• Bigger is not always better.

• Smaller players are not necessarily most at risk of injury.

• General normative data should not be applied to specific populations.

 

Participation in sport seems to decline from adolescence into early adulthood ¹. Adolescence is a critical period in a child’s development across all areas (physical, emotional, mental, etc.), and some care should be taken when letting one’s child enter sporting situations. Also, the rate of development may vary for children of the same chronological age. This has implications for the physical maturation of the child. For example, two 14 year old boys may have a 50cm difference in height or a 35kg difference in weight. The rate of development varies in timing, duration, and tempo. Based on this, it is high likely that young sportsmen and women would come up against opposition that are developmentally different to them during competition. This raises some questions…

 

“What are the permutations of mismatches in physical attributes and performance?”

Krause et al. (2015) tested 485 male adolescent (u12-u15) rugby union players in the first half of the 2013 season in Australia. The data collected were demographic information, body mass, stature, counter movement jump, and sprint speed. In comparison to their positional counterparts, forwards were taller and heavier, while backs had better sprint times and relative power. Body mass, speed, and power data were divided into age-specific tertiles (thirds) for better comparison. Only 6% of players were in the highest tertile for all three of these attributes, while only 4% were in the lowest tertile. Therefore, only few players had a distinct advantage or disadvantage over their peers. While this remains a complex issue, being physically bigger does not necessarily translate into performance advantages.

“Should my child be playing in a different age group if he/she is considerably bigger or smaller?”

In short, probably not. As mentioned above, very few players had distinct advantages or disadvantages across body mass, speed, and power. Also, Krause et al. study found that the heavier players reported missing games more frequentlydue to rugby-related injuries compared to their smaller counterparts. The disproves the assumption that lighter/smaller players may be at higher risk of injury.

“How applicable are existing criteria for making these decisions?”

When compared to general age-related normative data for body mass, it was found that u12-u13 rugby players were in the 50th-75th percentiles, while u14-u15 players fell above the 75th percentile. What this means is that we should not be using general population normative data to inform decisions for specific populations, in this case, youth rugby players. Keeping in mind that this data were collected in Australia, and might not apply to other countries, Krause et al. suggest that players below age-specific body mass set-points can be considered for “playing down”. See below.

u12 <34.79kg

u13 <41.31kg

u14 <50.76kg

u15 <57.28kg

 

References

1. Australian Bureau of Statistics. Participation in sport and physical recreation, Australia, 2011–12 – characteristics of persons who participated, 2013.
   Available at: http://www.abs.gov.au/ausstats/abs@.nsf/Products/4177.0~2011-12~Main+Features~Characteristics+of+persons+who+participated?OpenDocument [Accessed 23/09/2015].
2. Krause, L. M., Naughton, G. A., Denny, G., Patton, D., Hartwig, T., & Gabbett, T. J. (2015). Understanding mismatches in body size, speed and power among adolescent rugby union players. Journal of Science and Medicine in Sport, 18(3), 358–363. doi:10.1016/j.jsams.2014.05.012

 

Jason Wulfsohn

MSc Candidate

University of Cape Town

 

 

 

No.

In a recent review published in Sports Medicine, leaders in the area Nick Wattie, Jorg Schorer and Joe Baker discuss how birth date cannot determine sport participation and success. At least on it’s own that is.

The relationship between birth date (usually based on month) relative to peers in an age-grouping year and the attainment of sporting success is known as the Relative Age Effect. The typical explanation for this phenomenon is that children who are relatively older within an age-grouping year are physically more mature. This physical maturity is then mistaken for ability.

Wattie, Schorer and Baker argue however, that sport participation and sporting success is not that simple, but rather a product of the interaction of a number of factors – birth date being just one.

To help us understand this complex issue, our colleagues apply what is known as the constraints-based model. The model basically describes and categorises factors (or constraints) that govern our actions. These categories are individual constraints (for example, height, weight, birth date, etc), task constraints (for example, demands of the sport, goals, performance context, etc) and environmental constraints (for example, policies, family, coach influence etc). These constraints can both restrict or facilitate. Think about driving a car to get from point A to point B. The car represents the individual constraints; we can only go as fast as the car allows us to go. The task constraints represent the driver of the car; his goal is to get from point A to B. And environment = the road, its rules and other obstacles.

The key point though is that all these constraints need to interact with each other to produce its effects. In our driving analogy, we won’t get to our destination if the car, driver or roads are not functional. Therefore, birth date (an individual constraint) itself is meaningless in terms of predicting sporting success. Birth date gives rise to the effects of relative age however, when it interacts with an age-grouping policy for sport (an environmental constraint). Add to this the physical demands of the sport (a task constraint), and boom, children born in the earlier part of the year seem to emerge as having an advantage.

If anything changes during our trip from A to B, the tyres on the car, or the road is bumpier than anticipated, it will affect the other constraints (for example the driver) along the way. Likewise, if we change anything at the individual, task or environmental level, it will affect the other constraints. Lets not also forget that things will change over the journey, and every journey from point A to B is different – this speaks to the plasticity and diversity of the Wattie, Schorer and Baker model.

Although theoretical for now, the proposed model invites us to think differently about the Relative Age Effect phenomenon. This has implications for the way we collect, analyse and interpret the relationship between birth date and sporting success. And of course, no need to plan giving birth within the 1^st 3 months of the year for your kids to be good at sport.

For the full article, go to – http://link.springer.com/article/10.1007/s40279-014-0248-9

Wattie, N., Schorer, J., & Baker, J. (2015). The relative age effect in sport: A developmental systems model. Sports Medicine, 45(1), 83-94.

Sharief Hendricks

A study by Drs John Stoszkowski (@JohnStoszkowski) and Dave Collins (@DaveGM4P) highlights the sources of knowledge for coaches. This full study is available free at the following link currently: http://www.tandfonline.com/doi/full/10.1080/02640414.2015.1072279#.VcSqR3j3Dbw

It was also brilliantly summarised by infographic guru Dr Yann Le Meur of YLMSportScience (@YLMSportScience):

In summary, it seems that most coaches prefer informal learning (as opposed to formal or non-formal) which includes peer discussion, watching other coaches, books and websites, online social networks and YouTube. As study author Dr John Stoszkowski says about the findings in a recent tweet: “Hence [the] need to teach/support coaches to approach info in a critical way in order to make sure self-directed learning optimal“!