Using Training Data For Long Term Player Development
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Imagine if you were given the task to find the next John Terry, Andy Murray or Katie Taylor. You know that they’re out there somewhere kicking a ball, returning a serve or winning a bout among thousands of other kids their age. While some look like future champions at age 7, it’s unknown what they’ll be like at 17.
Finding a group with some genetic gifts and then developing them through years of physical and mental growth demands access to new tools with one secret ingredient, data. Just ask Ben Smith and Marco Cardinale.
In a recent interview with the Big Data Insight Group, Ben Smith, Head of Development Performance Systems for Chelsea Football Club, commented, “The professionalisation of sport has been dramatic over recent years and it’s only going to continue. There’s a huge amount of money and drive within the industry today; the rewards are massive for those getting things right and they’re substantial for getting it wrong – data analytics helps us ensure we do the former and avoid the latter.”
In this talent identification and development process, breaking down the data on hundreds of prospective youth players falls into two categories, quantitative and qualitative. The quant side measures and tracks objective data points from devices worn by the player or observed metrics like timed drills and strength workouts. Just as important, the qualitative data tracks the subjective opinions and observations of the player and coaches related to the perceived progress of their daily training.
In a recent interview with the Big Data Insight Group, Ben Smith, Head of Development Performance Systems for Chelsea Football Club, commented, “The professionalisation of sport has been dramatic over recent years and it’s only going to continue. There’s a huge amount of money and drive within the industry today; the rewards are massive for those getting things right and they’re substantial for getting it wrong – data analytics helps us ensure we do the former and avoid the latter.”
In this talent identification and development process, breaking down the data on hundreds of prospective youth players falls into two categories, quantitative and qualitative. The quant side measures and tracks objective data points from devices worn by the player or observed metrics like timed drills and strength workouts. Just as important, the qualitative data tracks the subjective opinions and observations of the player and coaches related to the perceived progress of their daily training.
Collecting gigabytes of data is only step one. Without a way to summarize and visualize the data in a format that is easy for coaches and players to understand, the effort is wasted. “Numbers are really, really dry and people from a coaching background, even the modern coaches, are not often data driven,” according to Smith. “If you can present the numbers in a way that means they quickly understand its direct relevance to the things they’re trying to achieve then they will appreciate the significance of what it’s telling them.”
While managing athlete development data for one sport is difficult, coordinating progress across multiple sports introduces an even greater challenge. That job falls to Dr. Marco Cardinale, Head of Sports Science and Research of the British Olympic Association. He recently described to the MIT-Sloan Management Review some of the complexity and hurdles Team Great Britain has to overcome to keep each sport’s program moving forward.
“The biggest problem we have in sport is the difficulty in collecting data,” said Cardinale. “The real analytics we are interested in is the ability to understand what athletes do on a daily basis to be able to affect their training programs, and that’s where the difficulties occur. It’s very sport specific.”
Like the Chelsea staff, Cardinale understands the need for athletes and coaches to track the ebb and flow of daily training. “In my view, the coach and the athlete are the main unit able to deliver success,” he commented. “I think the biggest edge will be if they understand themselves better. In too many sports, athletes train either too much or not too much, and it’s because they have to gauge what they do on a daily basis against their feelings or what the coach sees. I think if they have more data about themselves, they can have an edge because they can be smarter in the way they train.”
Improving performance with data analytics is not a quick-fix solution. Identifying patterns and trends related to training requires multiple data points over an extended period and a commitment to its long-term use. As Cardinale concludes, “It’s a long journey, which means a club or an institution really need to invest in a project for at least a good three to four years. The power of data resides in good longitudinal information rather than snapshots.”
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