The connection between the geographical stratification of athletic club and the effectiveness of child football training.
Notice bibliographique
Résumé
IntroductionThe production of new players is the motive of many football clubs mainly in Europe, which have set up classes for the identification and instruction of young players. They see the production of young footballers as a fruitful investment for two reasons: a) in a few years some of them will staff their men's teams and b) others will be profitably sold. They aim to incorporate particularly gifted young players into their force and to train them according to their own particular philosophy in order to achieve their maximum level of performance (Reilly, Williams, Nevill, Franks, 2000). The Rules for the Official Approval of Teams adhered to by football federations in member-countries of the European Football Federation (UEFA, 2012) also contribute to this.The continually increasing demand for footballers creates the need for the carefully planned instruction of players, especially the young (Kock, Krauspe, 1996). Whereas once it was possible for a talented athlete to distinguish himself in any sport in a relatively short time, now it takes many years of systematic, planned training (Avgerinos, 2007). According to Simon & Chase (1973) it requires at least 10 years' preparation to reach the highest level of performance in any sport. This ten-year golden rule, as it is called, has been applied to football by Helsen, Starkes & Hodges (1988) who seek to identify all of the factors which influence the development of young athletes, with the aim of producing footballers by the safest, least risky methods. According to Bloom (1985) the stimulation received by athletes from the environment in which they grew up, stemming in the main from the urge to train and the skills obtained, leads to differences which can still be observed in maturity. Over the last few decades researchers have been studying the effect that the size of a town has on the development of an athlete and his career. Relative studies have also been carried out by Curtis & Birch (1987) on Canadian ice-hockey players, Carlson (1988) on Swedish professional tennis players, Baker & Logan (2007) on young Canadian hockey players, Cote, MacDonald, Baker, Abernethy (2006) on professional athletes in different fields, Farser-Thomas, Cote, MacDonald, Baker, Abernethy (2006) on adolescent swimmers, Lidor, Cote, Arnon, Zeev, Cohen-Maoz (2010) on team-sportsmen, Bruner, MacDonald, Pickett, Cote (2011) on Swedish, Finnish, American and Canadian athletes. Most of the studies that have been carried out mainly refer to very large countries such as Canada and the United States, the comparison being made between cities with populations greater or lesser than 500,000 inhabitants. Data acquired was mainly from professional or high-level athletes. The conclusions showed that athletes who grew up in cities with fewer than 500,000 inhabitants had a greater chance of reaching a high level of achievement than those who either came from very small towns or very large ones. These areas offer more chances for playing games, easier access to athletic facilities, greater safety, a greater range of choice, more positive social support and better organization (Curtis & Birch, 1987; Cote et al., 2006; Baker & Logan, 2007; MacDonald, King, Cote, Abernathy 2009; Fraser-Thomas, Cote, MacDonald, 2010).Other researchers maintain that population size isn't on its own advantageous to the development of an athlete and that other parameters, concerned with the social and cultural environment should be taken into account. Such parameters are the cultural tradition, athletic system and geographical distribution of the population (Baker, Schoner, Cobley, Schimmwer, Nattie, 2009; Lidor et al., 2010; Bruner, Macdonald, Pickett, Cote, 2011).The geographic stratification of the Greek populationThe geographic stratification of the Greek population during the 20th C. underwent significant changes. According to the 1920 census 22.09% of the population lived in urban areas, 15. …
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».