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Enregistrement W4360609445 · doi:10.1371/journal.pone.0283390

The relative age effect is widespread among European adult professional soccer players but does not affect their market value

2023· article· en· W4360609445 sur OpenAlexaboutno aff
Eduard Bezuglov, Ryland Morgans, Mikhail Butovskiy, Anton Emanov, Larisa Shagiakhmetova, Bekzhan Pirmakhanov, Zbigniew Waśkiewicz‬, Artemii Lazarev

Notice bibliographique

RevuePLoS ONE · 2023
Typearticle
Langueen
DomainePsychology
ThématiqueSport Psychology and Performance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLeagueQuarter (Canadian coin)DemographyCompetition (biology)Affect (linguistics)Position (finance)PsychologyMatch playDemographic economicsMedicineBusinessGeographyPhysical therapyEconomicsSociologyBiologyFinance

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The relative age effect (RAE) is most prevalent in highly competitive youth soccer and persists to a lesser extent in senior soccer. However, it is known that soccer players born in the second half of the year are as successful at senior level, indicating that they are equally talented although under-represented at youth level due to bias during the selection process, in which the emphasis is on more pronounced physical qualities in a specific period of time. Examining the prevalence of the RAE among professional soccer players depending on the level of competition and playing position, as well as analyzing the relationship between the player's birth quarter and market value, are of scientific interest. METHODS: The dates of birth, playing position, and market value of all adult male soccer players included in the final rosters of teams from the top-division of 54 European countries, listed on www.transfermarkt.com on August 15th, 2020, were analyzed (18,429 soccer players in total). All players were categorized into four groups according to the quarter of birth (Q) and playing position. All teams were further sub-divided in groups depending on the soccer clubs' level of representation in the UEFA Champions League. RESULTS: Of 18,429 players, 30.9% were born in Q1, 25.7% in Q2, 23.8% in Q3 and 19.6% in Q4. The number of soccer players born in Q1 was lower in less competitive leagues. The number of players born in Q1 decreased as the level of competition decreased; the highest percentage of these players was observed in clubs that are among the top 50 ranked in UEFA or compete in the most prestigious European championships. The RAE was less pronounced in teams that participate in lower competitive championships. There was no significant difference in market value between players playing position and level of competition when born in different quarters. Although, the most expensive soccer players in the European championships were late-born forwards. Players of various groups differed in their market value. CONCLUSIONS: The RAE is currently prevalent in all the most competitive senior soccer leagues and teams in Europe regardless of playing position. There are no significant differences in market value between players of the same playing position and level of competition when born in different quarters. The most expensive soccer players in the European championships are forwards born in Q4. These findings may indicate that the under-representation of "late-born" soccer players in youth, and then consequently in adult soccer, is not associated with lower talent, but with other factors, possibly based on physiological characteristics and socio-cultural factors. Further measures are needed to mitigate the discriminatory effects of selection bias based on the RAE.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,226
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,004

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.

Tête enseignante Opus0,034
Tête enseignante GPT0,296
Écart entre enseignants0,261 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations13
Publié2023
Routes d'admission1
Résumé présentoui

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