A proposal for a maturity index to help identify early- and late-maturing athletes in local clubs and teamsBeing able to identify early- and late-maturing athletes gives coaches one more tool they can use to understand the demographic forces at work in their athlete pools
This is the revised Maturity Offset Calculator, which now includes the maturity index formulas discussed in this article. The measurement protocols have also been revised.
Maturity Offset Calculator (revised 21 July 2021)
Maturity Measurement Protocols (revised 21 July 2021)
National sport governing bodies (NGBs) worldwide face unnecessarily low capitalization rates of their athlete pools. NGBs are discarding potential high performance athletes long before anyone has a chance to know if those athletes are any good. While the reasons for this vary, not accounting for growth rates among the youngest in the athlete pool and misunderstanding the demographics that shape the athlete pool are the most likely causes. The specific factors include the relative age effect (RAE), and variable growth rates in youngsters that result in some athletes maturing much earlier or later than their peers.
In a recent letter I discussed the impact the RAE can have on an athlete population and how these effects are built into the system when fixed annual registration dates are used to determine age. The RAE is based on a perception of ability masked by birth date. Judging ability in young athletes without consideration of birth date produces a bias toward believing that relatively older athletes are better athletes. Fixing RAE issues within an NGB involves overcoming administrative inertia so while the 'what to do' may be clear getting it done is hard.
The long-term effects of relative age bias are widely documented, but the effects of being an early- or late-maturing athlete are not as well understood and rely mostly on anecdotal evidence. Varying maturity rates are sometimes addressed in research that examines sport dropout and burnout.
I've written about this in a previous letter:
Late-maturing athletes on the other hand are presumed to have somewhat rockier early sport experiences because they receive little of the coaching attention or praise given to their early-maturing counterparts:
The early-maturer receives early accolades and enjoys the sport more than slower maturing athletes. But when maturity begins to level off, the early-maturer may become frustrated when those he easily outperformed just a short time ago begin catching up with him in terms of size, speed, strength, and skill; leading some to speculate that early-maturing athletes may be more susceptible to dropping out of the sport at this point.
The late-maturer is more susceptible to dropout in the beginning of their sport experience because of their general lack of early success. They are smaller, slower, and less skilled than the early-maturers on the team, so their experience is assumed to be less enjoyable. But if they stick with the sport things gradually improve for them when their growth catches up with their early-maturing teammates.
Both of the above scenarios are speculation. There's little evidence, aside from vaguely designed sport dropout research, to back them up. However, this is one of those things that sound like it should be right--and it may be--we're just not sure of it. But whether it's right or not it would be helpful if coaches were able to identify athletes on their teams who are in the tails of the maturity distribution. Some might argue that it's easy to see who is a late maturer and who isn't. But growth is not always obvious in terms of size so having a metric can identify varying maturity rates among athletes.
The maturity index
Is there anything we can do within our teams or clubs that addresses maturity rates? We have tools to identify possible relative age bias; can we identify early- and late-maturers within local athlete pools? I think we can.
The easiest way to do this is to create a maturity index for athletes in a particular club, team, or training group. Sportkid Metrics has modified its Maturity Offset Calculator so that it also calculates a maturity index. This index ranks athletes on the spreadsheet relative to others on the same spreadsheet.
Here's how it works:
- Collect measurements for each athlete as noted on the Maturity Measurement Protocols and enter the collected data into the Maturity Offset Calculator.
- An average APHV is calculated by sex.
- The maturity index for each athlete is calculated by subtracting the athletes APHV from the average APHV for athletes listed on the spreadsheet.
- Two shades are used for each maturity category. Light green indicates athletes who are three months behind the average APHV relative to others. Darker green shows athletes six months or more behind. Likewise, light red is for athletes who are three months ahead, darker red is for those six months or more ahead of normal.
- A negative index value indicates an early-maturing athlete. A positive index value indicates a late-maturing athlete.
- The colors are done with conditional formatting on the spreadsheet and set to ±0.25 years (3 months) and ±0.5 years (6 months) so anyone interested in experimenting can make changes.
It's important to note that the maturity index is relative to athletes on the spreadsheet. So coaches can identify maturity rates among athletes in their own clubs or specific training groups, or in any group of athletes they have appropriate data for.
Coaches will also notice that as athletes are added to the list or as modifications are made to athlete measurements the maturity index may change. This is due to the relative nature of the calculation and because only local data is used. The comparison is local because being an early- or late-maturing athlete has to be relative to something and it makes sense to compare athletes with others they will learn, train, and compete with and where any effects of varying maturity rates will be felt.
The maturity index is an experiment, so I would like to hear from anyone who tries it with their own club. Sportkid Metrics works with data like this all the time so improvements to the online tools will be published when they are available.
Being able to identify early- and late-maturing athletes gives coaches one more tool they can use to understand the demographic forces at work in their athlete pools.