An Investigation of Relative Age Effect in Youth Football

Background. Youth sport programs typically group children based on an annual birthdate cut-off (e.g., U8, U9, U10). However, research indicates this results in relative age effect (RAE), the overrepresentation of children born early in the selective year and underrepresentation of children born later (Barnsley et al., 1985), which inadvertently advantages older and more biologically mature children (Hancock et al., 2013). Sports in which physical size contributes to success often have a more pronounced RAE from youth to professional levels; however, studies of American football at the professional level have not observed RAE. This absence is speculated to be a product of youth football policies that use additional factors, such as weight and skill level, coupled with age to group children (Wattie et al., 2015). Interestingly though, RAE has not yet been studied in youth football. Therefore, the purpose of this study was to determine the extent to which RAE is promoted or diminished when grouping children by various developmental factors (i.e., age, weight, and skill level). Method. Data was acquired from a mid-Atlantic youth football registration database that used a standardized weight matrix to organize children of various ages and grouped them into teams based on their skill assessment. A purposive sample (N = 1,265) of 8-13 year old boys was extracted and classified into quartiles based upon birth month for data analysis. Multiple chi-square goodness of fit tests were run using expected values of live birth from the CDC. Results. The mean age of the sample was 11.0 years. Chi-square goodness of fit tests indicated significant differences (p \u3c .001) of departures from expected frequencies when independently categorized by age only (AO), weight only (WO), and skill level only (SO). However, there were fewer significant departures when categorized by age + weight + skill level (AWS). Discussion. The findings from this study provide youth sport programs with needed data to for considering alternative organizing practices were grouping children together. Specifically our study indicates using a singular developmental criterion to group children together (e.g., by age, promotes RAE, whereas a more robust developmental approach appears to alleviate RAE. (e.g., age + weight + skill level). This is significant because the absence of RAE provides children with the opportunity to develop sport-specific skills in a fairer environment among their peers with more developmentally appropriate instruction (Wattie et al., 2008), which promotes more positive sport-based, physical activity experiences for them

Relative Importance of the Fun Factors: Pattern Matched Perceptions among Players, Parents, and Coaches

Background. The FUN MAPS are evidence-based blueprints for the fun integration theory, youth sport’s first-ever stakeholder-derived, theoretical framework for promoting fun through structured skill development and competitive play (Visek et al., 2015). Developed from the direct input of players, parents, and coaches, the FUN MAPS identify and quantify the importance of 81 fun-determinants within 11 factors (i.e., Positive team dynamics, Trying hard, Positive coaching, Learning and improving, Game time support, Games, Practices, Team friendships, Mental bonuses, Team rituals, and Swag). However, the FUN MAPS are based on the combined input from players, parents, and coaches. Efforts to promote the most fun for children requires their priorities to be considered independent of adults and comparatively to one another within and across sex, age, and competition level. Additionally, to elucidate exact points of consensus/discordance between children and adults, players’ priorities should be considered alongside parents and coaches’ perceptions. Therefore, the purposes of this study were to use pattern matching displays, useful for determining within and between group differences, to identify the extent to which children’s (n = 142) reported importance of the 11 factors evolves throughout their development and how their perceptions compares to adults (parents, n = 57; coaches, n = 35). Methods. The Concept Systems® Global MAX license was used to produce pattern match displays for consensus analysis (r). Mann-Whitney U tests were used to identify the fun-factors on which groups significantly differed; and, the Fisher r-to-z transformation was used to determine whether consensus between pattern matches were significantly different from one another. Results. Results indicate remarkably high degrees of consensus among children, regardless of sex, age, and competition level comparison (r’s = .90-.97). Consensus was also high among children and parents (r = .93); however, it was significantly lower among children and coaches (r’s = .68-.93). Pattern matches are displayed using sophisticated, illustrative ladder graphs. Discussion. Novel findings from this study provide a more complete context for understanding children’s fun priorities across all 11 fun-factors. Overall, with respect to players, results support the gender similarities hypothesis, rather than the gender differences hypothesis and the other age and competition level assumptions that have long guided organized youth sport. The discordance observed between older players and coaches is of great concern to children’s continued participation into adolescence and may account for the dramatic dropout that occurs around the age of 13. Best practices for optimizing children’s fun sport experiences are forwarded