1. Relative age effect
in Singapore Sports School
Chee Y. Low and Taisuke Kinugasa.
Sports Science Department, Singapore Sports School.
Introduction
Chronological age grouping is the strategy used for organising youth sports participation and competition in many countries
including Singapore. However, this organsiation strategy is likely to promote relative age effect (RAE) and may result in the
uneven distribution of birth dates that favour athletes born in the initial months of a selection year. Since one of the main
roles of Singapore Sports School (SSP) is to select and develop the elite youth athletes for both local and international
competition, the purpose of this study is to investigate how RAE influence athlete selection in SSP.
Methods
484 SSP athletes (281 males and 203 females) recruited between the year 2004-2009 were divided into four groups based
on the month of birth. Differences between the observed and expected birth-date distribution were tested with the chi-
square goodness of fit statistic. Expected birth-date distributions were calculated from available population birth statistics
between 1991 and 1996. Odds ratios (OR) and 95% confidence intervals were also calculated to examine the differences
within individual sport and gender. OR were then interpreted using Hopkin’s (2009) criteria: < 1.5 trivial, 1.5 < small, 3.5 <
moderate, 9.0 < large.
Results
Our results (Fig 1) showed a strong over-
Number of athletes
250
2
representation (X2=79.37, df=3, p<.001) of athletes
200 X =79.37, df=3, p<.001 (39%) born in the first quarter of the year and an
150 underrepresentation of athletes (15%) born in the last
100 quarter of the year. OR for all sports (Table 1) were
50 also calculated and three sports (track and field,
swimming and netball) were found to have a small but
0
significant likelihood for female athletes to be born in
Q1 Q2 Q3 Q4 the first half of the year. In contrast, only one sport
Fig 1. Quarterly birth-date distribution. (track and field) was found to have a small but
significant likelihood for male athletes to be born in the
first half of the year.
Table 1 . Odds ratio (95% confidence interval) examining birth-date distributions in relation to sport and gender.
Sport Gender Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Male 0.92 (0.48, 1.79) 0.86 (0.41, 1.77) 0.82 (0.39, 1.73) 1.00 (0.62, 1.59)
Badminton
Female 1.32 (0.41, 4.20) 2.46 (0.79, 7.67) 1.85 (0.56, 6.10) 1.19 (0.61, 2.33)
Male 1.26 (0.60, 2.64) 0.46 (0.18, 1.21) 1.74 (0.80, 3.79) 0.66 (0.41, 1.06)
Bowling
Female 1.17 (0.52, 2.64) 0.66 (0.26, 1.69) 1.40 (0.59, 3.30) 0.73 (0.42, 1.26)
Netball Female 5.11 (2.23, 11.70) 3.62 (1.53, 8.59) 2.37 (0.96, 5.86) 2.66 (1.67, 4.25)
Female 1.06 (0.41, 2.76) 0.29 (0.07, 1.20) 1.58 (0.59, 4.23) 0.55 (0.29, 1.02)
Sailing
Male 0.57 (0.22, 1.47) 0.91 (0.36, 2.34) 0.78 (0.29, 2.12) 0.81 (0.42, 1.56)
Soccer Male 1.78 (1.11, 2.87) 1.32 (0.79, 2.22) 1.13 (0.66, 1.97) 1.48 (1.08, 2.03)
Male 0.90 (0.40, 2.03) 1.72 (0.77, 3.84) 1.96 (0.88, 4.36) 0.77 (0.48, 1.23)
Swimming
Female 0.49 (0.25, 0.97) 0.63 (0.31, 1.27) 1.25 (0.64, 2.42) 2.09 (1.77, 3.45)
Male 0.25 (0.08, 0.82) 1.33 (0.53, 3.38) 0.93 (0.34, 2.52) 0.67 (0.34, 1.30)
Table Tennis
Female 0.06 (0.01, 0.47) 0.51 (0.16, 1.56) 0.91 (0.32, 2.54) 0.22 (0.09, 0.54)
Male 1.11 (0.65, 1.89) 1.44 (0.82, 2.53) 0.54 (0.28, 1.04) 1.80 (1.23, 2.63)
Track & Field
Female 1.27 (0.72, 2.23) 0.98 (0.54, 1.81) 0.54 (0.28, 1.07) 1.60 (1.06, 2.40)
Conclusions
Our findings show that RAE does exist within some sports academies in SSP. In order to reduce this bias, it is
recommended that coaches and sports scientists in SSP take RAE into consideration when selecting potential
athletes for the school. In addition, longitudinal studies can be conducted in future to investigate if the
implementation of a long term athlete development (LTAD) program mininises or eliminates the occurrence of RAE.
References
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Hopkins, W. G. A scale of magnitudes for effect statistics. A new view of statistics (available online at: http://sportsci.org/resource/stats/effectmag.html,
2009.