Swimming Canada’s Stat-Based Talent ID System Predicted Oleksiak

SwimSwam’s 2016 Swammy Award winner for Canadian Swimmer of the Year and Breakout Swimmer of the Year, Penny Oleksiak, looked like a seasoned Olympian in Rio when she raced away with 4 medals. At just 16 years of age, Oleksiak scored two bronze medals as a critical member of her nation’s 4x100m freestyle relay and 4x200m freestyle relay.

Oleksiak also wowed the world when she stole silver only behind Swedish World Record Holder Sarah Sjostrom in the 100m butterfly individual event, then capped off her campaign with the coveted women’s 100m freestyle gold medal, tying American Simone Manuel. As the Rio audience and swim enthusiasts around the globe asked themselves where this teen phenom came from, Iain McDonald, Senior High Performance Manager at Swimming Canada, was feeling validated.

Two years ago, when Oleksiak was just 14, the Toronto swimmer was identified by Swimming Canada as a medal potential for the next 5-8 years. As such, she received funding to help develop the talent her performances were displaying. But, at a time when her highest world ranking was 319th, many would ask what made the Canadian powers that be take a special interest?

It’s all about the data. McDonald explained recently to The Star that Swimming Canada uses a sophisticated, statistics-based system that helps identify emerging athletes. Their ‘on-track times’ system uses ‘decades of data from global competitions and the progression rates of the world’s best swimmers to predict race times that a swimmer needs to meet, at a particular age, in a specific event, to be on the path for an Olympic medal at the 2020 and 2014 Games.’ (The Star)

The data began being collected domestically in 2014, then grew to an internationally based database shortly thereafter, courtesy of Canadian Tires’ data analytics division. “It better defines what it takes to be a world-class athlete, not just the best athlete in Canada, and that’s our task,” says McDonald.

However, as with any method of progression, there are trip-ups along the way where things don’t go according to plan. As for Oleksiak, the now internationally famous young face outperformed even the data’s expectations. She was originally targeted for the 2020 Games, when her performances just months before Rio started exponentially improving that the stats changed course to identify her as a potential medalist in 2016.

“They’re humans, it’s not point A to point B in a nice curve, they trip along the curve and it’ll be bumpy but the idea is that over time, hopefully, this information will help us . . . identify the pool of athletes that could actually have international success,” McDonald said.

Prior to the implementation of this statistics-based system, athletes had been eligible for Swimming Canada‘s development level support once a world ranking with the top 150 was achieved. This was regardless of the swimmers’ ages or where they were at in their careers. In essence, new and old swimmers alike were held to the same standards for funding.

This has changed drastically with the new system, as McDonald says, a swimmer “could go year on year hovering (in the top 150), and it’s a bit cutthroat but you’re investing in somebody who is never going to get there.

“We have to try and be better at identifying the athletes and supporting them because we have that limited pool.”

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Prickle

“past performance is no guarantee of future results” – mutual fund disclaimer. The long term predictions in sport, especially in swimming, is even less reliable than VERY SOPHISTICATED models of predictions of stock market behavior.

coacherik

Would be curious, without having to name names, how successful this was for 2016. Was there any other athlete that fit this predictive model?

Murica

Its pretty much the same thing. We’re all human and so any organization of any kind is human and fallable at its roots.

marklewis

I read somewhere that Australia does a similar thing.

They take your event times when you are 14 years, and then calculate a ratio of how close you are to records or cutoffs.

If you are within a certain percentage (like 80% of the cutoff time), then you can be part of their Development Program.

mikeh

I bet no one needed statistical analysis to tell that Peggy Oleksiak was going to be great. Just a guess on my part. Logical progression can be part of the equation but there are a lot of intangibles, and no one can predict how someone will develop, especially distance athletes. There are distance runners who don’t hit their stride until their 30’s. The fact that swimmers usually make it (or don’t) by the age of 21 may be a product of our determination to hurry the process, and get swimmers in international competition, whether the swimmer is ready for that or not. Just a theory on my part.

Bo swims

Penny
… not Peggy…. guess you missed all the Penny/Rio memes

Not Affiliated

My guess would be the $ available to many runners that can support them or keep them in the sport. If you look at the swimmers sticking around until their 30s they are all making $.

About Loretta Race

Loretta Race

Loretta grew up outside Toledo, OH, where she swam age group and high school. Graduating from Xavier University, she stayed in the Cincinnati, OH area and currently resides just outside the city in Northern KY.  Loretta got back into the sport of swimming via Masters and now competes and is …

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