De Larochelambert Quentin

Difernand Audrey
email: audrey.difernand@insep.fr

Antero Juliana
email: juliana.antero@insep.fr

Sedeaud Adrien
email: adrien.sedeaud@insep.fr

Toussaint Jean-François
email: jean-francois.toussaint@aphp.fr

Louis Pierre-Yves
email: pierre-yves.louis@agrosupdijon.fr

Coulmy Nicolas

Relative age effect in French alpine skiing : problem and solution 1

Keywords: Ski, Relative age effect, Performance, Maturity, Talent Identification 15

Objectives: To identify the potential bias of the relative age effect (RAE) in French 16 alpine skiers and to propose a mathematical correction adjustment for such a bias.

17

Methods: All performances and birthdates of skiers on the national and 18 international circuit were collected from the 2004 up to 2019. A goodness-of-fit 19 chi-square test and the residuals were used to study distribution of birth trimesters 20 in youth competitors. A linear relationship between the distribution of 21 performances and the months obtain a calibration coefficient allowing to rebalance 22 the performance by considering the effect of RAE. 23 Results: Individuals born at the beginning of the year are over-represented in the 24 elite young selections in all disciplines for both genders. A coefficient based on 25 the relationship between month of birth and performance adjusts individual 26 performance and cancels out the effect of RAE. 27 Conclusion: The RAE is present in French alpine skiing. We present a method 28 allowing to consider the effect of the RAE in the performances realized in each 29 gender and discipline. This method allows coaches to have a more objective 30 opinion on performance and to reduce selection bias.

Introduction 39

The relative age effect is defined as a bias that advantages individuals born close to the 40 age-group cut-off in the cohort entry (eg school tests, sports competitions) (Helsen, 41 Winckel, and Williams 2005). The relative age effect has been a subject studied in many 42 fields including education since Freyman [START_REF] Freyman | Further Evidence on the Effect of Date of Birth on Subsequent 448 School Performance[END_REF]) and Armstrong (Armstrong 43 1966) in the 1960s. The tendency of better success of the individuals born at the beginning 44 of the year in the school environment has been confirmed in other studies. For example, development. Regarding performance, we will talk about the development of physical, 65 physiological or cognitive faculties. This link between development and performance has 66 been studied and theorized [START_REF] Berthelot | Exponential Growth Combined with Exponential Decline Explains 406 Lifetime Performance Evolution in Individual and Human Species[END_REF]Berthelot et al. 2019;[START_REF] Moore | A Study of Age Group Track and Field Records to Relate Age and 490 Running Speed[END_REF]) by a bi-67 phasic function of progression and decline. Depending on the determinants of sports 68 performance, this maturity can be achieved at different ages. For example, maturity in 69 chess, with a cognitive dominance, is reached at the age of 31, while in 100 meters 70 athletics, with a physical dominance, is reached at the age of 25. The determinants of 71 performance vary depending on the sport and therefore also the phases of development 72 and therefore the effects of RAE (Till et al. 2014). 73 This relative age effect generates long-term effects. Brustio and al. looked at the RAE to 74 adulthood in world athletics (Brustio et al. 2019). They first of all confirmed the presence 75 of bias in all disciplines in the youth categories (under 18 and under 20). They also 76 identified an relative age effect that persisted into adulthood in some disciplines, notably 77 throwing events. Likewise, Gennaro Boccia and al have shown that being relatively born 78 earlier in the year can confer an advantage throughout men's careers in throwing events 79 [START_REF] Boccia | Elite Junior 413 Throwers Unlikely to Remain at the Top Level in the Senior Category[END_REF]. Conversely, MacCarthy et al showed a reversal of 80 the RAE advantage (McCarthy, Collins, and Court 2016). Although relatively young 81 professional rugby and cricketer players were less likely to be selected into their 82 respective national academy systems, they were more likely to advance to senior national 83 teams. [START_REF] Mccarthy | Initial Identification & Selection Bias Versus 480 the Eventual Confirmation of Talent: Evidence for the Benefits of a Rocky Road?[END_REF] confirm this "reversal" of the 84 RAE in rugby. This reversal effect has been called the "underdog" effect (Gibbs, Jarvis, 85 and Dufur 2012). Finally, de la Rubia et al showed a relative age effect still present in the 86 adult male and female categories in international handball (de la Rubia et al. 2021). [START_REF] Baker | Variations in Relative Age Effects in Individual Sports: Skiing, Figure Skating 401 and Gymnastics[END_REF]. Analyzes of 1,474 ski jumpers, 7,501 cross-country skiers, 15,565 89 alpine skiers, 4,179 snowboarders and 713 Nordic combined athletes revealed significant 90 RAE for most of these disciplines for all genders. But no published study has investigated 91 French alpine skiing. Since each country has its own system of talent identification, 92 previous studies may not be simply extrapolated to the French spline skiing system. The 93 presence of RAE in skiing was global and having not changed over the past 10 to 15 years 94 [START_REF] Steidl-Müller | Did the Relative Age Effect Change over a Decade in Elite Youth Ski Racing?[END_REF], it is useful to focus on this phenomenon in French alpine 95 skiing specifically. In addition, no solutions regarding the RAE in alpine skiing has been 96

proposed. 97

In other sports, solutions have been proposed to consider the RAE. In the National 98 Hockey League, reducing the age groups; changing the annual limits for age groups; or 99 to delaying the selection and identification processes beyond the stages of puberty and 100 maturation have been tested [START_REF] Barnsley | Birthdate and Success in Minor Hockey: 404 The Key to the NHL[END_REF]. Nolan and Howell have 101 shown that these biases have persisted in hockey despite the available option for adjusting 102 this bias [START_REF] Nolan | Hockey Success and Birth Date: The 492 Relative Age Effect Revisited[END_REF]. Another study in basketball (de la Rubia Riaza et al. 103 2020) proposed to evaluate the athlete on her/his performance in the long-term and not 104 the immediate results only. They suggested to take into account the maturation of the 105 athlete rather than the age groups, and to design models of evaluation of young athletes 106 accounting for other parameters (psychology, emotional intelligence, cognitive skills, 107 etc.). These methods include development parameters linked to maturation, but none has 108 presently been validated that would more precisely detect future talent. Romann and 109

Cobley proposed a linear correction in sprint athletics [START_REF] Romann | Relative Age Effects in Athletic Sprinting 498 and Corrective Adjustments as a Solution for Their Removal[END_REF]. They 110 showed that a relative age difference of nearly one year resulted in expected average 111 performance differences ranging from of 10.1% to 5.3% among Swiss junior sprinters. between chronological age and objective performance measures of swimming (Abbott et 114 al. 2021(Abbott et 114 al. , 2020;;Cobley et al. 2019). A practical solution proposed by Helsen et al in soccer 115 [START_REF] Helsen | Leveling the Playing Field: A New Frontiers in Sports and Active Living[END_REF] A systematic review [START_REF] Webdale | Solving Sport's[END_REF]) identified ten independent solution to RAE 121 types from sixty-three peer-reviewed publications between 1980 and 2018. Six of these 122 solutions were "technical and policy-based". This relates to modifying the age structures, 123 correcting performance by a coefficient, achieving player quotas and making categories 124 by anthropometric parameters. Three proposed solutions were pedagogical and 125 informational focusing above all on pedagogical training to raise awareness of the RAE 126 bias. They highlight the difficulty of applying the different methods proposed and that 127 the implementation of these solutions has the potential to affect both positively and 128 negatively the career and life outcomes of participants. The recommended solutions seem 129 difficult to implement quickly because they require substantial changes in sports 130 organizations. 131 Therefore, we aimed to 1) determine the relative age effect in French alpine skiers in 132 young age groups and adulthood, and 2) recommend a method to consider the RAE. We 133 hypothesized that a relative age effect is present in both genders among French alpine 134 skiers and that a proper regression method could provide a robust solution to compensate 135 for this effect.

Methods 137

Studied population 138

Inclusion criteria 139

The population studied included all male and female French alpine skiers having 140 participated in at least one competition in Giant Slalom (GS), Slalom (SL)

Sample description 147

The studied population consisted of 21348 individuals ,7672 women (36%) and 13661 148 men (64%).Among these competitors, 2793,1917,1162 individuals participated to the 149 national U14, national U16 championship and international U18 championship 150 respectively. The distribution of performance and age for each category and gender are 151 presented in the table .  152

Data source 153

The performance data on the national and international circuit as well as dates of birth 154 were available on the website of the French Ski Federation: http://www.ffs.fr/ski-155 alpin/competitions/resultats. Alpine skiing performance is quantified using FFS (French 156 Ski Federation) points. The number of points scored on the day of the competition 157 depends on the overall level of the race and the time achieved in relation to the winner of 158 the race : ( 𝑇 𝑥 𝑇 0 -1). 𝐹, with 𝑇 𝑥 the runner's time in seconds, 𝑇 0 the winner's time in seconds and 𝐹 a discipline dependent factor. The data regarding of participation in elite youth 160 competitions is private internal data that of the French Ski Federation made available for 161 this study. 162

Data analysis 163

The age group categories in alpine skiing are determined according to the skiers' year of 164 birth. The U14 category is made up of skiers born in year 𝑦 -14 and 𝑦 -13 for the 165 season 𝑦 -1/𝑦. The U16 category is made up of skiers born in year 𝑦 -16 and 𝑦 -15 166 and category U18 is made up of skiers born in year 𝑦 -18 and 𝑦 -17. For example, a 167 skier born in 2006 will be in the U14 category for the 2018-2019 season. An individual 168 can be present in several categories if he has competed for several years. For Every year, 169 there are two French (FFS) youth elite championships in U14 and U16 categories and one 170 youth elite international (FIS : International Ski Federation) championships in U16 171 category. Only skiers selected by their regional committees in each category (U14, U16) 172 participated in these competitions. 173

Identifying the relative age effect 174

To have a large sample size in each group and a higher statistical power, birth dates of 175 participants in the elite young competitions (U14 and U16 FFS, U16 FIS) were 176 categorized into trimesters (January, February, March: Q1, April, May, June: Q2, July, 177

August, September: Q3, October, November, December: Q4). A Chi-squared test of 178 adequacy was used to compare the distribution of birth trimesters with the distribution of 179 birth trimesters for the entire French population over the study period. Historical public 180 data for the entire French population were obtained from the INSEE (Institut national de 181 la statistique et des études économiques): 182 https://www.insee.fr/fr/statistiques/serie/000436391. In the presence of a global effect, the normal distribution (of order 0.1,0.05,0.01) to determine the birth trimester over or 185 under represented. These residuals represent the standardized deviation between the 186 observed and expected values. They asymptotically follow a standard normal distribution. 187

In the absence of a relative effect of age, the distribution of trimesters of birth is assumed 188 to follow the distribution of the general French population, therefore a rejection of the 189 null hypothesis of homogeneity will identify a relative age effect. 190

Relative age effect in adulthood 191

The homogeneity test was performed on the birth trimesters of individuals present on the 192 international circuit (FIS) in adulthood (between 22 and 30 years) in GS, SG,SL and DH. 193 The FIS circuit is the highest level of alpine skiing in the world. If the distribution of birth 194 trimesters does not follow the French distribution, the relative age effect is present in 195 adulthood. 196

Rellocation RAE solution 197

The method was developed for the U14, U16 and U18 categories, individuals of the same 198 category were separated by their year of birth 𝑦(first year) and 𝑦 + 1(second year). Each 199 skier is represented only once per season, characterized by his best performance achieved. 200

For each year, the dates of birth have been categorized into months of birth (January to 201 December). At each age, a linear regression was performed between the month of birth 202 and the skier best performance (in FFS points). If the slope coefficient 𝛼 is significantly 203 different from 0, it is used as a calibration coefficient to rebalance the effect of relative 204 age. We calculated the "adjusted" points for each individual 𝑖 : 205

𝑃𝑡𝑠𝑐𝑜𝑟𝑟 𝑗 𝑖 = 𝑃𝑡𝑠 𝑖 -𝛼 𝑗 * 𝑘
with 𝑗: age, 𝑘 : Birth trimester 206 207 These adjusted points allow to compare several skiers independently of their month of 208 birth. The categories being composed in 12-month range, the coefficients will adjust the 209 performance within each of the group (1st and 2nd year). For example, the U14 1st year 210 and the U14 2nd year will have two distinct coefficients, and will allow each individual 211 to be compared with regard to his category (1st or 2nd year). We used a cross validation 212 method to check the robustness of our method. We cut our sample into two parts, one 213 accounting randomly for 75% of the sample data, and another part with 25%. The 75% 214 made it possible to calculate the calibration coefficients at each age category. Then, the 215 calibration coefficients was applied on the remaining 25% data sample. The RAE bias is 216 considered to be annulated cancelled out if the previous positive linear relationship 217 between performance and month of birth on this "test" sample was now absent. 218

Results

219

Identifying the relative age effect 220

The birth trimesters of participants in elite youth competitions does not follow the 221 distribution of the entire French population (proportion (%) = 24.4, 25.0, 26.1 24.4) 222 (Figure 1) (p<0.05). A relative age effect was identified in all youth competitions for both 223 genders. Skiers born in the first trimester are over-represented in all categories. 224 (proportion (%) = 33.3, 31.7, 34.6, 27.1, 30.7, 29.2 for men's U14 ; women's 14 ; men's 225 U16 ; women's U16 ; men's U18 and women's U18 respectively , p<0.01). Skiers born 226 at last trimester are underrepresented (proportion (%) = 16.7, 17.3, 15.8, 18, 16.6, 18.6 227 for men's U14 ; women's 14 ; men's U16 ; women's U16 ; men's U18 and women's U18 228 respectively, p<0.01) 229

The proportion observed in each discipline (GS, SG and SL) are represented in the 230 Supplementary figures. A different distribution of the French population is present in all disciplines (GS, SG and SL) for national competitions (FFS). The first and second 232 trimesters are overrepresented and the 3rd and 4th trimesters are underrepresented in all 233 men's and women's disciplines for U14 competitions. In U16, the 4th trimester is 234 underrepresented in all disciplines for both genders, and the first trimester is 235 overrepresented in SG and SL. 236

Relative age effect in adulthood 237

Figure 2 shows an imbalance in the distribution of the birth trimesters among adult skiers 238 as well compared to the French population. Individuals born in the first trimester are over-239 represented in men GS (proportion=29.4% p<0.05), GS female (35.2% p<0.01), SG male 240 (proportion=29.7% p<0.05), SG female (proportion=41.7% p<0.01), SL male 241 (proportion=28% p<0.1), SL female (proportion=34.7% p<0.01) and DH women 242 (proportion=47.5% p<0.01). Individuals from the last trimester are underrepresented in 243 GS male (proportion=20.5% p<0.1),GS female (proportion=18.9% p<0.1),SG female 244 (proportion=16.7% p<0.1),SL male (proportion=20.8% p<0.1), SL female 245 (proportion=16.5% p<0.1) and DH women (proportion=12.5% p<0.1). 246

Rellocation RAE solution 247

The methodology allowing performance to be adjusted by the relative age effect 248 developed in all categories for both genders in alpine skiing is presented for the densest 249 category in terms of the number of performances achieved, the male GS. A linear 250 relationship between performance and month of birth for each category in Giant Slalom 251 is illustrated in Figure 3. In each age category, the slope coefficient is different from 0 252 (3.27, 2.73, 2.09, 1.88, 2, 1.47 for men's U14 1st and 2nd year ; women's 14 1st and 2nd 253 year; men's U16 1st and 2nd year; women's U16 1st and 2nd year; men's U18 1st and 254 2nd year and women's U18 1st and 2nd year). This coefficient represents the potential gain in points for each additional birth month at each age in male GS. Calibration 256 coefficients as well as the confidence intervals are summarized in Figure 4 for each 257 category, gender and discipline. 258

After correcting the performance / age relationship using the calibration coefficient, the 259 RAE was considered in Figure 5 (∀ age 𝑗 : 𝑎𝑙𝑝ℎ𝑎 𝑗 = 0, 𝑝 > 0.05). All the confidence 260 intervals of the performance averages for each month of birth follow the linear trend with 261 a zero slope, except for the month of October for the U16 first year category. 262

Figure 6 also includes the calibration coefficients obtained with the methodology 263 presented for women (bottom) also and for the disciplines SG, GS and SL (in column). 264

For each category (on the vertical axis) is represented the calibration coefficient as well 265 as its confidence interval delimited by the two bars. If the value 0 (represented by the 266 horizontal bar) is within the confidence interval, the assumption that the calibration 267 coefficient is equal to 0 is not rejected. A null calibration coefficient means that the month 268 of birth has no influence on performance. All the calibration coefficients for the U14 269 categories (1st and 2nd year) are significantly different from 0. Conversely, 4 coefficients 270 are significantly different from 0 in U18 1st year and 1 in U18 2nd year. 271

Discussion 272

This study shows a relative age effect in all disciplines and age categories among French 273 alpine skiers. It also demonstrates that this effect lasts until adulthood in the international 274 FIS circuit, with a higher proportion of individuals born at the beginning of each year. 275

Based on such findings we propose a solution to adjust for this effect via a calibration 276 coefficient calculated for each event, age category, and gender. This option allows 277 coaches or regional selectors compare performances in a fairer way of several skiers born 278 in different months of the same year. 279

Several studies have investigated the relative age effect in other sports (Cobley et al. 280 2019;[START_REF] De La | Impact of the Relative Age Effect on Competition Performance in 441 Basketball: A Qualitative Systematic Review[END_REF][START_REF] Helsen | The Relative Age 461 Effect in Youth Soccer Across Europe[END_REF]Nolan and 281 Howell 2010;[START_REF] Tribolet | A 515 Multidimensional Approach to Talent Identification and Selection in High-Level Youth 516 Australian Football Players[END_REF]. This work is the first one to quantify RAE in French 282 alpine skiing disciplines. Development between 13 and 18 years has already been studied 283 in other studies [START_REF] Bottollier | Energy Demands in Well-Trained Alpine Ski Racers During Different Duration of 418 Slalom and Giant Slalom Runs[END_REF]. The strong physical component of alpine skiing, 284 with important energy needs partly explains why relatively older individuals find 285 themselves advantaged in performance. The link between physical and cognitive abilities 286 and RAE exists [START_REF] Huertas | Relative Age Effect in the Sport 468 Environment. Role of Physical Fitness and Cognitive Function in Youth Soccer Players[END_REF]) but some studies do not observe it (Carling et al. 287 2009). It also depends on the selection criteria developed to reach the high level in the 288 different sports. 289

The global relative age effect is probably due to the fact that selections at youngest ages 290 are mostly made on the sole criterion of performance. Individuals in the same age 291 category but months older have more time to further develop their physical, physiological 292 and psychological characteristics [START_REF] Helsen | The Roles of 464 Talent, Physical Precocity and Practice in the Development of Soccer Expertise[END_REF]. Diry et al. (Diry et al. 2020) 293 showed this dependence on maturity of physiological factors for rowing. Another study 294 reinforced this hypothesis, showing that the relatively older male competitors were 295 significantly taller and heavier than the younger competitors in youth winter Olympics 296 [START_REF] Raschner | The Role of a Relative 495 Age Effect in the First Winter Youth Olympic Games in 2012[END_REF]. Like in other studies, the RAE is exacerbated 297 in the youngest categories and wanes in the proximity of the senior categories (Johnson, 298 Farooq, and Whiteley 2017;[START_REF] Smith | Relative Age Effects Across and Within Female Sport Contexts: A Systematic 505 Review and Meta-Analysis[END_REF][START_REF] Votteler | Cross-Sectional and Longitudinal Analyses 519 of the Relative Age Effect in German Youth Football[END_REF], with a greater 299 presence of RAE in adulthood in men than in women (Boccia, Cardinale, and Brustio 300 2021). This persisting effect in senior categories can find explanations probably because 301 the skiers selected in the young categories benefit earlier from more investment in 302 trainings, more experienced trainers and training partners, and the knowledges about the 303 elite circuits, which facilitates their access to the highest level in adulthood. We therefore proportions of birth trimester in adulthood, yet present in different team sports such as 306 rugby [START_REF] Mccarthy | Initial Identification & Selection Bias Versus 480 the Eventual Confirmation of Talent: Evidence for the Benefits of a Rocky Road?[END_REF] or basketball (de la Rubia [START_REF] De La | Impact of the Relative Age Effect on Competition Performance in 441 Basketball: A Qualitative Systematic Review[END_REF]. If this 307 is not due to a statistical hazard, maybe team sports have more performance factors than 308 alpine skiing which may lead to more subjective judgments from coaches which allow 309 access even in late ages. Conversely, as in our study, we find a RAE in various individual 310 disciplines, notably throws in athletics [START_REF] Boccia | Elite Junior 413 Throwers Unlikely to Remain at the Top Level in the Senior Category[END_REF]Brustio et 311 al. 2019). These individual sports have performance determinants closer to alpine skiing. 312

Selection methods, which offer future benefits, may be carried out early in an athlete's 313 career, and leave little opportunity for new athletes. 314

The methods proposed so far [START_REF] Barnsley | Birthdate and Success in Minor Hockey: 404 The Key to the NHL[END_REF] have not made it possible 315 to reduce the biases linked to the RAE [START_REF] Nolan | Hockey Success and Birth Date: The 492 Relative Age Effect Revisited[END_REF]. These are indeed fair 316 measures, but proposals such as reducing the ages for regrouping or setting up quota 317 systems in elite selections are costly and difficult to implement from a logistical point of 318 view on an entire population of young athletes. An interesting idea would be to carry out 319 standardized and quantifiable physical, physiological, psychological and sociological 320 tests at regular intervals to monitor, on multiple indicators, long-term performance 321 (several years). However,unlike the method proposed in this study, this approach can be 322 complex to set up and requires individual monitoring over the long term. 323

Our method is easy to set up and allows for a faster adaptation of institutional practice 324 with regard to detection and selection of young people. This method is similar to what 325 has been previsouly suggested in swimming sprint disciplines [START_REF] Romann | Relative Age Effects in Athletic Sprinting 498 and Corrective Adjustments as a Solution for Their Removal[END_REF]326 Abbott et al. 2021326 Abbott et al. , 2020;;Cobley et al. 2019). The authors have used a linear regression 327 between performance and chronological age to determine the potential gain in terms of 328 performance according to the swimmer's birth period. Their adjustment procedures exponential (Berthelot et al. 2019), but for the sake of implementation simplicity, 331 compensation can be approximated through a linear relation over a 12 months period. 332

Both methods allow researchers and coaches to compare in an equivalent manner all the 333 swimmers of the same category regardless of the month of birth. This has two main 334 practical advantages: in the short term, individuals may have their performances 335 rebalanced in a fairer way for their real age. In the long term, individuals who were 336 previously unselected due to their period of birth may reintegrate into a long-term training 337 program for young skiers and the paths of high performance and thus be able to more 338 easily exploit their potential to reach the top level. The method is simple to apply, and 339 could be integrated directly into the coaches monitoring tools, providing more accurate 340 evaluation of the performance of young skiers. 341

Practical application 342

This study therefore makes it possible for the coaches and federal staff of young skiers to 343 become aware of the presence of a relative age effect in French alpine ski. We show that 344 it may be necessary to assess young athletes on potential performance and not current one 345 only. Second, this study proposes a method to better compare skiers of different 346 chronological ages, integrating the RAE in a given category. For example, if two skiers 347 have similar performances but at different chronological age, the calibration coefficient 348 method will give the youngest one a fairer evaluation, suggesting a higher potential in the 349 future. The proposed method does not recommend selecting athletes with a best potential 350 estimated by our model, it aims to provide this additional information to make a decision. 351

The person using the decision support tool, i.e. the coach or federal staff for example, will 352 then adjust these coefficients himself by his personal knowledge. It is therefore a first 353 step towards correcting the bias of the RAE in French alpine skiing. Skiers may no longer assessment of its young categories, that will better support long-term potentials by 356 rebalancing the chances from the beginning. 357

Limitations and perspectives 358

The present study has two main limitations. First, the calibration equations depend on a 359 performance dataset with a 15-year follow-up only. The volume of data is more important 360 for the young categories and decreases over time. Hence, there is a greater uncertainty 361 regarding the calibration coefficient in the adult ages. But the proposed method will get 362 more precise as new data entries will increase the robustness of the model. In addition, 363 the confidence interval of the coefficient makes it possible to add a nuance taking into 364 account the sample size and the outliers. The second limitation of this study is that it only 365 considers the age and performance variables to correct for the RAE. It would be 366 interesting to take into account other factors allowing to calculate biological maturity 367 [START_REF] Mirwald | An Assessment of Maturity from Anthropometric Measurements[END_REF] and its influence. It may therefore be worthwhile to include 368 biological / biometrical variables of interest (height, seat height, leg length … ) or other 369 parameters of ski, and compare the performances of skiers according to the progression 370 of these variables also. The method is mainly based on easily retrievable performance and 371 age data, it would be interesting to apply it to other sports whose performance is measured 372

quantitatively. 373 Conclusion 374

A relative age effect is present in all youth age categories of the French alpine skiing. In 375 the French national ski teams, skiers born at the beginning of the year are over-376 represented compared to those born at the end of the year. This effect is also present in 377 adulthood on the international elite circuit (FIS). We propose an easily implementable calibration method that corrects for such a bias. By adjusting the FIS points of each skier 379 according to their month of birth, it allows for a fairer and more accurate performance 380 evaluation. Ultimately, this tool could help rebalance the chances of reaching the highest 381 levels, regardless of the month of birth. 382

Review

Figure 1 :Figure 4 :Figure 6 :Figure 7 : 562 Figure 8 :

 14675628 Figures 528

J. G. Mitchell, James Salter,Michael Romann, and Stephen Cobley. 2021. "Maturity-

Michael Romann. 2019. "Removing Relative Age Effects from Youth Swimming: The

'Relative Age' Problem: A Systematic Review of Proposed Solutions." International