Relative age effect in U-16 Asian Championship Soccer Tournament

Abstract

Athletes born at the beginning of the year may present the advantage of physical characteristics, motor ability or cognitive knowledge that could increase their chances for selection against their peers. This circumstance could lead to the over-representation of older athletes in an age-defined competition which is commonly referred to as relative age effect (RAE). Although, a number of studies have demonstrated that RAE is apparent in youth soccer, such studies barely investigates the performance advantage that may likely exists across the birth month of the athletes. The current study aims to determine the presence of RAE in the under 16 Asian Football Confederation Championship (AFC), investigate the effect of RAE on the team qualification success as well as ascertain the existence of RAE in the playing position amongst the soccer players. Data of the 2018 AFC under 16 qualifications matches were obtained from the AFC. A total of 719 players from 32 countries participated in the qualification competitions. Chi-square for goodness fit is used to determine the existence of the RAE across the players’ month of birth while logistic regression is applied to analyze the differences of the quartiles’ distribution with respect to the quartile, qualification status (qualifier or non-qualifier) as well as the playing position of the players. The results demonstrate the presence of RAE in the AFC under 16 soccer tournaments [χ2(4) = 21.53; p < 0.001] with the largest number of older players dominating the qualified team. Likewise, a substantial difference is observed with regards to the quartile and various playing position of the players p < 0.05.

A longitudinal analysis of injury characteristics among elite and amateur tennis players at different tournaments from electronic newspaper reports

“…As a result, the test will not operate with numeric data that are parametric or continuous. It is worth noting that the data set necessary to conduct the Chi-square test should be in the form of frequency, i.e., count datasets, rather than percentages, percentiles, or relative frequency ( 50 ). The Chi-square analysis is used in the current study to examine the incidence, exposure, and status of tennis-related injuries as reported by the selected online newspapers.…”

Surveillance of Injury Types, Locations, and Intensities in Male and Female Tennis Players: A Content Analysis of Online Newspaper Reports

“…Thus, the test does not work with parametric or continuous numeric data. It is worth highlighting that the data set required to run the chi-square test should not be in the form of percentages, percentiles, or relative frequencies but rather in the form of frequency, i.e., count datasets [ 38 ]. In the current investigation, chi-square analysis was employed to study the incidence, exposure, and status of tennis-associated injuries as covered by the selected online newspapers.…”