaching quality, and training volume to minimize injury and dropout. The

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American Orthopaedic Society for Sports Medicine also released a consensus statement

calling for improved messaging that specialization can lead to avoidable injuries, and that

sport diversity does not impede a pathway to high performance sport (LaPrade et al.,

2016). With the evidence that currently exists, all known position statements and

consensus papers have been against early specialization, with the exception of artistic or

kinesthetic, routine-based, sports (e.g., figure skating and gymnastics) (Bayli et al, 2013;

Ferguson & Stern, 2014).

Potential Benefits of Early Specialization

Despite the criticisms that early specialization has received, there are some

alleged benefits. At the root of Ericsson et al.’s (1993) work is the monotonic benefits

assumption, which addresses the positively correlated relationship between practice and

performance. Although this assumption has been misrepresented and taken out of context

by the media (Berry, Abernethy, & Côté, 2008; Brenner, 2016; Côté, 2011), there does

exist a positive relationship between practice and performance (Barker et al., 2014;

Helsen et al., 1998; Jayanthi et al., 2012; Livingston et al., 2016). Experts spend more

time training compared to their lesser accomplished peers (Helsen et al., 1998), although

the amount of time spent training, and the types of training do differ among experts

(Baker et al., 2003a; Baker, Côté, & Abernethy, 2003b). Even amongst a sample of

similarly talented soccer players, Williams et al. (2011) showed that those who had

accumulated more hours of soccer-specific practice scored better on perceptual-cognitive

measures than a group of equally-talented players with less practice. It is commonly

believed that the quest for external rewards is the reason behind the specialization

required to accumulate these hours (Myer et al., 2015a). However, a more recent study by

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Post, Thein-Nissenbaum, Stiffler, Brooks, and Bell (2017) showed that 343 current

Division I NCAA athletes from various sports (i.e., basketball, golf, hockey, soccer,

tennis, football, wrestling, softball, and volleyball) specialized due to enjoying that sport

the most.

The age at which athletes are identified as talented becomes important because

early decisions about their abilities influence coaches’ decisions throughout their

development (Carlson, 1993). Therefore, accumulating practice hours to be labeled as

‘elite’ early in a career can encourage early specialization (Ferguson & Stern, 2014;

Malina, 2010). Even though the prepubescent success that is attained through early

specialization is often due to physical factors, such as relative size, that may change after

puberty (Barreiros, Côté, & Fonseca, 2014). In addition, Coakley (2010) explained that

not specializing may create a stigma, suggesting that the child is not committed to the

sport or team. Ericsson et al. (1993) argued that the physiological differences in adults are

due to the accumulation of deliberate practice hours early in an athlete’s development,

however these physiological differences are not proven to be domain-specific, or sport-

specific (Baker et al., 2003a).

Potential Adverse Effects of Early Specialization

Some authors have questioned the merits of early specialization, stating that they

may not be valid (Baker, Cobley, & Fraser-Thomas, 2009; Brenner, 2016; Jayanthi et al.,

2012), that the negative effects may be very costly to a young athlete (Bridge & Toms,

2013; Russell, 2014), and that a greater quantity of practice will not necessarily lead to

success (Brylinsky, 2010). Firstly, early specialization has a strong positive correlation

with chronic, or overuse, injuries due to the intense training without adequate rest

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(Brenner, 2016; Halvorson, 2014; Mattson & Richards, 2010; Mostafavifar et al., 2013;

Valovich McLeod et al., 2011). Bell et al. (2016) observed 302 high school athletes and

classified them as low, moderately, or highly specialization based upon their answers to a

sport specialization survey. Those who reported to have trained in one sport for more

than eight months out of a year (i.e., high specialization) were more likely to report a hip

or knee injury. These results took into account the athlete’s age and the time spent in

sport of any kind, isolating specialization as the risk factor for such injuries. A similar

study was also conducted by Jayanthi et al. (2012) with 7- to 18-year olds who were

recruited from a sports medicine clinic, also finding an independent risk of injury for

specialized athletes. Hall, Barber Foss, Hewett, and Myer (2015) found that specialized

female middle school and high school athletes may be four times more likely to suffer

specific knee injuries than non-specialized players.

Another aspect of early specialization that should be considered is that early

success of being selected for elite junior teams does not predict success at senior levels,

and that early specialization can cause a player to reach his or her highest level of play at

the age of 15 (Barreiros et al., 2014; Güllich & Emrich, 2014), which is well before the

type of senior level success that is being prioritized. Moesch, Elba, Hauge, and Wikman

(2011) found that more time on a national junior athletics team, as well as more

accumulated practice hours by age 15, might negatively predict success at international

athletics competitions. While these findings contradict Ericsson et al. (1993), more hours

of accumulated practice by age 18 did predict international success. Accumulating these

practice hours do appear to be important in career progression, however doing so too

early may be detrimental to success at the highest level. Despite the potential for

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negatives outcomes that are reported in the media (Scanlan, 2017; Ward, 2015), parents

are allowing their children to specialize at alarming rates (Malina, 2010). Some authors

consider early specialization one of the biggest issues in youth sport (Bergeron et al.,

2015), largely because parents do not want to acknowledge that their child may fall

victim to the pitfalls (Laprade et al., 2016).

A lack of overall athleticism due to repeating the same movement patterns may be

one factor in the increased rate of injury in specialized athletes (Jayanthi et al., 2012;

Myer et al., 2015b). Not only are strength and flexibility imbalances created by the

repetitive patterns involved in participating in one sport (Jayanthi et al., 2012), without

diversity early in a sporting career, the appropriate neuromuscular skills to prevent injury

are not developed (Myer et al., 2015a). Early specialization also causes a reduction in

overall motor skill proficiency, corresponding to the potential for losing the development

of particular sport skills for a lifetime (Myer et al., 2015b). The possibility also exists that

specialized athletes are willing to push themselves with more intensity, and play through

pain until they need to seek medical attention (Jayanthi et al., 2012).

Another negative impact that early specialization may have on a young athlete is

missing out on other opportunities that they may enjoy (Brenner, 2016). This can mean

being unable to play a sport, or a position within a sport, that the child may enjoy after

maturation (Branta, 2010; Lloyd & Oliver, 2012; Wierike, Elferink-Gemser, Tromp,

Vaeyens, & Visscher, 2015; Williams, Ward, Ward, & Smeeton, 2008). A more diverse

background may allow an athlete to find a more functionally appropriate sport or position

(Güllich & Emrich, 2014). Being denied these opportunities may have negative physical

effects, and may also negatively affect a child’s psychological development (Brenner,

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2016; Jayanthi et al., 2012). Brenner (2016) details an array of psychological

ramifications, including altered social relationships and socially maladaptive behaviours,

resulting in a concern for the risk of physical, emotional, or sexual abuse by the adults

involved in the sport. The author also presents evidence for the increase of anxiety,

depression, and burnout amongst early specializers. These emotional responses may be

due to the intense pressure created by the overly-structured, and adult-driven training and

competitions (Gould, Wilson, Tuffey, & Lochbaum, 1993; Myer et al., 2015b; Russell,

2014; Weissensteiner et al., 2009), or they may be related to the reduced intrinsic

motivation to participate because chasing extrinsic rewards is emphasized (American

Academy of Pediatrics, 2000; Gould, Udry, Tuffet, & Loehr, 1996). This view is

challenged by Post et al.’s (2017) finding that NCAA athletes listed enjoyment for their

sport as a more important reason for specialization than extrinsic rewards, or parental

influence. Of note, these athletes rated themselves as specialized in high school, which

would not qualify them as early specializers as they would have been 15 years old at the

time of specialization.

Due to the many emotional and psychological issues prominent in early

specializers, it has also been shown that these athletes are also more likely to drop out of

sport altogether (Baker, 2003; Coakley, 2010; Hecimovich, 2004; Mostafavifar et al.,

2013). These stressors create a negative attitude towards physical activity, and may lead

to a sedentary lifestyle and increase the chance of obesity (Myer et al., 2015a; Russell &

Limle, 2013). While only a minority of athletes will attain the level of success that

specializers are often chasing, those who reach professional or championship heights are

glorified. However, there are many more cases of players that leave sport completely,

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with those cases going unnoticed (Coakley, 2010). Strachan et al. (2009) found that a

sample of specializers rated higher on emotional and psychological exhaustion than a

sample of athletes who had a more diverse sporting background. Although the Strachan et

al. (2009) study focused on individual sport athletes, Wall and Côté (2007) studied active

minor hockey players and compared them to a group that had dropped out of hockey. The

authors found that the earlier children engaged in off-ice training, and the more often they

trained, the more likely they were to drop out. Bruner, Hall, and Côté (2011) also showed

that differences exist in the experiences of individual sport athletes and those who

participate in team sports. For example, basketball players reported a greater rate of

experiences promoting teamwork than middle-distance runners, although middle-distance

runners reported fewer negative experiences.

Unfortunately, children who drop out do not get to experience the benefits of

physical activity and the social assets gained through sport. Wiersma (2000) believes that

when an athlete no longer enjoys a previously enjoyable activity, it is not a personal

failure of the athlete. Instead, the author refers to this as ‘burnout,’ and considers it a

failure of the organization involved. This burnout may also affect the athlete’s family, as

Livingston et al. (2016) noted that an athlete beginning sport participation too early often

causes the family to burn out and experience less enjoyment from their child’s

participation.

Potential Benefits of Sport Sampling

Brylinsky (2010) explains that multiple sport athletes are still exposed to

appropriate physiological strains for sport success. While single sport athletes may have

more control over their annual plan, the quality of the general physical preparedness

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principles during an athlete’s early development are more important. The author goes on

to say that deliberate play activities enable elite performance by helping to build a

resistance to stress and heighten one’s sense of competence, which has been supported in

other research (Côté & Fraser-Thomas, 2008; Masters, 2008; Maxwell, Masters, &

Poolton, 2006). Deliberate play is considered to be a major component of sampling since

being introduced by Côté (1999) in the DMSP. Deliberate play is characterized by

voluntary and pleasurable participation in sport, providing immediate gratification. The

enjoyment from this type of participation may help develop intrinsic motivation which

has been shown to be an important characteristic in successful athletes (Weissensteiner et

al., 2009), and may be explained by athletes’ willingness to participate in individual

deliberate practice activities later in their development (Vink, Raudsell, & Kais, 2015).

Sampling, or playing a variety of sports, is believed to create a more diverse

athletic background, and an increase in overall athleticism (Branta, 2010; Fransen et al.,

2012; Güllich & Emrich, 2014; Nacierio & Faigenbaum, 2011; Wall & Côté, 2007).

Athletes may have many sport and personal outcomes in common despite having chosen

different pathways (i.e., specialization or sampling) (Strachan et al., 2009). However, it is

argued that by sampling various sports in the developmental years, athletes may decrease

the number of hours required to attain expertise in one sport (Baker et al., 2003a; Baker

& Young, 2014; Berry et al., 2008; Lidor & Lavyan, 2002; Rosalie & Muller, 2012). In

domains such as music and chess, 10,000 hours of deliberate practice during 10 years of

commitment may be required (Ericsson et al., 1993; Simon & Chase, 1973). In team

sports, it has been shown that athletes may achieve elite status with just 4,000 to 6,000

hours of deliberate practice (Baker et al., 2003a; Berry et al., 2008). Although, 10 years

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may be needed to reach the most elite levels of a specific sport, due to factors such as

reaching the appropriate physical maturity (Baker et al., 2003; Bruce et al., 2013; Jonker,

Elferink-Gemser, & Visscher, 2010; Rosalie & Müller, 2012). Golfers who sample

different sports may only need two years to reach a gain expertise if they show superior

abilities early in their careers (Hayman et al., 2011). This is in contrast to many other

sports where specialization is an attempt to become superior (McFadden et al., 2016).

While diversification may reduce the number of hours of deliberate practice early

in a career, there appears to be no indication that it hinders an athlete’s ability to become

elite after maturation, with the exception of some artistic and kinesthetic sports such as

gymnastics, figure skating, and snowboarding (Baker et al., 2003a; Bayli et al., 2013;

Bergeron et al., 2015; Côté et al., 2009; Ford et al., 2009; Soberlak & Côté, 2003).

Sampling has garnered considerable support by researchers and practitioners (Harwood &

Knight, 2015; Hendry, Crocker, & Hodges, 2014; Moesch et al., 2011; Valovich

McLeod, 2011) because it eliminates many of the detrimental effects of early

specialization (Coutinho et al., 2015; Güllich & Emrich, 2014; Moesch et al., 2011;

Mostafavifar et al., 2013), builds intrinsic motivation (Brenner, 2016; Côté et al., 2009;

Phillips, Keith, Renshaw, & Portus, 2010), and is a key determinant of life-long

engagement in sport (Fransen et al., 2012; Moesch et al., 2011).

Throughout the literature, there are examples of high performance athletes who

have sampled a variety of sports during their developmental years (LaPrade et al., 2016),

including athletes in triathlon (Fransen et al., 2012), netball (Bruce et al., 2013), hockey

(Robertson-Wilson, 2002), golf (Hayman et al., 2011), basketball (Leite & Sampaio,

2010), soccer (Hornig et al., 2016), field hockey (Güllich & Emrich, 2014) and tennis

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(Carlson, 1988, 1993). Soberlak and Côté (2003) studied the developmental activities of

four professional hockey players while playing junior hockey. They found results

consistent with the DMSP; the hockey players studied engaged in the majority of their

deliberate play from the ages of 6 to 12, then increased deliberate practice throughout

their development, and played most of their games after they were 16 years old. This is a

common theme, as Carlson (1993) found similar results with Swedish hockey players 10

years earlier. Specifically, Carlson (1993) found that elite players spent more time

playing sports in their free time and were injured less frequently, which helped them

become more technically competent, and allowed them to overcome being physically

immature relative to a comparison group. Robertson-Wilson (2002) also found that a

group of elite Canadian junior hockey players played more sports than a comparison

group of less successful players, and the comparison group started to specialize at 12

years old. Importantly, the argument has also been raised that more athletic children may

have the ability to be successful in several sports, contradicting the notion that playing

several sports may enable one to become more athletic (Bridge & Toms, 2013; Capranica

& Millard-Stafford, 2011).

With these results in mind, several authors have supported early sport

diversification throughout childhood (Bergeron et al., 2015; Côté et al., 2009; Lloyd et

al., 2012). Côté et al. (2009) outlined several benefits of sampling for all sports, with an

emphasis on increasing overall participation to help counter childhood obesity. The

authors strongly recommended diversification due to evidence that it builds different life

skills and peer groups, promotes prosocial behavior, a healthy identity, and the

acquisition of social capital. The International Society for Sport Psychology outlined

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seven postulates promoting diversification as a pathway to elite performance and

participation. These postulates indicate that diversification facilitates positive youth

development, both physically and psychosocially. Athletes should only start reducing the

number of sports that they participate in at the age of 13. If they choose to pursue an elite

sport status, they should not invest into one sport until the age of 16 (Côté et al., 2009).

More recently, Côté and Hancock (2016) outlined evidence-based policies regarding

youth sport that supported the DMSP by stating that athletes should not specialize until

13, and that seasons should not exceed six months. Once again, the authors point to

diversification as a method to promote both long-term participation and elite

performance.

Regardless of the path chosen, early specialization or sampling, it is understood

that there are many pathways to attain elite status in any domain (Collins & Parker, 2010;

Phillips et al., 2010). Despite the evidence supporting sampling as a pathway to success,

there appears to be a reduction in the number of children who voluntarily play different

sports, and the number of athletes that compete in more than one sport (Brenner, 2016).

Coakley (2010) states that it is the construct of youth sport that promotes the belief that

one must commit fully to one sport to succeed (Coakley, 2010). However, elite athletes

tend to accomplish career milestones, such as being selected for a national team, later

than non-elite athletes (Güllich & Emrich, 2014; Hornig et al., 2016; Moesch et al., 2011;

Vaeyens, Güllich, Warr, & Phippaerts, 2009), and specialize in accordance with the

DMSP (Carlson, 1993; Coutinho et al., 2015; Duffy, Lyons, Moran, Warrington, &

MacManus, 2006), which does not affect the overall amount of practice accumulated

(Baker, Côté, & Deakin, 2005; Moesch et al., 2011).

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Possible Mechanisms for Benefits of Sampling

The transfer of learning across domains involves applying previous experiences to

current circumstances; if the domain remains the same, the learning becomes more

specific (Rosalie & Müller, 2012). According to Rosalie and Müller (2012), transfer

across sport can be considered ‘near’ transfer if the sports are similar, or ‘far’ transfer if

the sports are very different. The transfer may have both conscious and subconscious

mechanisms allowing an athlete to adapt to inconsistent environments, which is a critical

skill for perceptual-motor success. Near transfer appears to have garnered the most

support throughout the available research (Allard & Starkes, 1992; Güllich & Emrich,

2014; Smeeton, Ward, & Williams, 2004; Williams et al., 2011). For example, Berry et

al. (2008) found that expert rugby players that played other types of invasion sports

scored better than non-expert rugby players on in-sport decision-making tasks.

Although far transfer does not appear to have the same volume of support,

research exists to support its merits as well (Güllich & Emrich, 2014). The across-sport

transfer is consistent with Baker et al. (2003a) who showed that athletes who sampled

other activities are more likely to be considered expert decision makers in team ball

sports. Coutinho et al. (2015) also concluded from their study of volleyball players that

the transfer of skills attained from participating in a greater number of diversified sport

activities early in development allowed for those athletes to become expert volleyball

players. These results may be explained by Lloyd et al. (2012) who contended that global

movements learned by young athletes will transfer as building blocks to more sport

specific performance movements due to the neural plasticity of pre-adolescent athletes.

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Another positive element of diversity in an athletic background is the acquisition

of physical literacy, which has been defined as mastering fundamental movement and

sports skills, such as the ABCs: agility, balance, coordination, and speed (Brenner, 2016).

There can also be psychosocial aspects, including having the motivation, confidence, and

desire to maintain an active and healthy lifestyle (Aspen Institute, 2016; Francis et al.,

2016; Whitehead, 2007). One method to provide children with opportunities to

experience a range of activities is through physical education classes in school (Pesce et

al., 2012). These classes can utilize multiple sports and activities to promote a healthy

lifestyle, and have the same positive effects as playing multiple sports outside of school

(Pesce et al., 2012). An integrated neuromuscular training program can also be applied to

enhance movement patterns that specialized athletes may not encounter (LaPrade et al.,

2016; Lloyd et al., 2012). Such a training program, however, may not mitigate all of the

detrimental effects of early specialization. In a study of minor hockey players, Wall and

Côté (2007) found that earlier commencement to off-ice training may increase dropout

rates in minor hockey players.

While the DMSP, and other proposed models, recommend waiting until 15 years

of age before specializing in one sport (Brenner, 2016), research indicates that there may

be specific ages that children would benefit most from sampling, or other types of

integrated neuromuscular training for global movements (Kushner et al., 2015; Viru et

al., 1999). For example, motor coordination and endurance later in childhood may be

improved with frequent physical activity as young as 2 years old (Kushner et al., 2015).

Also, prior to peak height velocity, children appear to be particularly receptive to the

development of overall motor function (Viru et al., 1999). During this time, refining

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movement patterns could allow an athlete to progress through a structured training

program more efficiently (Kushner et al., 2015). The window of opportunity for an

athlete to develop movement skills then continues until approximately 12 or 13 years of

age (Carlson, 1988; Ford et al., 2011; LaPrade et al., 2016). Accordingly, in a study of

elite athletes from the United Kingdom, Bridge and Toms (2013) showed that playing

three sports at 11 years old predicted achieving national level playing status at 16 to 18

years old. It is thought that these windows of development correspond with brain

maturation, although it is acknowledged that these age guidelines still need more research

(Ford et al., 2011), and exclude late-maturing athletes (Vaeyens et al., 2009). After basic

movement skills have been developed during prepubescence, post-puberty specialization

may become advantageous to those athletes pursuing a potential career in sport (Bergeron

et al., 2015; LaPrade et al., 2016).

Purpose

For those pursuing a career in sport, or any level of elite sport, the appeal of

extrinsic rewards has become almost irresistible, effectively professionalizing youth sport

(Gould, 2009). Specializing at some point may be a necessity to achieve this goal (Hill &

Simons, 1989), but doing so too early may have a detrimental effect on many aspects of

an athlete’s development (Baker & Horton, 2004; Wall & Côté, 2007). For any athlete,

the pathway to success can be very dynamic and individualized (Phillips et al., 2010).

Commitment (Baker & Horton, 2004), early sport diversity (Côté, 1999; Weissensteiner

et al., 2009), high quality training and coaching (Barker et al., 2014; Durand-Bush &

Salmela, 2002; MacNamara & Collins, 2015), and taking appropriate breaks in training

(Bergeron et al., 2015) appear to play a major role in attaining success, regardless of the

112

pathway. High quality training ensures that the hours that are accumulated in practice

time are meaningful, and the athlete is not simply hoarding or ‘bean counting’ practice

hours (Côté, Baker, & Abernethy, 2007). In team sports, these practices are often filled

with mismanaged time due to the logistics of involving a number of players (Smith,

Ward, Rodrigues-Neto, & Zhang, 2009). With the increasing popularity of early

specialization, it is important that sport organizations acknowledge the benefits that can

come from sampling different sports, and encourage athletes to play other sports during

training breaks from the main sport (Gould, 2009; Wiersma, 2000; Wuerth, Lee, &

Alfermann, 2004). With that in mind, more research is needed regarding the transfer of

physical skills, and psychosocial assets across sports to better understand optimal

developmental influences (Bruce et al., 2013).

Similar to other sports being criticized for early specialization, such as soccer in

England, and baseball in the U.S. (Culley & Walters, 2017; Shipley, 2013); immense

cultural relevance of hockey in Canada (Campbell, 2013; Keating, 2010; Martin, 2011;

Riches, 2015), following the guidelines of the DMSP and other diversification models for

hockey in Canada has its challenges. The Canadian Hockey League (CHL) is the primary

developmental league for National Hockey League (NHL) talent (“About the CHL”,

n.d.), and is made up of approximately 1,300 players on 60 teams across Canada and four

of the United States of America. The CHL has more players drafted to the NHL than any

other junior league in the world (National Hockey League, 2015). Players range in age

from 15 to 21 years, and are drafted into the league as early as 14 years of age (Western

Hockey League, n.d.). Therefore, at the age of 14, players who wish to play in this elite

developmental league to pursue a professional career must have already shown a superior

113

skill set and ability than their peers. In order to achieve this goal, players may feel

pressure to accumulate hours of practice at the expense of other sports, even though it is

recommended that these years be used to experience different sports (Brenner, 2016). The

adapted Bayli et al. (2013) model suggested by Hockey Canada (2013) recommends

initiating hockey-specific training and reducing the number of other sports played at the

age of 12, but this leaves little time for players to elevate their skills above that of their

peers.

A potential adverse effect of early specialization in hockey players is a common

overuse injury referred to as femoroacetabular impingement (FAI)6, due to the

biomechanics of skating being repeated with high frequency (Stull, Philippon, &

LaPrade, 2011). Philippon, Ho, Briggs, Stull, and LaPrade, (2013) found that youth

hockey players are 4.5 times more likely to show indications of FAI than a control group

of equally active skiers. This prevalence increases with age in the hockey group, showing

that the movements required to participate in the sport may enhance the likelihood of

developing FAI. These results coincide with Stull et al. (2011), as the authors stated that

risk factors for FAI include on-ice exposure and younger start ages. These results are

worth noting as hockey is identified as a sport with a high incidence of early

6 Femoroacetabular impingement is characterized by ‘cam’ and/or ‘pincer’ bony

abnormalities to the femoral head/neck or acetabular rim respectively. These

abnormalities may occur in isolation, or in conjunction with one another, and have been

studied for their effect on the acetabular labrum and articular cartilage junction

(Philippon et al., 2013).

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specialization (McFadden et al., 2016), meaning that younger athletes would be

performing these potentially harmful movements more often at a younger age. Any injury

will limit a player’s ability to participate to the best of his/her ability, and could possibly

prohibit him/her from playing temporarily, or permanently (Brenner, 2016; Halvorson,

2014).

As mentioned previously, Soberlak and Côté (2003) studied the developmental

activities of four professional hockey players while they were participating in the CHL.

The authors found that these players followed the deliberate practice and play

recommendations of the DMSP on their way to signing professional contracts in the

NHL. The study’s focus was on ‘late bloomers’ making the inclusion criteria very

selective, and only included 20-year old players who were not drafted into the NHL, but

signed NHL contracts before leaving junior hockey. Robertson-Wilson (2002) also

studied the developmental activities of junior hockey players, with a slightly larger

sample of 10 participants. The author interviewed parents to gain an understanding of

parental involvement in activities that were undertaken by their children. The results

showed that players in the OHL group participated in a greater number of sports than a

less successful comparison group. While these studies involved junior hockey players

participating in the CHL, both took place before the players could be affected by the

Hockey Canada Open Ice Summit on player development in 1998 (Hockey Canada,

2005). This summit reformed Hockey Canada’s grassroots program’s structure by

recommending and implementing the use of skills academies, which allowed minor

hockey players to practice hockey during hours that rinks were generally unused (Hockey

Canada, 2005). In the last 15 years, as many as 5,000 Canadian high schools have starting

115

offering hockey as part of the curriculum, allowing minor hockey players to spend more

hours playing hockey at a younger age (Traikos, 2015). These academies can cost up to

$50,000 in exchange for being on the ice for approximately nine hours per week in

addition to off-ice training, motivational speakers, and nutritional advice (Cole, 2012;

Traikos, 2015). Research regarding the degree and age of specialization by current junior

hockey players in pursuit of professional careers is warranted to further understand the

obsession with early hockey specialization in Canada.

In the current study, the developmental activities of hockey players that have

achieved an elite status (i.e., participation in the CHL) since the reformation of Hockey

Canada’s grassroots programs have been analyzed. Specifically, accumulated ‘deliberate

play’ and ‘deliberate practice’ hours have been categorized into the stages of the DMSP

(Côté, 1999) to determine if the ages for ‘specialization’ and ‘investment’ are still

accurate for elite hockey players. The amount and types of additional activities that were

participated in during the players’ childhood have also be studied to better understand

their developmental pathways. Additionally, the scale used in Jayanthi et al. (2015) has

been utilized to determine age at which these athletes became highly specialized. I

hypothesized that, although the athletes would have had a diverse athletic background

early in their athletic careers (e.g., sampling years), ‘specialization’ and ‘investment’

would occur earlier than suggested by the DMSP, and athletes would be considered

highly specialized before they are 14 years old. The purposeful sampling created a

homogenous group, which allowed for an information-rich sample to explore the

developmental activities of elite hockey.

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APPENDICES

Appendix A Interview Script�

Interview Procedures – Developmental Activities of Ontario Hockey League (OHL) Players

First, I would like to focus on the activities that you were involved in when you were younger. I would like you to list your involvement outside of mandatory school activities, for example music, dance, play and other types of activity. I am also interested in your early sport involvement. Looking back over your entire life please tell me of any type of activity that you engaged in on a regular basis before you decided to specialize in hockey. What musical, sport, play and artistic activities, if any, were you participating in before becoming seriously involved in hockey? Please list all of these activities, such as piano, dance, drawing, etc.

1. Any sport activity?

If YES: How old were you when you first got started? How long have you kept up the involvement? Please tell me about any periods when your involvement was stopped.

2. Any musical activity?

If YES: How old were you when you first got started? How long have you kept up the involvement? Please tell me about any periods when your involvement was stopped.

3. Any artistic activity such as dance or drawing?

If YES: How old were you when you first got started? How long have you kept up the involvement? Please tell me about any periods when your involvement was stopped.

4. Organized games with rules supervised by self and peers (e.g. game of street basketball)

If YES: How old were you when you first got started? How long have you kept up the involvement? Please tell me about any periods when your involvement was stopped.

5. Any other activity that consumed large amount of your time (e.g. watching sport on TV)

If YES: How old were you when you first got started? How long have you kept up the involvement? Please tell me about any periods when your involvement was stopped.

<Fill in Chart 1 Early Activities>

For each of the activities that you have provided, can you recall the number of hours per

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week and months per year you were regularly involved. Were there periods of time that participation would have increased or decreased (Peak/Off Peak)?

<Fill in hours/months per year in Chart 2>

Developmental Chart for Maturation and Performance in Main Sport

In this section of the interview I would like to focus more specifically on your development as a hockey player. We will try to get a sense of your development in hockey by assessing different factors that may have contributed to your achievement.

Development as an athlete

In this interview different aspects of your athletic development will be explored. However, first I would like you to answer a series of questions regarding reference information.

What is your date of birth? ____________________________________________________�

How old were you when you participated for the first time in the following activities on a regular basis?�

____ years old for first regular involvement in hockey as a child or adolescent.

____ years old for first involvement in supervised training by an adult in hockey.�

____ years old for first involvement in training not supervised by an adult in hockey.�

Performance career

How old were you when you first participated in any of the following in a scheduled competition with recorded results?

Competition at the Recreational Level

____years old for first participation on an organized team�

____years old when recognized among top five players at the recreational level

____years old when recognized as the best player at the recreational level

Competition at the Competitive level (AAA Team or highest level available)�

____years old for first participation on a competitive team�

____years old when recognized among top five players at the competitive level

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____years old when recognized as the best player at the competitive level

Competition at the League All Star level�

____years old for first participation on a League All Star team�

____years old when recognized among top five players at the League in your position

____years old when recognized as the best player at the League in your position

Competition at the national level�

____years old for first participation at the national level�

____years old when recognized among top five players at the national level in your position

____years old when recognized as the best player at the national level in your position

Competition at the international level (Junior Olympics, World Championships)

____years old for first participation in a major international event

____years old when recognized among top five players in the world for your age

____years old when recognized as the best player in the world for your age

Developmental milestones�

___ years old when idea for becoming an elite athlete first emerged�

___ years old when first engaged in the regular training of a team

___ years old when decision was made to become an elite athlete

___ years old when all available leisure time began being spent on your athletic training

___ years old for first “off season” training camp

___ years old when you first moved (relocated) to attend regular training for hockey

___ years old when you first established a close and extended relation with a coach

___ years old when you think that you will reach (or have already reached) your maximum potential in your sports career

___ years old when you think that you will retire from competitive hockey Sports specific milestones�

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___ years old when you first started playing (not in an organized league)

___ years old when first played in an organized league

___ years old when first began sport specific training regularly�

___ years old when first began non-sport specific training (aerobic, strength, etc) regularly

<Interviewer produces Developmental Chart for Maturation and Performance in Hockey - Chart 3

I would like to summarize the information on the development of your performance in hockey. Some of it is related to the information that you gave earlier on your level of competition. Let’s start with the first stage of engagement and proceed by column.

You were X years old when you first got involved in training for hockey. For each age please indicate the level at which you competed.

<Fill in level >

For each age please provide the age of the peers you trained and competed with.

<Fill in Age of Peers>

For each year, can you provide the highest accomplishment your team achieved as well as any personal accomplishments you may have attained?

We need to come up with individual and team performance measure that would give us an indication of your performance at each age.

<Fill in Team and Individual Accomplishment>

When looking back I would like you to tell me a little bit about your height. Were you average in comparison to the population? If not, how much taller or shorter were you? Are there any significant changes in height that occurred during your development?

<Fill in height >

When people engage in training, their concentration and physical effort differs from time to time and from day to day. Can you recall when your level of intensity was maximal? Can you briefly describe that and what it felt like? I want you to think of that as 100%. At the opposite extreme one can engage in training and take it easy. I want you to think of that as 0%. I now would like you to go through each stage and rate the average or typical level of intensity for your training. I would like you to give a rating for every stage that you engaged in regular training. You will compare the intensity of that stage to the time in which you demonstrated 100% maximal effort.

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<Fill in Intensity >

Whether individuals do or do not take advantage of coaches’ instructions and training resources, the availability of such resources for instruction and training might limit the speed and quality of development of highly motivated individuals. Disregard for the moment whether you used all or even most of the available resources for your development and rate for each stage the quality of training resources that would have been accessible to a successful and motivated hockey player in the same environment. Use a rating of 100% to correspond to the best possible environment in the world for the development of world-class hockey players. A rating of 0% indicates a complete lack of resources. Consider that these environments may differ significantly as a function of the age of the athlete so identify the best environment for each age or age-group as your reference point for your ratings. Resources can be defined as the money that was put forth by parents, self or team, the quality of the training facilities and equipment and quality of coaching and social support.

<Fill in Quality of Training Resources Column>

Have you ever sustained an injury that had an adverse affect on your activity involvement? If so, please describe this injury. Have you suffered from more than one injury? If so, please describe these. How were these injuries incurred? Approximately how long did you miss completely for each injury (Time/games/percentage of season)? Approximately how long did the injury affect your performance, whether or not you missed games/practices (time/percentage of season)?

<Fill in Injury Column>

For each of the stages listed in the chart can you provide the number of hours per week and number of months per year that you were involved in hockey? This includes competitive events and specific training activities for your sport (organized training, competitions, self-initiated training, and individualized instruction).

<Fill in Hours per Week and Months per Year>

I would like to take a moment and look at the charts we have just developed together. I would like you to regroup the years for which your training remained consistent and identify specific years at which your training has changed in terms of quality and quantity. Please do not force any categorization; regroup years only if it makes sense to you. This will allow identification of major periods in your development as a hockey player. These periods will be used as a frame of reference for the rest of the interview.

<Athlete categorizes years into stages>

3. Development of Relevant Practice Activities in Main Sport

The following section includes sections for: the related practice activities you engaged in, the number of hours spent practicing per week, the intensity of practice, and the number of months per year you were training for each of the relevant practice activities. This will

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be done for each of the stages you previously identified.

<Interviewer produces Chart 4 - Development of Practice -

<Interviewer completes the age and stage columns in accordance to information given in chart 3>

Please list all of the activities related to hockey, and other sports that you participated in during each of the developmental stages. The activities to be listed should encompass both competitive season and off-season.

<Fill in Activities>

Now that you have listed all of the activities you participated in at that stage, we would like you, if possible, to categorize these activities in accordance to the following list.

____ 1. Indirect involvement: e.g. going to watch games, watching TV/video coverage of your sport, reading books about the sport

___ 2. Organized games with rules supervised by self and peers: e.g. road hockey with friends��

___ 3. Organized games supervised by coach(es) or adult(s): coach structuring game activities in practice

___ 4. Organized training in-group supervised by coach(es) or adult(s): e.g. team practice that focused on techniques and strategies

___ 5. Individualized instruction: e.g. private lessons, individualized work with a coach

___ 6. Self-initiated training: e.g. shooting or stick-handling, unsupervised off-ice training

___ 7. Organized competition in-group supervised by adult(s): e.g. league games

___ 8. Started non-hockey activities to enhance performance in your sport (working out, yoga, etc)

<Fill in Category>

<If they have not talked about all of the categories then the following questions should be asked about the categories they did not mention. These should then be added to the chart>

♦ Is there any time that you were indirectly involved in your sport? If so, what were these activities? �

♦ Is there any time that you participated in organized activities that were supervised

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only by self or peers? If so, what were these activities? �

♦ Is there any time when you participated in events supervised by a coach or adult? If so, what were these activities? �

♦ Is there any time that you participated in organized training in a group supervised by a coach or adult? If so, what were these activities? �

♦ Is there any time that you participated in individualized instruction with a coach? Is so, what were the activities? �

♦ Is there any time when you participated in self-initiated training? If so, what were these activities? �

Please indicate how many hours per week you were participating in each particular activity.

<Fill in # hrs per week>

Please indicate the number of months per year that you engaged in each activity.

<Fill in Months per year>

When people engage in training, their physical effort differs from time to time and from day to day. Can you recall being engaged in an activity when your level of physical effort was maximal? Can you briefly describe that and what it felt like? I want you to think of that as 100%. At the opposite extreme one can engage in training activities and take it easy. I want you to think of that as 0%. I now would like you to go through each activity and rate the average or typical level of physical effort. I would like you to give a rating for every activity. You will compare the physical effort of that activity to the activity in which you demonstrated 100% maximal effort.

<Fill in Physical effort>

When people engage in training, their mental concentration differs from time to time and from day to day. Can you recall being engaged in an activity when your level of concentration was maximal? Can you briefly describe that and what it felt like? I want you to think of that as 100%. At the opposite extreme one can engage in training activities and not being mentally focused. I want you to think of that as 0%. I now would like you to go through each activity and rate the average or typical level of concentration. I would like you to give a rating for every activity. You will compare the concentration of that activity to the activity in which you demonstrated 100% concentration.

<Fill in Mental concentration>

Next, I would like you to think of types of activities that were the most fun at the corresponding ages, such as watching your favorite program on TV or playing games with friends. We can all recall participating in activities that were so fun we did not want

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them to end. Please describe the activity that you would consider most fun for each of the stages you previously identified. These activities do not have to be sport related. Let us set each of these activities as 100% fun. Using these activities as references now go back and give a separate rating for fun in each activity within the stages. Compare each training activity to the activity that you identified as being 100% fun at that stage.

<Fill in Fun>

● At what age did you consider hockey your primary sport? �

● At what age did you quit other activities to play hockey? �

● At what age did you start training for hockey for more than 8 months a year? �

● Have you ever been told that you were not allowed or permitted to play other sports during the season? During the off season? �If so, was there any reasoning given, and who made the request (coach/parent)? �

● Was there more than one city/association? If so, when and why did you change? Did you ever attend a hockey academy? �

● Were you drafted to the OHL? If so, which round? Were you drafted to the NHL? Have you signed an NHL contract? �

This completes the interview procedure. Thank you for your time and patience in filling out each of the charts

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Appendix B Chart B1

Activities and Ages Note start & stop age (+ indicates continuing involvement) and periods of temporary stoppage

Activity 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 +

1

2

3

4

5

6

7

8

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Chart B2 Hours per week and months per year involved in other activities

Activity 6 (Column for each age 7-20)

21 +

Peak Off Peak Peak Off Peak Peak Off Peak

1 Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk

Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr

2 Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk

Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr

3 Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk

Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr

4 Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk

Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr

5 Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk

Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr

6 Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk

Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr

7 Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk

Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr

8 Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk Hr/Wk

Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr Mth/Yr

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Chart B3a: Developmental chart for maturation and performance in main sport

Stage ID

Year Age Group/ Level

Age of Peers

Personal Accomplishments

Team Accomplishments

Height ...

Relative Difference Change

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Chart B3b: Developmental chart for maturation and performance in main sport

Stage ID

... Intensity Quality of resources Injury Hrs/Wk Sport

Description Mechanism Time Away Time Affected

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Chart B4: Development of Relevant Practice Activities

Age Stage Activity Category Peak Hr/Wk

Peak Mths/Yr

Off Peak Hr/Wk

Off Peak Mths/Yr

Physical Effort

Mental Concentration

Fun

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Chart B5: At what age did you consider hockey your main sport?

At what age did you quit other activities to play hockey?

At what age did you start training for hockey for more than 8 months a year?

Have you ever been told that you were not allowed or permitted to play other sports during the season?

During the off season?

If so, was there any reasoning given, and who made the request (coach/parent)?

Have you ever attended a hockey academy?

Have you played for more than one city/association?

If so, when and why did you change?

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Appendix C

Expanded discussion for deliberate practice

The decline in the number of hours spent participating in unstructured activities during

childhood has been noted in previous research (e.g., Barreiro & Howard, 2017; Wojtys,

2013). This, despite popular media sources discussing the many benefits of childhood

play, and the promotion of play being found in the literature as early as 1810 (e.g., Berry

et al., 2008; Good Morning America, 2017; Gregory, 2017; Vealey & Chase, 2016;

Weissensteiner et al., 2009). Some of these benefits include fostering physical literacy

(Whitehead, 2001), encouraging creativity (Côté et al., 2009; Imtiaz, Hancock, & Côté,

2016; UFSPRC, 2013), and building ‘metaskills’ (Côté & Erickson, 2015). ‘Metaskills’

are considered attributes that allow athletes to positively adjust to new and unpredictable

situations (Côté & Erickson, 2015). The decline in athletes’ spontaneous play was

explained by parents of elite athletes as being a consequence of the time commitments

required to participate in organized sports, such as travel, competitions, and rigid

schedules (Kirk et al., 1997).

The reduction of sport-specific play activities as noted in this study, may

negatively effect some elements of development that assist with improving performance.

Roca, Williams, and Ford (2012) found that 21.8% of the variance found on a soccer

decision-making task was accounted for by participants’ accumulated childhood soccer-

specific play hours. Moreover, Ford, Ward, Hodges, and Williams (2009) found that

soccer-specific play was the only developmental activity that differentiated professionals

from less successful players. In a country known for its creative soccer stars, Brazil has

devoted a word, ‘pelada,’ to describe unstructured soccer activities (Block, 2013;

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Goldblatt, 2014). These free-play soccer activities are believed to help develop creativity

and develop sport-specific skills (Vealey & Chase, 2016). A possible mechanism for

developing this creativity is that free-play typically constrains the space available, forcing

sport-specific decisions to be made faster and more often (Chow et al., 2016).

As defined by Smith, Takhvar, Gore, and Vollstedt (1986), there are five criteria

for an activity to qualify as play. These criteria include that the activity (a) is intrinsically

motivated and not led by adults, (b) is pleasurable and not serious, (c) has the purpose of

being fun, (d) does not have the goal of attaining a specific result, and (e) has variation or

flexibility in the rules. This type of peer-led free-play is alleged to foster intrinsic

motivation in the activity (Baker & Cobley, 2013; Güllich, Kovar, Zart, & Reimann,

2017). Although this view is not universally accepted (e.g., Hendry, Crocker, & Hodges,

2014), limiting adult involvement during childhood does appear to encourage positive

development (Csikszentmihalyi & Larson, 1984; United States Soccer Federation, 2016).

Furthermore, Csikszentmihalyi and Larson (1984) found that intrinsic motivation and

adult control are inversely correlated. However, regardless of the mechanism, the

evidence supports that elite athletes engage in relatively higher amounts of sport-specific

play than less successful athletes (Coutinho et al., 2016; Ford et al., 2009; Jordet, 2015;

Roca et al., 2012). Jordet (2015) lists ‘passionately playing’ as one of five traits of expert

athletes, along with persistently pursuing performance, self-regulating their learning,

coping with change, and coping with competitive pressure. Although the amount of play

is related to these other traits, more evidence is needed to show causation.

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Appendix D

Comparison of results to accepted developmental models

Comparing the developmental pathway shown by the participants of this study to other

accepted models reveals both commonalities and differences. The Hockey Canada (2013)

LTAD, which is an adaptation of the Bayli et al. (2013) model, states that from infancy to

4 years of age athletes should focus on physical literacy and skating skills. While this

study did not specifically account for the hours accumulated at these ages, participants

did start playing hockey as young as 2 years old, by 3 years old most had initiated hockey

activities, and by 4 years old, most were regularly involved in hockey. From the ages of 5

to 6, participation in the ‘initiation program’ is recommended to acquire basic skating and

puck skills, as well as fun team competitions. Again, specific activities were not

calculated for this stage in the current study; however, by 5 years old, most participants

had joined a team for supervised training and organized hockey games against the

recommendation of the Hockey Canada (2013) LTAD model.

The next three stages of Hockey Canada’s LTAD model (Hockey Canada, 2013)

coincide with Côté’s (1999) DMSP’s sampling stage when playing various sports is

recommended, and should focus on improvement rather than competitive success

(MacDonald, Côté, & Kirk, 2007). Firstly, the ‘fundamentals 2’ stage of Hockey

Canada’s (2013) LTAD model encompasses 7 and 8 year olds, and suggests that players

take part in two practices each week focusing on physical literacy and basic skills. At 8

years old, participants of the current study had a median of 168 hours of hockey-specific

deliberate practice, or over three hours each week for 52 weeks. Similarly, participants

engaged in over three hours per week of sports other than hockey. Collectively, at 8 years

154

old, this sample of OHL players appear to adhere to the DMSP and practice more than

the Hockey Canada (2013) LTAD model recommends.

Secondly, Hockey Canada’s (2013) ‘learn to play’ stage includes 9 and 10 year

olds, and emphasizes the importance of skill development, and the transfer of those skills

to games. At this age, the median number of hours of hockey-specific deliberate practice

collected by participants was lower than the amount of hours spent playing sports other

than hockey. While the amount of time spent competing in organized games begins to

increase, and hockey-specific deliberate play remains consistent from the previous stage,

but still approximately 30% higher than the time spent participating in games. This shows

concepts that comply with both Hockey Canada’s (2013) LTAD and the DMSP (Côté,

1999).

The final stage of the Hockey Canada (2013) LTAD model that corresponds to

the sampling stage of the DMSP (Côté, 1999) is titled ‘learn to train,’ and involves 11

and 12 year olds. The LTAD model states that this stage is the most important period for

hockey skill development, adding that a balance of organized games and practices are

recommended. Compared to the previous stage, participants engaged in 29% less hockey-

specific deliberate play, 39% more hockey-specific deliberate practice, 33% more

organized games, and 30% more sports other than hockey. Showing an increased

commitment to hockey, participants also spent considerable time playing sports other

than hockey, which would appear to concur both the Hockey Canada (2013) LTAD

model, and the DMSP (Côté, 1999). However, the increase in hockey-specific deliberate

practice and organized games, with a reduction in the amount of time spent in hockey-

155

specific deliberate play, indicates that there are aspects of specialization occurring earlier

than suggested by the DMSP (Côté, 1999).

The Hockey Canada (2013) LTAD model suggests that athletes start increasing

hockey specific activities at 12 years old, which is the age at which participants of this

study were found to be moderately specialized. This corresponds with the ‘peewee’ age

division that was determined by Philippon et al. (2013) to be the age at which hockey

players show a significantly higher risk for FAI. In the current study, participants

reported the first overuse injury at the age of 12 years old, and the first incidence of an

injury affecting performance after the initial injury had resolved. However, 12 years old

was also the age at which participants engaged in the highest number of hours in ‘other

sports.’ The interaction of the increase in hockey activities and other sports may have

interfered with recovery (Goodger, Lavallee, Gorely, & Harwood, 2006; Kutz & Secrest,

2009; Lemyre, Roberts, & Stray-Gundersen, 2007).

At 13 years old, athletes enter the ‘specializing’ stage of the DMSP (Côté, 1999)

and are expected to show an increased commitment to their sport, with the opposite

approach to sports other than hockey from the ages of 13 to 15. The DMSP’s (Côté,

1999) specializing years are overlapped by Hockey Canada’s (2013) ‘train to train’ stage,

which incorporates the ages of 12 to 16, and focuses on building a physiological base for

high-level hockey, and tactical knowledge of the sport. By 14 years old, hockey-specific

deliberate play has been all but eliminated from the lifestyles of the athletes in the current

study, and sports other than hockey decreased 44% from the previous stage (12 years

old). Furthermore, the median time spent in hockey-specific deliberate practice increased

56%, and the commitment to organized games increased 19% from when the participants

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were 12 years old. With the results showing that the priorities at 14 years old appear to be

solely focused on hockey, it can be argued that these participants have entered the

DMSP’s (Côté, 1999) ‘investment’ stage.

The investment stage of the DMSP (Côté, 1999) begins at 16 years of age and

lasts until the athlete has retired from competitive sport. However, this stage appears to

be entered into as young as 14 years old in the current sample. Hockey Canada’s (2013)

LTAD model states that the ‘train to compete’ stage ranges from 16 to 17 years old, the

‘train to win’ stage includes 18 to 20 year olds, and the ‘excel’ stage involves athletes

that are 21 years and older. These stages complete a high-performance pathway, focusing

on optimizing support staff, physiological aspects, and tactical capabilities. According to

the Hockey Canada LTAD, international competition should start at 17 years old;

however, of the five participants that competed internationally, they had all done so by 16

years of age. Moreover, for each of these three stages physical fitness is emphasized,

while there is apparent prioritization of ‘off-ice workouts’ as early as 14 years old.

Additionally, by 16 all players have started working out off-ice year-round, rate their

intensity and available resources as 100%, and have the expectation to condition

themselves to maximize their on-ice performance. Therefore, the results of the current

study show that these stages occur earlier than is proposed by the current LTAD model

(Hockey Canada, 2013).

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Appendix E

Expanded discussion for developmental milestones and effort, fun, and concentration

When examining Danish team sports (i.e., hockey), Moesch, Hauge, Wikman, & Elbe

(2013) found that players who started playing at a significantly older age were more

successful than players who started playing younger, which is consistent with other

research findings noting that start age is negatively correlated with performance (Gobet &

Campitelli, 2007; Howard, 2012). Similarly, some studies have found that the average

age of initial participation for Olympic athletes’ from a variety of sports was older than

10 years (Barreiros et al., 2013; Vaeyens, Güllich, Warr, & Philippaerts, 2009; Huxley et

al., 2017). Some authors have suggested that the age at which an athlete starts an activity

may account for the variance in performance that practice does not explain (MacNamara,

Hambrick, & Oswald, 2014).

Previous research found that players started competing at 5 years old for baseball

and soccer in Canada (Valeriote & Hansen, 1988) and elite soccer in England and

Belgium (Ford et al., 2009; Helsen et al., 1998). Until 9 years old, Hockey Canada’s

LTAD) model recommends introducing fun competitions in a team environment, but

makes no mention of organized games or leagues (Hockey Canada, 2013). With

development in mind, early engagement may be important, as the window for acquiring

new complex motor skills appears to close considerably at 10 years of age (Bruce,

Farrow, & Raynor, 2013; MacNamara et al., 2014; Vealey & Chase, 2016). Therefore, it

has been suggested that athletes in complex sports (i.e., hockey) should focus on skill

acquisition, and not start competition until 10 years old because early competitive success

may be attributed to biological factors such as size and physical maturity (Barreiros et al.,

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2014; Ford et al., 2009; Leite & Sampaio, 2010; Moesch et al., 2011; Vealey & Chase,

2016).

By starting competition at 5 years old, it is understandable that most of the

participants also began supervised training at this age due to team practices with an adult

coach. Unsupervised training, such as practicing hockey skills, was initiated by most

participants by 8 years old, which was the same age that the majority first thought of

becoming an elite athlete, and began regular team training. These milestones occur earlier

with the hockey players in the current study than Portuguese and English national level

athletes (Barreiros et al., 2013; Bridge & Toms, 2013), but at a similar age as high-

performing and recreational soccer players (Roca et al., 2012). The participants in the

current study had a wide range of answers for these milestones, with the youngest

athletes having started supervised training and the idea of becoming an elite athlete at 3

years old. Conversely, the oldest age at which participants achieved these milestones

were 10 and 14, respectively. Having the idea to become an elite or professional athlete at

such an early age may reflect the goals of the parents, as it has been shown that children

may take on the parental aspirations (Callender, 2010). These goals then appear to stay

with the athletes as 78% of division I NCAA male hockey players believe that it is

somewhat likely that they will play in professional leagues or in the Olympics (Paskus &

Bell, 2016). By 8 years old, participants already reported a median of 480 hours per year

of combined hockey activity, but when the time spent in each activity (i.e., workouts,

deliberate play, and deliberate practice) was calculated, the median reached 612 hours. If

broken down over 52 weeks, participants of the current study spent almost 12 hours per

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week participating in hockey-specific activities at 8 years old, compared to approximately

3 hours in sports other than hockey.

Most of the participants had been selected for a competitive team by the age of

nine, and by the time they were 10 years old, the majority had been recognized as one of

the best players on their respective teams. Ten years old also marks the first age that all

the players were competing against their own age group, as opposed to players that were

older than they were. Additionally, at 10 years old, most of the players were playing at

the ‘AAA’ level (i.e., the highest level of minor hockey available), and by 11, all but one

of the participants were playing AAA. With many of the participants advancing to the

AAA level, the steep increase of deliberate practice hours between 9 and 10 years old

may be explained due to the increased commitment required (Sugimoto, Stracciolini,

Dawkins, Meehan, & Micheli, 2017). Likewise, the accumulated hours of deliberate play

reported decrease sharply at the same time, possibly showing that between 9 and 10

participants went through a transition as they began to commit to becoming a hockey

player. Children of this age have the belief that if they try harder, they will accomplish

more, and do not understand that there may be a limit to their capabilities (MacDonald et

al., 2007). This may explain the increase of average intensity rating from 55% at 9 years

old, to over 61% at 10 years old from the participants in the current study, which is the

largest percentage increase until participants began competing in the OHL.

In a qualitative analysis of elite soccer in England, Horrocks et al. (2016), labeled

the ages of 8 to 11 as the ‘romance’ stage, when deliberate play and athletic curiosity are

explored. Consistent with the ‘sampling’ stage of the DMSP, deliberate play is

encouraged during this stage, and considered essential to progressing towards becoming

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an elite soccer player. Following the ‘romance’ stage, athletes in the Horrocks et al.’s

(2016) study enter the ‘elementary educational’ stage. This stage spans from the ages of

11 to 15, and focuses on introducing structure and tactical education. Horrocks et al.’s

‘elementary education’ stage differs slightly from the DMSP’s ‘specializing’ stage, which

takes place between 12 to 15 years old. The current study also appears to show transition

to a higher commitment level around 12 years of age, which is consistent with other

studies of successful athletes (Baker & Côté, 2003; Huxley et al., 2017; Jayanthi &

Gugas, 2017). At 12 years old, most participants in the current study had initiated regular

off-ice hockey-specific training, decided to become an elite athlete, and developed a

relationship with a coach. Wall and Côté (2007) found that AAA players who drop out of

hockey initiated off-ice training at a younger age. Of the eight active AAA players in that

study, only two had started off-ice training by 13 years of age, but all four players who

had dropped out of the sport had started by that age. Additionally, 12 years of age marked

the first year at which all of the participants of the current study played at the AAA level,

and they then remained at that level until they started playing in the OHL. Being selected

for a AAA team may act as a motivating challenge initially, but remaining at the level

may not pose the same adversity as coaches tend to re-select players that were previously

chosen for competitive teams (Carlson, 1993; Collins, MacNamara, & McCarthy, 2016,).

Furthermore, at 12 years old, participants reported their first overuse injuries,

acknowledged sustaining multiple injuries, admitted the first incidence of an injury

affecting performance after the initial injury had resolved, and deliberate play decreased

substantially. Twelve years of age corresponds with the ‘peewee’ age group of minor

hockey, which is the age at which Stull et al. (2011) warned hockey players could start

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being affected by FAI. However, the reported injuries could also be a result of the

interaction between the increase of both hockey activities and other sports which may

interfere with proper recovery (Goodger et al., 2006; Kutz & Secrest, 2009; Lemyre et

al., 2007).

By 13 years of age, the majority of the participants in the current study had started

sport-specific training, participated in an off-season training camp, and total deliberate

practice increased almost 20% from the previous year. In addition, at 13 years old,

participants’ median intensity rating was 75%, and the calculated hours spent in hockey

activities was approximately 16 hours per week, if spread over the entire year. This

calculated total of all the individual activities is more than double the number of hours

reported when asked to summarize the time spent participating in hockey, possibly

showing that participants underestimate their commitment to their sport. The increase in

commitment at this age coincides with Helsen et al.’s (1998) study of team sports that

showed a similar rise in commitment after 8 years. Finally, by 14, most of the participants

of the current study answered that all of their leisure time was dedicated to training for

hockey, which is shown by participation in other sports decreasing approximately 30% at

this age. This is also the first instance of a participant reporting an injury that currently

affects his performance, despite the injury occurring several years earlier.

While it is important that adolescents participate in sports, specialization before

13 years old has not been recommended (American Academy of Pediatrics, 2000). It is

also advised that athletes take a one or two days off every week, and two months off from

competitive sports every year to physically and emotionally recover from the stressors

involved (Brenner, 2007; Callender, 2010; Côté & Hancock, 2016; Stambulova,

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Alfermann, Statler, & Côté, 2009). With the median number of deliberate practice hours

reportedly increasing over 50% from 12 to 13, but the number of hours spent

participating in other sports not decreasing substantially until the age of 14, the

participants in the current study would have little time away from sport. This is consistent

with results from a NCAA study which stated that 55% of Division I male hockey players

and 61% of Division III players have specialized by 12 years old (Paskus & Bell, 2015).

These milestones are also in line with the Hockey Canada LTAD stage of ‘train to train,’

where it is suggested that players decrease other sports played, and increase structured

training activities (Hockey Canada, 2013). The time demands required to meet the

expectations of these commitments may negatively affect the athlete’s ability to properly

recover, study effectively, or have healthy sleep habits, all of which could lead to the

injury trends noticed among the current sample of athletes (Ballard, 2017; Valovich et al.,

2011). A 1993 study of Swedish national-level tennis and hockey players noted that

injuries were rare for these elite athletes (Carlson, 1993); conversely, other research has

stated that minor injuries may introduce adversity, which is beneficial along one’s

developmental pathway (Collins et al., 2016).

Corresponding with the ‘investment stage’ of the DMSP, the final stage of

accomplished milestones along the development towards becoming an OHL player

appears to occur at 16 years old. This is when the majority of the players in the current

study relocated for hockey, which can be explained considering they would be billeted

within their OHL city at this time. It is an accepted occurrence for families that choose to

have a child commit to pursuing a career in sport, to allow the child to move away from

home at this age and fully invest their time (Diogo & Gonçalves, 2014; Wojtys, 2013).

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Seven of the 15 participants in the current study also reported that, by 16 years of age,

they were first exposed to national-level competition, while five players reported

competing internationally at this age. Injuries at this age doubled compared to any other

year during development, and the combined time away from hockey due to injury almost

tripled from when they were 15 years old. The increase in injuries is despite all the

participants having initiated non-sport specific training, which has been shown to prevent

injuries (Kushner et al., 2015; Sugimoto et al., 2017). At 16 years old, one player

competed in ‘Junior B,’ one player in ‘Junior C,’ and nine players in the OHL; all of

these players are at the appropriate age for these leagues, but could be competing against

players five years older than they are.

The age of 16 corresponds with the ‘investment stage’ of the DMSP (Côté, 1999),

and aligns with the ‘advanced educational’ stage of Horrocks et al. (2016) investigation

of elite soccer players. These authors describe the advanced educational stage as starting

at 16 years old, but ending flexibly depending on the level that the athlete attains. Some

players may end this stage at 18 years old as a part of a youth team, while others may end

at approximately 21 years of age as a potential underage international player. After this

stage, these elite soccer players may progress to ‘graduation’ to play professional, and

‘evolutionary excellence’ by excelling at the highest professional and international levels.

There are many parallels from this pathway to that of the elite hockey players in this

study. Players typically play in the OHL until they are 18 to 19 years old, unless they

show the potential to play professionally. These players are eligible leave for the NHL at

18 years old, or may continue playing in the OHL until 21 years of age before pursuing

professional careers.

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The final milestone questions that were asked as part of the interviews for the

current study pertain to future performance. First, most participants felt as if they would

reach their maximum potential as a hockey player when they were 24 years of age, but

answers ranged from 19 to 38 years, with both 20 and 28 being the most popular answers.

Research has indicated that statistically, professional hockey players attain their peak

performance between 27 and 29 years old, but for sports such as hockey, the range of

peak performance can be very large due to the physical, technical, or tactical capacities

required (Allen & Hopkins, 2015; Brander, Egan, & Yeung, 2014). Helsen et al. (1998)

found that national level soccer players reached elite status after 10 years of deliberate

practice, and peaked 15 years after they started in the sport. The last question regarding

milestones found that most participants in the current study felt they would retire by 35

years of age, which was also the most popular reply.

There is relatively little research published in regards to the effort, fun, and

concentration involved in the developmental activities of elite athletes (Imtiaz et al.,

2016; Visek et al., 2015). However, in relevant studies that are available, participants’

perceptions of having fun while engaging with their sports seems to affect persistence,

and may be crucial to later success (Baker & Horton, 2004; Côté, Baker, & Abernethy.,

2003; Coutinho et al., 2016; Diogo & Gonçalves, 2014; Duffy, Lyons, Moran,

Warrington, & MacManus, 2006). In the current study, median ratings of ‘fun’ remained

high (i.e., 90% or above) for organized games, practices, and self-initiated hockey

activities from 6 to 14 years old. However, ratings of fun dropped as low as 80% for self-

initiated activities at 15 years of age, 80% for organized games at 16, and 75% for

practices at 16. In a study of sport enjoyment, Visek et al. (2015) found playing time to

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be ‘very-to-extremely’ important to most players, coaches, and parents. With all the

participants of the current study indicating that they were acknowledged as the best

player on their team at some point at the competitive level, it can be assumed that they

were given significant playing time. Enjoying one’s sport may positively affect the

quality of practice, lead to good competition, and give a sense of accomplishment to

athletes enabling positive development (Gonçalves, Carvalho, & Light, 2011; Long &

Carless, 2010).

One factor that research has stated as negatively affecting athletes’ enjoyment

ratings was too much structure early in an athletes’ development (Gould, Tuffey, Udry, &

Loehr, 1996; Weissenteiner et al., 2009). However, in the current study, information was

only collected until they were 16 years old; therefore, the athletes may be too young to

show any negative effects of their early structure. The apparent reduction in the perceived

enjoyment of hockey at 15 and 16 years old could be a result of a decrease in playing

time due to their higher level of play (Visek et al., 2015). Participants switching teams,

and thus teammates, causing them to lose cohesiveness with players to whom they were

close, could also explain the reduction of fun (Sugimoto et al., 2017).

Another factor which has been shown to negatively impact enjoyment has been

intense training at a young age (Coutinho et al., 2016; Law, Côté, & Ericsson, 2007).

Median ‘physical effort’ ratings increased evenly throughout development for all

activities in the current study, and the lowest median rating given was 77.5% for practices

at six years old. At 12 years old, the median ‘physical effort’ rating given reached at least

90% for all activities, while fun remained at 90% or above for these activities. Attaining

the threshold of a 90% median rating of ‘physical effort’ corresponds with the drastic

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reduction of hockey-specific deliberate play hours, and increase in hockey-specific

deliberate practice undertaken by participants. The increase in ratings for ‘physical effort’

may be related to all players playing at the AAA level for the first time at 12 years old,

and thus, intense training is attempting to improve performance (Côté et al., 2009). This

supports Diogo and Gonçalves’ (2014) findings in elite soccer players, which credited the

competitive climate for higher perceived ratings of physical effort. Additionally, the

concurrently high ratings for ‘fun’ and ‘physical effort’ correspond with Visek et al.

(2015), who found that players rated ‘trying hard’ as the second most important

dimension of fun. Imtiaz et al. (2016) also found that high physical effort resulted from

enjoyable activities that required high levels of concentration and that were challenging

for them. The combination of these attributes can promote positive development for the

children involved (Imtiaz et al., 2016; Larson, 2000).

To foster positive development from sport, it has been shown that elite athletes

need to have high levels of motivation (Aicinena, 1992; Baker & Horton, 2004;

Csikszentmihalyi & Larson, 1984; Duffy et al., 2006; Iso-Ahola, 1980; Lemyre et al.,

2007). Due to the high level of enjoyment reported by the participants of the current

study, it would appear they were highly motivated to engage in hockey activities.

Previous research has indicated that self-determined, or intrinsic, motivation has been

associated with elite athletes; conversely, higher levels of extrinsic motivation has been

related to lower-level athletes (Lemyre et al. 2007). This intrinsic motivation may be a

cause of the high levels of effort, concentration, and amount of time spent in hockey

displayed by the participants of the current study (Imtiaz et al., 2016; Schmidt &

Wrisberg, 2000). Intrinsic motivation has also been found as a factor in elevated

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concentration levels in sports due to the specific challenges within the sport (Larson,

2000). While ratings of ‘mental concentration’ were typically lower than ‘fun,’ and

‘physical effort’ by the participants of the current study, by the time the participants in

the current study were 10 years of age, all activities were rated as 80% or above.

The highest levels of intrinsic motivation are noted in activities that are peer-led,

and lowest in activities that are adult-driven (Csikszentmihalyi & Larson, 1984).

However, a study of recreational soccer players revealed that only effort and

concentration, not enjoyment, were affected by the difference of adult versus peer-led

activities; with effort and concentration being higher for activities that involved adult

instruction (Imtiaz et al., 2016). Likewise, having fun while practicing is important for

players to maintain motivation over their careers (Abbott & Collins, 2004). The elite

status of the participants of this study indicates that their motivations were coupled with

physical ability and enjoyment for their sport, as these characteristics are dependent on

each other to create a high performing athlete (Baker & Horton, 2004; Vealey & Chase,

2016).

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Appendix F

Expanded discussion for deliberate practice

The concept of deliberate practice improving performance is widely accepted by

the general public (Baker & Young, 2014), and is even perceived by athletes to be

instrumental in success (Horrocks et al., 2016). As found with this study, the general

trend of participants’ increasing amounts of deliberate practice at the expense of play

activities is consistent with previous research (Côté, Baker, & Abernethy, 2007;

Hutterman, Memmert, & Baker, 2014). However, while some studies find that higher

amounts of accumulated sport-specific practice results in better performance (e.g., Berry

et al., 2008; Coutino et al., 2016), others find that expert status is not explained by

accumulated practice hours alone (e.g., Moesch et al., 2013; Phillips, Davids, Renshaw,

& Portus, 2010). Some elements of performance that that may be practiced, as they are

essential for sport expertise, are technical skills, physical capacities, cognitive

(tactical/perceptual) strategies, and psychological abilities (Deshaies, Pargman, &

Thiffault, 1979; Vealey & Chase, 2016).

Research has indicated that between 11% and 13% of decision-making and

anticipatory skills are explained by deliberate practice activities (e.g., Roca et al., 2012;

Weissensteiner, Abernethy, Farrow, & Müller, 2008). According to MacNamara et al.

(2014), deliberate practice accounts for 18% of the overall variance in sport performance.

However, for less-predictable sports (e.g., hockey compared to athletics), the variance

explained by practice may be even lower. Moesch et al. (2013) found that team sport

athletes with fewer practice hours at age 12, but more at age 15, had more success than

did their peers. Similarly, Roca et al. (2012) found that the practice hours accrued

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between 6 and 12 years of age did not significantly affect scores on a perceptual-

cognitive task. However, practice hours accumulated between the ages of 13 to 18 years

proved to be significant (Roca et al., 2012).

The extent to which deliberate practice may be effective could be explained by

the quality of the practice that is performed. The quality of deliberate practice may affect

performance negatively if the focus is too narrow (Davids, Button, & Bennett, 2008), if

the athlete is not sufficiently motivated to endure intense training (Berry et al., 2008;

MacDonald et al., 2007; Stambulva, 1994), or if there are inadequate resources and effort

levels (Baker & Young, 2014). The focus of practice may be too narrow if a skill set is

rehearsed with little variation, and without allowing for different configurations (Davids,

Glazier, Araújo, & Bartlett, 2003). With respect to training motivation, one study of

junior hockey players found that the fear of failure was the main motivator for junior

hockey players (Deshaies et al., 1979). In regards to resources, each participant of this

study listed an individual coach that focused on skill, skating, or goaltending, which is

consistent with Soberlak and Côté (2003), and is critical for the development of elite

athletes (MacDonald et al., 2007). Another crucial element of an elite player’s resources

is a supporting family, to provide emotional, financial, and logistical (e.g., transportation,

planning) support (Baker & Côté, 2003; Knoetze-Raper, Myburgh, & Poggenpoel, 2016;

Robertson-Wilson, 2002). Moreover, to optimize quality deliberate practice a ‘deliberate

environment’ is essential. Elements such as team meetings, video sessions, reflection,

proper diet, are examples of resources that are important to create this deliberate

environment (Ford, Coughlan, Hodges, & Williams, 2015).

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MacNamara et al. (2014) concluded that, while practice is unquestionably

important to performance, it may not be as important as implied by Ericsson et al. (1993).

The MacNamara et al. (2014) findings are consistent with Güllich et al. (2017), as they

found that practice and training alone did not account for a significant difference in

performance. Güllich et al. (2017) found that it was the interaction of unstructured play,

training in other sports, and deliberate practice that may explain up to 65% of

performance variance. Roca et al. (2012) also found that the interaction of sport-specific

practice and play significantly increased the variation explained in an anticipation and

decision-making task. MacNamara et al. (2014) found that retrospective recall

methodology (employed by Côté et al., 2005), and performance measured by group

membership (i.e., playing in the OHL) may artificially increase the variance explained by

deliberate practice. When considering these factors, the authors state that the performance

variance explained by deliberate practice may be as low as 5%.

While the interview script used in this study allowed for the distinction between

deliberate play and deliberate practice, the difference between the two activities may not

be easily recognized. Côté et al. (2013) suggest that a continuum exists between the two

forms of developmental activities. Additional categories that may be used to classify

activities are ‘play practice,’ and ‘spontaneous practice.’ Play practice is prescribed by

adults and is intended to increase performance by using activities that encourage fun.

Spontaneous practice is completed in the athletes’ free time, much like deliberate play,

but there is structure and the intent is to increase sport-specific abilities (Berry et al.,

2008; Vealey & Chase, 2016). Furthermore, Ford et al. (2015) suggest that there are four

types of practice, which include ‘deliberate practice,’ ‘maintenance practice,’ ‘play

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practice,’ and ‘competition.’ This definition of deliberate practice is similar in that the

goal is improvement and the activity is not always enjoyable. Maintenance practice has

the goal of maintaining skill, and may or may not be enjoyable. Play practice is viewed as

enjoyable while athletes attempt to improve their performance. Finally, competition is

often enjoyable, and the focus is on winning the activity.

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Appendix G

Expanded discussion for other sports

Consistent with this research, other studies have shown that expert athletes in

other sports tend to participate in several sports until the age of 12, at which point they

narrow their focus (i.e., Barreiros et al., 2013; Post et al., 2017; Vealey & Chase, 2016).

Likewise, athletes who played a variety of sports at the age of 12 have better results in

several measures of athletic performance (Fransen et al., 2012). Conversely, Ford et al.

(2009) found no difference in the number of hours spent playing other sports between

elite and non-elite soccer players. While the authors did not find an advantage in playing

different sports early in one’s development, they also found that doing so did not hinder

the chances of attaining elite status.

In addition to collecting the hours of sports played other than hockey, the type and

classification of sport was also recorded. Soccer was the most popular sport played other

than hockey among the participants in the current study, with all 15 participating at some

point during their developmental years. Basketball, baseball, golf, and volleyball were the

second most popular sports reported, as 12 participants engaged in these activities.

Athletics had nine participants and was the most popular individual sport. Roca et al.

(2012) found athletics and basketball to be the most common alternative sports played by

high-performing soccer players, while judo and cricket were the most popular for

recreational players.

Other studies have also found that the number of sports played in childhood did

not differentiate between skilled and less-skilled players (Diogo & Gonçalves, 2014;

Roca et al., 2012, Weissensteiner et al., 2008). Additionally, other studies have shown

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that early diversification is related to success later in life for athletes in hockey (Carlson,

1993; Robertson-Wilson, 2002; Soberlak & Côté, 2003), soccer (Bridge & Toms, 2013;

Güllich et al., 2017), field hockey (Bridge & Toms, 2013), golf (Hayman, Polman,

Taylor, Hemmings, & Borkoles, 2011), athletics (Bridge & Toms, 2013; Güllich &

Emrich, 2014), netball (Bridge & Toms, 2013; Bruce et al., 2013), rugby (Bridge &

Toms, 2013), and swimming (Bridge & Toms, 2013).

Playing these different sports during childhood may have helped the participants

choose hockey by functionally matching to the sport, or choosing the sport in which they

show the most talent (Güllich & Emrich, 2014). Quitting other sports to focus on hockey

younger than previous research displayed may increase the risk for injuries, but children

should be encouraged to participate in other sports only as long as they feel like doing so

(Carlson, 1993; Valovich et al., 2011). Furthermore, evidence exists suggesting that the

more sports participated in before specialization, the fewer hours of practice needed to

attain expert status (Leite, Santos, Sampaio, & Gomez, 2013).

Research has shown that different contexts allow for athletes to adapt to new

contexts due to the transfer of pattern recognition, and perceptual, cognitive, and motor

abilities (Causer & Ford, 2014; Giblin, Collins, & Button, 2014; Santos et al., 2017).

Smeeton et al. (2004) studied pattern recognition transfer across soccer, field hockey, and

volleyball players, finding there was some transfer of perceptual skills for the invasion

sports (e.g., soccer and field hockey). Similarly, Abernethy, Baker, and Côté (2005)

found that expert netball, field hockey and basketball players recalled sport-specific

patterns better the non-experts. The experts also recalled the patterns of other sports more

consistently, supporting the transfer of pattern recognition (Abernethy et al., 2005). In

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contrast to pattern recognition, Müller, McLaren, Appleby, and Rosalie (2015) showed

that baseball and rugby athletes were not capable of transferring anticipation skills from

one sport to the other.

Appropriate decision-making is an essential cognitive skill affecting performance

in many sports, and appears to be a learned ability (Causer & Ford, 2014; Roca &

Williams, 2017). Time and space constraints force athletes to integrate information from

the current situation and relate them to past experiences (Roca & Williams, 2017).

Several theories exist to explain transferring the ability to make appropriate decisions.

The ‘identical elements theory’ relies on similar elements between tasks to allow transfer

(Thorndike & Woodworth, 1901). The ‘transfer-appropriate processing’ theory states that

the mental and cognitive elements of the tasks need to be similar (Lee, 1988). Finally, the

‘specificity of learning’ hypothesis argues that previous experience does not transfer to a

different task (Proteau, 1992). This hypothesis states that a performer cannot acquire

attributes in one domain, and make them relevant in another (Proteau, 1992; Roca &

Williams, 2017)

When considering decision-making across sports, Allard and Starkes (1992)

found that, while varsity hockey and basketball players could recall their own sport with

considerable consistency, they could also recognize the other sports at a rate well above

chance (Smeeton et al., 2004). Additionally, Causer and Ford (2014) found that response

accuracy on a soccer task was similar between soccer players and athletes from other

invasion sports. Although decision-making transfer occurred across sports, specificity of

learning was also supported due to athletes from invasion sports scoring better than

athletes did from unrelated sports (Causer & Ford, 2014). These results are supported by

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Roca and Williams (2017) who discovered that soccer players were more accurate on

basketball decision tasks, when compared to tennis tasks. Therefore, the abundance of

hours accumulated by participants of the current study in invasion sports may assist with

the important ability to make appropriate decisions in hockey tasks (Berry et al., 2008;

Côté, Horton, MacDonald, & Wilkes, 2009; Roca & Williams, 2017). This is based on

‘generalization,’ which allows an athlete to react properly to a new stimulus based on its

similarity to a familiar stimulus (Issurin, 2013).

The transfer of motor skills is believed to be initiated in specific regions of the

brain, as the breadth of experiences are theorized to strengthen synaptic pathways, which

may aid the ability to make faster decisions (Casey, Tottenham, Liston, & Durston, 2005;

Moody, Naclerio, Green, & Lloyd, 2014). Moreover, previously acquired motor

representations are stored and retrieved when they are later needed, as opposed to

learning a new movement, expediting the learning of new skills (Seidler, 2010; Seidler &

Noll, 2008). In the current study, it is worth noting that no participants reported playing a

sport that was played on ice, or required ice skates. In addition, only one participant

played inline hockey, which would have mechanics similar to that of ice hockey. The

expedited learning process displayed by athletes who have a diverse developmental

background may only be apparent at later stages of a career (Güllich et al., 2017). Güllich

et al. (2017) found that more activities during the ‘sampling’ stage did not differentiate

performance in that stage; differences were only noticed when the athletes entered the

‘specialization’ stage. Furthermore, research has shown that a lack of creativity, or

robotic performance, may be caused by ‘chunking’ information based on only one

context and limited experiences (Simon & Chase, 1973; Vealey & Chase, 2016).

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Therefore, the diverse sport history that participants of the current study experienced may

have assisted them in progressing in their careers by allowing them to be more creative

than if they had not played other sports. However, when athletes reach a high level of

competency there does not appear to be an advantage to playing sports other than their

primary sport (Issurin, 2013).

There also exists the potential for negative effects from trying to transfer skills. If

sports are too different, or lack similarities, some authors believe that it may negatively

affect performance (Davids et al., 2008). Schmidt and Wrisberg (2000) address this issue

by breaking sports down into movement, perceptual, and conceptual elements. With this

method, the authors find similarities between sports that may appear dissimilar. For

example, they state that similarities exist between hockey and tennis in movement (e.g.,

balance and object manipulation), perceptual (e.g., interpretation of opponents’

movements), and conceptual elements (e.g., spacing). These authors argue that negative

transfer does not exist across sports if applying these classifications to the individual

elements of the sports, with the possible exception of beginners who are starting to learn

a sport (Schmidt & Wrisberg, 2000). Another potential negative effect of multiple sport

participation is the demand that back-to-back high-intensity activities could place on a

child’s body (American Academy of Pediatrics, 2000; Brenner, 2007; Caine, DiFiori, &

Maffulli, 2006; Kutz & Secrest, 2009). Furthermore, single sport athletes have the

perception that they have more control over their schedules (Brylinski, 2010). Therefore,

monitoring the intensity of the other sports in which children participate should take into

consideration the emotional and physical demands of puberty, school, and social

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pressures to minimize the chance of overtraining and burnout (Goodger, Lavallee,

Gorely, & Harwood, 2006; Kutz & Secrest, 2009; Lemyre et al., 2007).

In a study of high-school varsity athletes, Miller at al. (2017) found that male

single-sport athletes showed greater movement asymmetry than multi-sport athletes. This

asymmetry may increase the risk of injury and be due to a lack of movement skills due to

the narrow focus of practicing only one sport (Miller et al., 2017; Moody et al., 2014;

Myer et al., 2015a). This contradicts Gorman, Butler, Rauh, Kiesel, and Plisky (2012)

who found no difference in movement symmetry between single-sport athletes and multi-

sport athletes. Of note, the single-sport athletes in the Gorman et al. (2012) study were

significantly older than the multi-sport athletes, which may have possibly affected their

findings. Alternatively, experiencing multiple sports nurtures physical literacy and social

self-efficacy, and promotes movement variability, which has been shown to prevent

excessive wear on joint structures (Côté & Vierimaa, 2014; Davids et al., 2008; Hamill,

Haddad, Heiderscheit, Van Emmerik, & Li, 2006; Horrocks et al., 2016; Pesce et al.,

2012). Therefore, overuse injuries may be decreased later in a career by moving

differently and training opposing muscle groups through playing different sports, (Côté &

Vierimaa, 2014; Hamill et al., 2006; Sugimoto et al., 2017). In addition to decreasing

injuries, movement variability may also improve performance by allowing sport-specific

skills to be executed despite in-game disturbances (Davids et al., 2008; Santos et al.,

2017; Schöllhorn, Hegen, & Davids, 2012; Tan, Chow, & Davids, 2012).

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Appendix H

Expanded discussion for requirement to specialize

The results of this study show that both parents and coaches may request that

players not participate in other sports to prevent injuries from affecting their performance

in hockey. The participants of the current study utilized individual coaches, bought

equipment from sporting goods suppliers, and travelled regularly, to mention just some of

their costs. In the USA, youth-sports is projected to be a $15.3 billion dollar industry,

which is a 55% increase relative to 2010 (Gregory, 2017). This industry is fueled by

parents that willingly invest more than 10% of their annual income to provide resources

for their children (Gregory, 2017).

Additionally, sampling sports may cause their child to miss opportunities within

their sport, which may lead to a decrease in motivation and self-esteem (Horton, 2012;

UFSPRC, 2013). In part, these rankings and projections have created an estimation of 20

million highly-specialized youth athletes (Post et al., 2017). In addition to the obvious

costs associated with sport participation, such as registration and equipment, many

companies have joined in the pursuit of youth sports profits. Possibly the most glaring

example given by those reports is of Blue Star Sports, a start-up technology company

focusing on providing support for various aspects of youth sport (Blue Star Sports, n.d.).

Fueled by investors who have contributed hundreds of millions of dollars, Blue Star

Sports has acquired 18 different companies between 2016 and July of 2017, and provides

service in 32 countries in an attempt to become the ‘clear winner in the youth sports

management ecosystem’ (Gregory, 2017; Heitner, 2017). Likewise, Time Inc has

launched three Sports Illustrated Play apps which have over 17 million users injecting

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one billion dollars into the market (Gregory, 2017). Even nonprofit organizations are

benefitting from the influx of youth sport monies. The United States Specialty Sports

Association (USSSA) was given nonprofit status in the USA to promote social welfare.

In 2015, the USSSA claimed over $13 million in revenue, and payed its chief executive

officer over $800,000 (Gregory, 2017).

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VITA AUCTORIS

NAME: William Joseph Garland

PLACE OF BIRTH:

Carbonear, NF

YEAR OF BIRTH:

1980

EDUCATION:

Carbonear Integrated Collegiate, Carbonear, NF, 1998 Dalhousie University, B.Sc., Halifax, NS, 2002 Sheridan College, Dip. Sports Injury Management, Oakville, ON, 2005 University of Windsor, M.H.K., Windsor, ON, 2017