University of Windsor Scholarship at UWindsor
Electronic Theses and Dissertations Theses, Dissertations, and Major Papers
2017
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
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.
iii
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.
iv
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.
v
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.
vi
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
vii
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
viii
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
ix
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
x
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
xi
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
xii
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
xiii
LIST OF ABBREVIATIONS/SYMBOLS
ASA American Sports Act
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
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
xiv
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
1 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
2 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.
3
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
4 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).
5 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
6 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
7 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.
8
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
9 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
10 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).
11
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.
12
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
13 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.
14 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.
15
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
16 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
17 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
18 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
19 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).
20
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).
21 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.
22
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
23 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
24 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
25 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
26 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
27 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
28 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.
29
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),
30 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
31 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.
32
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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
59
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
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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
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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
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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
63
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
64
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 NA NA NA 1 1 NA 3 2 3 3 8 mechanism Overuse NA NA NA 0 0 NA 1 2 0 0 0 mechanism Combined time 30 30 NA NA NA NA 154 days 210 days 111 days 111 days 328 days away days days Combined time 30 30 5 5 5 NA NA NA NA 288 days 285 days affected days days years years years
65
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
66
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
67
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
68
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
69
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
71
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
72
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 6 7 8 9 10 11 12 13 14 15 16 Hours 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 9 - Play higher competition, 1 - Avoid conflict with a coach, 1 - Not selected for Reason for changing 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
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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
88 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),
90 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
91 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
92 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
93 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
94 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
97 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
101 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,
103 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
104 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
105 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
106
(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
107 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).
108
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.
109
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
110 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
111 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
112 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).
113 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
114 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.
115
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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.
For each of the activities that you have provided, can you recall the number of hours per
137 week and months per year you were regularly involved. Were there periods of time that participation would have increased or decreased (Peak/Off Peak)?
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
138
____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
139
___ 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
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. For each age please provide the age of the peers you trained and competed with. 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. 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? 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. 140 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. 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)? 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). 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. 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 141 be done for each of the stages you previously identified. 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. 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) ♦ 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 142 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. Please indicate the number of months per year that you engaged in each activity. 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. 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. 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 143 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. ● 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 144 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 8 145 Chart B2 Hours per week and months per year involved in other activities Activity 6 (Column for each 21 + age 7-20) 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 146 Chart B3a: Developmental chart for maturation and performance in main sport Stage Year Age Group/ Age of Personal Team Height ... ID Level Peers Accomplishments Accomplishments Relative Difference Change 147 Chart B3b: Developmental chart for maturation and performance in main sport Stage ... Intensity Quality of resources Injury Hrs/Wk Sport ID Description Mechanism Time Away Time Affected 148 Chart B4: Development of Relevant Practice Activities Age Stage Activity Category Peak Peak Off Peak Off Peak Physical Mental Fun Hr/Wk Mths/Yr Hr/Wk Mths/Yr Effort Concentration 149 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? 150 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; 151 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. 152 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 153 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- 154 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 155 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). 156 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., 157 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 158 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 159 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 160 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, 161 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). 162 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. 163 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 164 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 165 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 166 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). 167 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 168 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). 169 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 170 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. 171 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 172 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 173 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 174 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). 175 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 176 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). 177 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 178 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). 179 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 180