Commentary: “How Much is that Player in the Window? The One with the Early Birthday?” Relative Age Influences the Value of the Best Soccer Players, but Not the Best Businesspeople

Fumarco, Luca; Gibbs, Benjamin G.

doi:10.3389/fpsyg.2017.00058

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…Second, by focusing on RAE as skew in frequency and RAE as skew in value (transfer value, games played), we can contribute to the underdog literature which has so far produced mixed results. Third, by analysing at a disaggregate level, not only do we heed Fumarco and Gibb’s [ 24 ] warning about choice of competition year, but we can test the generalisability of our findings across countries and clubs. In so doing, we also hope to identify paragon countries or clubs: "paragon" in being less susceptible to RAE bias, and from whom others may learn.…”

Section: Introductionmentioning

confidence: 91%

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Relative age effect in elite soccer: More early-born players, but no better valued, and no paragon clubs or countries

Doyle

Bottomley

2018

PLoS ONE

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The paper analyses two datasets of elite soccer players (top 1000 professionals and UEFA Under-19 Youth League). In both, we find a Relative Age Effect (RAE) for frequency, but not for value. That is, while there are more players born at the start of the competition year, their transfer values are no higher, nor are they given more game time. We use Poisson regression to derive a transparent index of the discrimination present in RAE. Also, because Poisson is valid for small frequency counts, it supports analysis at the disaggregated levels of country and club. From this, we conclude there are no paragon clubs or countries immune to RAE; that is clubs and countries do not differ systematically in the RAE they experience; also, that Poisson regression is a powerful and flexible method of analysing RAE data.

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Section: Introductionmentioning

confidence: 91%

“…Fumarco and Gibbs [ 24 ] noted that the billionaires were drawn from various countries and US states, having many different definitions of the school year [ 25 ]. Thus to assume that all ages should be measured against the calendar year is misleading.…”

Section: Introductionmentioning

confidence: 99%

Relative age effect in elite soccer: More early-born players, but no better valued, and no paragon clubs or countries

Doyle

Bottomley

2018

PLoS ONE

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“…Given RAE, we anticipate a decrease in frequency across the cohort year, thus b should be negative; and the more severe the RAE bias, the more negatively large b should be. In the less likely situation of reverse RAE, the b should be positive and significant for an underdog effect [19,20]. In what follows, we focus almost entirely on b , the Poisson shape parameter.…”

Section: The Basics Of Poisson Regressionmentioning

confidence: 99%

“…There are 882, 734, 609, 882, and 868 such players in England, France, Germany, Italy, and Spain respectively; in total 3975 of 6644 registered players. This restriction minimises ambiguity due to players learning their trade in a foreign academy, while ensuring that the correct cohort year definition is applied [20]. Players in France, Germany, Italy and Spain are analysed using 1 st Jan. to 31 st Dec. as the domestic competition (cohort) year, while in England it is defined 1 st Sep. to 31 st Aug.…”

Section: Poisson Application: Big Five European Soccer Leaguesmentioning

confidence: 99%

The relative age effect in European elite soccer: A practical guide to Poisson regression modelling

Doyle

Bottomley

2019

PLoS ONE

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Many disciplines of scholarship are interested in the Relative Age Effect (RAE), whereby age-banding confers advantages on older members of the cohort over younger ones. Most research does not test this relationship in a manner consistent with theory (which requires a decline in frequency across the cohort year), instead resorting to non-parametric, non-directional approaches. In this article, the authors address this disconnect, provide an overview of the benefits associated with Poisson regression modelling, and two managerially useful measures for quantifying RAE bias, namely the Indices of Discrimination and Wastage. In a tutorial-like exposition, applications and extensions of this approach are illustrated using data on professional soccer players competing in the top two tiers of the “Big Five” European football leagues in the search to identify paragon clubs, leagues, and countries from which others may learn to mitigate this form of age-discrimination in the talent identification process. As with OLS regression, Poisson regression may include more than one independent variable. In this way we test competing explanations of RAE; control for unwanted sources of covariation; model interaction effects (that different clubs and countries may not all be subject to RAE to the same degree); and test for non-monotonic versions of RAE suggested in the literature.

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“…We investigate only domestic players because we pay particular attention to a commonly neglected aspect: different countries may adopt different cut-off dates (i.e., the date set by soccer federations to establish how to divide players into different age groups) and may enforce them to a different degree (i.e., it might be possible to circumvent formal youth age-grouping rules for several reasons; see Williams [ 6 ] for an example). In the absence of information about cut-off dates, researchers often assume uniformity of cut-off dates and this may be problematic and lead to spurious evidence of RAE [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Relative age effect and second-tiers: No second chance for later-born players

et al. 2018

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The main objective of this research was to determine the existence of relative age effect (RAE) in five European soccer leagues and their second-tier competitions. Even though RAE is a well-known phenomenon in professional sports environments it seems that the effect does not decline over the years. Moreover, additional information is required, especially when taking into account second-tier leagues. Birthdates from 1,332 first-tier domestic players from France, England, Spain, Germany and Italy and birthdates from 1,992 second-tier domestic players for the 2014/2015 season were taken for statistical analysis. In addition to standard statistical tests, the data were analyzed using econometric techniques for count data using Poisson and negative binomial regressions. The results obtained confirmed a biased distribution of birthdates in favor of players born earlier in the calendar year. For all of the five first-tier soccer leagues there was an unequal distribution of birthdates (France χ2 = 40.976, P<0.001; England χ2 = 21.892, P = 0.025; Spain χ2 = 24.690, P = 0.010; Germany χ2 = 22.889, P = 0.018; Italy χ2 = 28.583, P = 0.003). The results for second-tier leagues were similar (France χ2 = 46.741, P<0.001; England χ2 = 27.301, P = 0.004; Spain χ2 = 49.745, P<0.001; Germany χ2 = 30.633, P = 0.001; Italy χ2 = 36.973, P<0.001). Econometric techniques achieved similar results: estimated effect of month of birth, i.e., long-term RAE on players’ representativeness, is negative (statistically significant at the 1% level). On average, one month closer to the end of the year reduces the logs of expected counts of players by 6.9%. Assuming this effect as linear, being born in the month immediately before the cut-off date (i.e., December/August), reduces the logs of expected counts of players by approximately 75.9%. Further, ID (index of discrimination, that is, the ratio between the expected counts of players born in the middle of the first and the twelfth month of the selection year) is 2.13 and 2.22 for the first- and second-tier, respectively. In other words, in the top five European first-tier and second-tier leagues, one should expect the number of players born in the first month of the calendar year to be twice the number of those born in the last month. The RAE in the second-tiers is the same as in the first-tiers, so it appears that there is no second chance for later born players. This reduces the chances to recover talented players discarded in youth simply because of lower maturity.

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Commentary: “How Much is that Player in the Window? The One with the Early Birthday?” Relative Age Influences the Value of the Best Soccer Players, but Not the Best Businesspeople

Cited by 3 publications

References 19 publications

Relative age effect in elite soccer: More early-born players, but no better valued, and no paragon clubs or countries

Relative age effect in elite soccer: More early-born players, but no better valued, and no paragon clubs or countries

The relative age effect in European elite soccer: A practical guide to Poisson regression modelling

Relative age effect and second-tiers: No second chance for later-born players

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