For economic and prestige reasons, football organisations try to develop outstanding players (Relvas et al., 2010; Grix and Carmichael, 2012). To succeed in this aim, clubs and national federations have to think twofold. First, the most talented players with the potential to become elite athletes have to be identified at the right time. Second, optimising the learning environment is then crucial for developing these players (Williams and Reilly, 2000). In the search for this optimisation, several athlete development models have emerged from research and been adapted and implemented by professional sport organisations (Bruner et al., 2010; Côté and Vierimaa, 2014). Among other factors, practice contributes highly to these development models on the pathway to expertise (Baker and Horton, 2004; Rees et al., 2016). However, apart from a broad consensus statement on the essential role of practice from an early age, several open questions regarding its characteristics within youth sports participation are vigorously debated in the field of sport science (Côté et al., 2007). For that reason, policy makers in clubs and federations may struggle in designing evidence-based structures within talent development in football, demonstrating a need for further research on the parameters of practice for child athletes (Mountjoy et al., 2008).

Out of the open questions on the properties of learning activities in youth sports participation, two well-known ones deal with the subsequently introduced dimensions domain specificity and performance orientation (Storm et al., 2012; Coutinho et al., 2016b). We perceive domain specificity as the degree of congruence in biomechanical, physiological and psychological characteristics between learning activities and the primary sport domain of an athlete. Thus, it represents a major issue in the common specialise or sample debate (Côté et al., 2009), i.e., whether young talents should focus on a single sport-specific domain early, or try to build a foundation with broad and different experiences from several kinds of sports. In general, the former is known as specialisation (high value of domain specificity), the latter as diversification/sampling (low value of domain specificity). The second dimension of interest, the performance orientation, is perceived as a summation of a few structural characteristics of learning activities. By a combination of various degrees of goal setting, monitoring and correction (Côté et al., 2003), each learning activity receives a certain degree of performance orientation. The literature mainly differentiates between practice as a highly structured, coach-led activity (high value of performance orientation) and play, which represents a fun-oriented learning activity without supervision (low value of performance orientation; Côté et al., 2007).

Both presented dimensions play key roles in the development of the sporting talent (Bridge and Toms, 2013). As Coutinho et al. (2016b) named a sometimes neglected interaction between domain specificity and performance orientation, they are currently described as a two-dimensional construct of characteristics of learning activities. Though both dimensions seem to be a continuum with a broad spectrum of possibilities, many researchers reduce each case to the previously mentioned dichotomous counterparts of specialisation vs. diversification/sampling and practice vs. play (Baker et al., 2009; cf. Figure 1).

Côté and Erickson, 2015 recently reconceptualised the idea of diversification/sampling. They pointed out that one can specialise in a single sport-specific domain and sample through different experiences of the same sport domain (e.g., in case of football: playing and practising with your club, playing beach soccer with friends, playing in a park just right after school, and playing on your own). Although this perception would enable a coexistence of concurrent specialising and sampling, the literature on domain specificity is still dominated by a controversial, polarised debate.

The origin for the dichotomising of domain specificity and performance orientation is obviously the opposite profile and huge influence of two well-established concepts for developing expertise in sports, the deliberate practice framework (Ericsson et al., 1993; Ericsson, 2008; Baker and Young, 2014) and the development model of sport participation (Côté, 1999; Côté et al., 2007; Côté and Vierimaa, 2014). According to Ford et al. (2015), deliberate practice is based on the monotonic benefits assumption. This stands for a linear relationship between the amount of highly effortful and structured activity with the explicit goal of improving performance, and the acquired performance in a specific task. Naturally this means that each learning activity may be carefully monitored, with a focus on immediate correction (Côté et al., 2003). In summary, the deliberate practice view can be related to the idea of maximising domain specificity (specialisation) and performance orientation (practice) from an early age. In contrast, although the development model of sport participation accepts and integrates the deliberate practice framework as one possible solution to enabling elite performance, it also creates a multi-staged opposite position (elite performance through sampling) with initial sampling years up to 12 years of age. This view recommends an early involvement in several sports in combination with fun-oriented and loosely monitored learning activities as the preferable way to expertise (Côté, 1999; Côté et al., 2003, 2007). In other words, elite performance through sampling represents a minimum of domain specificity (diversification/sampling) and performance orientation (play) in the early stage of development, followed by an increasing amount of deliberate practice from 12 years on (Côté et al., 2007).

Researchers have hypothesised some mechanisms to explain the learning effects according to low values of domain specificity and performance orientation in youth learning activities. First, some researchers have explored possible transfer effects throughout different sports from diversification/sampling (Baker et al., 2009; Fransen et al., 2012). Second, others have assumed an increase in intrinsic motivation (Hendry et al., 2014; Vink et al., 2015) or tactical performance (Greco et al., 2010; Memmert et al., 2010) from playful activity. Finally, if these conjunctions of diversification/sampling and play are not able to compensate or even overshoot the accumulated loss in domain-specific and performance-oriented practice volume from an early specialisation, the elite performance through sampling advocates still claim that it may lead to the same senior skill level without negative consequences (Côté and Hancock, 2015). These possible risks of an early specialisation include, for example, damage to health (Law et al., 2007; Jayanthi et al., 2013; Difiori et al., 2014; Pasulka et al., 2017), higher incidence of dropout (Wall and Côté, 2007; Fraser-Thomas et al., 2008) and burnout (Strachan et al., 2009). A high degree of reservation and numerous warnings against early specialisation have resulted from these findings (Wiersma, 2000; Malina, 2010; Mostafavifar et al., 2013; Bergeron et al., 2015; Feeley et al., 2016; LaPrade et al., 2016; Read et al., 2016). Moreover, the effectiveness of specialised and performance-oriented practice has recently been critically scrutinised. According to Macnamara et al.’s (2016) meta-analysis, deliberate practice only explains 18% of the variance in sports performance, contrary to the monotonic benefits assumption. Overall, there is increasing criticism of specialisation and high performance-orientation within early sports participation, although their contribution to the development of expertise has been shown many times (Côté et al., 2003; Baker et al., 2003a, 2009; Baker and Young, 2014; Ford et al., 2015).

The inconsistency in the findings and positions according to this dichotomous debate supports the absence of a single solution for all sporting contexts (Suppiah et al., 2015). However, in the case of a single view on talent development in football, the distribution of learning activities shows a more consistent picture. Ford et al. (2009) offered the early engagement hypothesis, which provides a football-specific pathway to expertise. Consisting of an early entrance into the domain, an extensive amount of both, practice and play, and a substantially smaller amount of hours in sports besides football, this pathway has been recognised several times as a promising one (Helsen et al., 1998; Ford et al., 2009, 2012; Roca et al., 2012; Williams et al., 2012; Haugaasen et al., 2014). While finding similar characteristics in the allocation of learning activities, Ford and Williams (2012) and Hornig et al. (2016) claimed a more relevant influence of diversified learning activities besides football. However, all of these investigations may suffer from methodological problems, as there are difficulties in categorising different learning activities in team sports (Helsen et al., 1998). These problems notwithstanding, an early specialisation combined with activity types from different degrees of performance orientation obviously describe a promising pathway for talent development in football.

Beneath the characteristics of a specific sport domain, country- or culture-specific environments may also contribute to inconsistencies in the described debate (Baker et al., 2003b; Storm et al., 2012; Suppiah et al., 2015; Forsman et al., 2016). Although Ford et al. (2012) did find similarities between development activities of elite soccer players from different countries, Araújo et al. (2010) emphasised that specific environment constraints such as in Brazilian football (i.e., football played with adapted norms and rules in a variety of locations), may lead to specific pathways to expertise. In addition, Holt (2002) showed that talent development systems in football differed a great deal between Canada and England.

Overall, it may therefore be difficult to generate a viable answer to the appropriate configuration of domain specificity or performance orientation within youth sports participation from distinct sport domains or countries. There seems to be no one-model-fits-all approach, as “the details of the developmental route undertaken by a successful elite athlete largely depends on the nature of the sport, and the culture and context of the country” (Suppiah et al., 2015).

To shed further light on the current issue in the context of Swiss Football, Zibung and Conzelmann (2013) asked former Swiss junior national team (SJNT) players about their sport participation up to 12 years of age. Based on the multi-dimensional nature of the dataset and its context of developing human individuals, their data analysis followed a development-related perspective arising from the holistic and dynamic-interactionistic approaches of developmental science (Bergman and El-Khouri, 2003; Bergman and Andersson, 2010). These concepts question the existence of a single General Linear Model and the application of common variable-oriented methods (Bergman and Andersson, 2010). Hence, Zibung and Conzelmann (2013) used a person-oriented approach (Bergman and El-Khouri, 2003), which seems to be appropriate for issues of talent development by enabling the opportunity of non-linear interaction between single characteristics within each individual (Bergman and Andersson, 2010).

Using the person-oriented approach, Zibung and Conzelmann (2013) found results in alignment with the aforementioned early engagement hypothesis (Ford et al., 2009). High values of domain specificity consisting of both, practice and playful activities led to players reaching higher success levels as adults. Regarding the participants, one has to mention that SJNT players are already a highly selected population. At most, 2% of all registered football players in Switzerland have the chance to participate at that elite youth level (Romann and Fuchslocher, 2013), which becomes more and more relevant for reaching elite levels in adult football. Regarding late adolescence, participating in elite youth football development programmes (e.g., academies or junior national teams) has a significant impact on later participation in professional teams. For example, almost 90% of all German Bundesliga players (seasons 2009/2010 to 2011/2012) had been involved in a youth academy for at least one season (Güllich, 2014). Also, around 60% of German U19 national team participants become Bundesliga players and the same amount of Portuguese U17/18 national team players are selected for the senior national team later in their career (Barreiros et al., 2014; Güllich, 2014).

In addition to Zibung and Conzelmann’s (2013) study, which dealt with the pathway from elite youth football in late adolescence to adulthood, it would be interesting to know if similar distributions of learning activities in the initial phase of the sports career also boost the chances of even participating in elite youth football development programmes. For that reason, we investigated whether SJNT players had more domain-specific and performance-oriented experiences up to 12 years of age, than their less successful peers. Together with the aforementioned results, this may contribute to the relevant knowledge on domain specificity and performance orientation of learning activities within a talent development system in football.

Table 3 gives an overview of the total number of accumulated hours of activities during early sport participation up to 12 years of age, across specific clusters and throughout the entire sample. On average players completed a substantial amount of domain-specific in-club practice (1128 h). However, this accounts for only 22.5% of the total hours of all learning activities together. The players spent nearly twice as much time in free football play without supervision (2058 h, 41.0%) as in the club. Following from the broad inclusion of any kind of other activities, the sports activities besides football contributed to a remarkable extent to the early sport participation of the participants (1837 h, 36.6%). The initial involvement in organised football took place at an average age of 6.3 years and only five players in the whole sample entered their first football club later than 9 years of age.

The cluster analysis extracted a five-pattern solution (cf. Table 3 and Figure 2) and displayed an explained error sum of squares (EESS) of 52.1% after partitioning. Therefore it did not quite meet the desirable 2/3 criterion (Bergman et al., 2003). On the other hand, the total weighted mean of the homogeneity coefficients over all clusters reached a sufficient value (weighted HC_mean = 0.97) and the silhouette coefficient (SC = 0.52) illustrated that a reasonable structure was found in the analysed data (Kaufmann and Rousseuw, 1990; Vargha et al., 2015, 2016). Therefore, two out of three quality coefficients for the cluster analysis indicated an acceptable pattern solution.

According to the cluster profiles, cluster 1 was basically characterised by an extensive amount of free play within football (4258 h), which was around two standard deviations higher than average. In combination with a slightly increased amount of in-club practice (1304 h), which goes along with the early start of careers in football clubs at 5.5 years, these Football enthusiasts (n = 25) showed the highest number of domain-specific experiences overall. Furthermore, the profile of cluster 1 consisted of a slightly above average amount in activities besides football (1969 h), which led to the highest sum of 7531 h for all learning activities together.

The second cluster showed Club players (n = 56) to have the earliest initial club participation (5.4 years) and subsequently the highest amount of in-club practice (1602 h). Within free football, these Club players had a lower number of around 2270 h compared to the Football enthusiasts and, even in activities besides football, they were slightly below average (1737 h). Overall cluster 2 reached an amount of 5328 h within all learning activities.

The largest group of Average players (n = 106) without special characteristics, i.e., z-scores < |0.5| in each operating factor, was found in cluster 3. They completed a total number of 4127 h in the three sections of learning activities (1072 h in-club practice, 1695 h free play and 1360 h activities besides football). Their highest absolute z-standardised value concerned the diversified sports activities besides football, with around 500 h below average.

A group with an extraordinarily high engagement in sports activities besides football (3593 h) was found in cluster 4, which led to the description as Poly-sportive players (n = 42). As regards football-specific activities, these players showed a slightly below average amount of in-club practice (1091 h) and an above average amount of free play within football (2359 hrs), while they started their careers in football clubs at an average age (6.4 years). Overall, this cluster completed 7043 h in the three learning activities.

Finally, Football abstainers (n = 61) formed cluster 5. These players started later in the organised football than their peers (7.9 years) and they did not participate that much in domain-specific (743 h in-club practice and 1645 h free play) or diversified activities (1493 h), whereby they only reached a total amount of 3881 h.

Regarding the transition analysis from clusters to youth football success levels, several significant differences from the expected values arose. First, Football abstainers showed an increased probability of playing football only on a local level (OR [95% CI] = 5.6 [2.9; 10.6], χ²(1) = 19.79, p < 0.01) and reduced chances for higher youth football success levels, as players from this cluster could rarely move up to a regional (OR = 0.5 [0.3; 0.9], χ²(1) = 1.63, p = 0.02) or national squad (OR = 0.3 [0.1; 0.8], χ²(1) = 4.07, p = 0.01). Analysing the transitions of Poly-sportive players, there was an above average chance for them to become regional players (OR = 2.2 [1.0; 4.6], χ²(1) = 1.35, p = 0.03). Furthermore, players from this cluster had a reduced risk of ending up in local level football (OR = 0.3 [0.1; 1.0], χ²(1) = 2.85, p = 0.03). The transitions of Average players from cluster 3 did not show any differences from expected values, while cluster 2 and its Club players were almost completely absent from the least successful local level (OR = 0.1 [0.1; 0.5], χ²(1) = 8.33, p < 0.01). In contrast, the constellation in this cluster led to an increase in transitions to the national level (OR = 2.1 [1.1; 4.1], χ²(1) = 3.26, p = 0.02). Finally, the most successful cluster in terms of transitions to the SJNT was the Football enthusiasts (OR = 3.1 [1.3; 7.3], χ²(1) = 5.26, p = 0.01), which had also a reduced tendency to be at the average regional level (OR = 0.4 [0.2; 1.0], χ²(1) = 1.32, p = 0.04).

The presented findings support previous work showing the essential role of sport participation up to 12 years of age (Côté et al., 2007), as its characteristics significantly influence the chance to participate in a SJNT. Football enthusiasts and Club players have an increased chance of becoming selected for this highest level of youth football in Switzerland, whilst Football abstainers have hardly any. In between Average players distribute to youth football success levels as expected by chance, whilst Poly-sportive players tend to reach the average regional level most frequently.

This study examined the influence of characteristics of learning activities within early sport participation on youth football success levels. Whilst Zibung and Conzelmann (2013) investigated SJNT players and their later success in senior football, the present research dealt with the most promising pathway to even reach the SJNT level from a broader sample. According to this, around 15 years between the researched cohorts and a shift to a different selection level within Swiss football did not lead to any fundamental changes in terms of arising patterns within cluster constellations. Thus, the current contribution together with that of Zibung and Conzelmann (2013) draw a coherent picture of the talent development system in Swiss football. To succeed within this system, a huge amount of domain-specific learning activities within early sport participation is recommended.

However, we definitely do not claim that our data support the deliberate practice framework (Ericsson et al., 1993). Quite the contrary, the most successful cluster of Football enthusiasts with its extraordinary amount of free play supports the hypothesis that a broad range of settings within domain-specific learning activities may lead to superior success later in the football career. These diversified settings include different values of performance orientation (e.g., free play and in-club activities) as well as miscellaneous activities (e.g., beach soccer, playing in a park, and organised match play). Following the concept of Côté and Erickson (2015), who described this as a new understanding of diversification/sampling (i.e., sampling different settings within one domain instead of sampling between several domains), based on our data we perceive the talent development in Swiss football as a specialised sampling model (cf. Figure 3).

Nevertheless, mechanisms behind the specialised sampling model remain unclear to a certain extent. Of course, hypothesis of sampling/diversification pathways have been suggested earlier (e.g., transfer effects, increased intrinsic motivation; Fransen et al., 2012; Hendry et al., 2014; Vink et al., 2015). In that case, specialised sampling may reflect some kind of seeking for diversity. Depending on the environmental context, there may be different solutions to do this (Storm et al., 2012; Suppiah et al., 2015). Although Switzerland has a reasonable multi-sport history (e.g., winning Olympic diploma in 20 different sport domains in last summer and winter games) and multi-sport ideology in terms of national coaches education and public funding, football plays a key role in Swiss youth sport participation. The success of the senior national team (e.g., placing fourth in FIFA nations ranking in august 2017) and the high number of local clubs and registered players (Romann and Fuchslocher, 2013) result in a major impact on general society. Therefore, the search for diversity in Swiss youth sport participation may result in different settings of football as the most attractive solution. Further, supporting mechanisms of specialised sampling may connect to deliberate practice framework contents. Its deliberate practice activities are by itself not inherently enjoyable (Ericsson et al., 1993), whilst enjoyment of activities has been described as essential for keeping intrinsic motivation and following goals sustainable (Hendry et al., 2014). From that point of view, sampling several activities within the domain may reflect an addition of more enjoyable activities to keep the less enjoyable, more performance-oriented activities going. In addition, deliberate practice framework already stated on the embedment of playful activities for recovery (Ericsson et al., 1993). In that case, some amount of free play may have value for an active, biologocial and/or psychological recovery (Saw et al., 2016) from more intense and structured in-club practice.

Overall, it is very complex to unfold the exact mechanisms of successful patterns of early sport participation completely. Different cultures (e.g., availability of diversity of sport domains, popularity of a certain domain) and structures (e.g., talent development programmes) may significantly influence the success of certain development pathways (Storm et al., 2012; Suppiah et al., 2015). In Swiss football, we assume a specialised sampling model with a high degree of domain specificity within early sport participation (specialisation), which is enriched by a sport-specific diversity resulting from a broad range of settings within football (sampling), to be the most promising one.

If the existence of such models may be further supported by subsequent research, this could lead to a resolution of the specialise or sample debate (Côté et al., 2009). The ideas of former opposite counterparts would merge somewhere in the middle of extreme positions by enabling a coexistence of specialising and sampling through the specialised sampling model.

Substantial contributions to the conception or design of the work, interpretation of data, drafting the work or revising it critically for important intellectual content, final approval of the version to be published, and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: RS, CZ, MZ, and AC. Data acquisition: CZ. Data analysis: RS and MZ.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.