Data & Innovation, Performance | Dec 24, 2018
How to best blend data and intuition.

The Essentials:

  • The importance of understanding the needs across your entire athletic, coaching and performance department

  • Why data will never replace intuition

  • The need for flexibility in conveying the numbers

Cracking the so-called data code is a process that can drive coaches to distraction. What does the data say? Do we risk paralysis by analysis? Will this undermine everything I’ve believed throughout my career? Any data scientist worth their salt will tell you that it is less about supplanting years or even decades of accrued experience and more about supplementing that intuition with number-based evidence.

As data collection becomes ever more sophisticated we bring you eight steps, as part of our wider Performance Special Report, to consider as you look to embed smarter data usage in your organisation.

1. It has to start with the top level

It might sound obvious but as Nick Chadd, Head of Sport Science at the Manchester City Academy, says, when it comes to cracking the data code the support of the owners and directors “makes a 100% difference.” A support and understanding that stems from the upper echelons of the organisation ensures that Chadd and his staff have a suitable infrastructure around their use of performance data. “I couldn’t do it without our data insights team and the club has been absolutely fantastic; they integrate with us and there’s no barriers when it comes down to the people. We want to make this work and get the job done to the best possible standard and that goes across the whole club; you can see it in the way that people work together and it’s fantastic to be part of.”

2. Data must lead to intentional conversations

This is one of the things that causes tensions between data scientists and performance staff is the lack of appreciation for what the other does. Bryan Burnstein, Head of Performance Science at Cirque du Soleil, offers a template that could serve the world of sport well. “It’s not about how much data you collect – it’s about making the most of what you do collect,” he begins. “During that process it is important to explain to performers why we’re collecting the data, what is going to be collected and who’s going to have the opportunity to look at it.

“We don’t want to dehumanise a decision; data should lead to insights and it should lead to good intentional conversations about what’s going on with the individual. We’re dealing with human performance and we have to keep the human at the centre of that.”

3. The focus needs to be on core efficiencies

There is validity in pursuing marginal gains but data is best used fine-tuning your core efficiencies. “I’d rather do some simple things and do them really well and effectively where we can rely on the interpretation of that data,” says Nick Chadd. “That way it’s repeatable and more robust – rather than focussing on one percenters, it often comes back to the other 99%. Are we sure we’re doing this 99% as well as we can?”

Of course, it is important to keep evolving but an organisation cannot lose sight of the overall goal. Bryan Burnstein says: “If we’re changing something it’s because it gives us better insight and we’re not just changing for the sake of using some new technology or platform.

“It really has to optimise our core efficiencies. Otherwise, if we’re going to evolve something then we ensure there’s an appropriate transition, that we still have enough data to be able to be able to use that to guide our decision-making process and then make sure it’s still relevant to our content.”

4. Nothing can be considered in isolation

For good, intentional conversations to take place around core efficiencies then no data point can be considered in isolation. As Jamie Tout, Strength & Conditioning Coach with the New Zealand women’s national rugby team, says: “We don’t go through a tick box exercise.” No decision around the athlete can be based on numbers alone. He continues: “We don’t just go ‘you should do three gym sessions a week’; you don’t have to and sometimes you want to talk to the athlete about something, such as their sleep.”

Astute data users can use the numbers as a means to have a broader conversation around an athlete’s wellbeing. “It’s all relative and so we have to make sure that none of our numbers are ever looked at insolation. The players these days have a real hunger to understand why we’re doing things.”

5. Get a feel for your coaching staff

It is important for data collectors and interpreters to take time to know how best to convey actionable data with different coaches. “It’s definitely a challenge,” observes Tucker Zeleny, Director of Sports Analytics and Data Analysis at the University of Nebraska. “You go through a couple of seasons with a coaching staff and gain a feel for what they want and once you’ve built that trust you can get into a routine.” Zeleny and his colleagues work with 13 teams across different sports and so their approach is tailored for each and every one. A similar method has been adopted at the Manchester City Academy where teams range from boys aged nine and under through to the under-23s. “We take a different approach in every case,” says Nick Chadd. “Don’t forget that many coaches have a different background – so we have to be mindful of their understanding of data as well. Sometimes we assume they’ve been party to the same background or exposed to the same line of data as a sports scientist and that often isn’t the case. What I’m finding is that the most powerful data is the most simple; it isn’t always digestible for people and you cannot always see the wood for the trees when there’s an abundance of data.”

6. Provide your coaches with flexibility when using data

While alignment and continuity are paramount in data collection, there should also be scope for coaches to use data as they see fit or for new coaches to put their own spin on an analytical approach. As Nick Chadd explains: “They may want to come in and put their own slant on things and so the spine of alignment stays the same but they’re able to divert off and explore something else at the same time in more depth and detail. They’re absolutely allowed to do that and put their own individual flair on that.”

Cirque du Soleil, as Burnstein observes, take a similar outlook. He says: “A company like ours has to balance central intelligence with local need. We’re putting in minimum essential requirements across the board and we also provide latitude and flexibility so that if there are particular pieces relevant to the acts or disciplines on their show or provide utility to the experts that are on the show then they have the right to go above and beyond – but not in place of what we already require across the company.”

7. Take a scaled approach to datasets

One thing that is clear about data is that not every stakeholder needs to prioritise the same data points. Jamie Tout says: “There’s some things I’ll share with coaches from a coaching level and they’ll understand those numbers; there’s some things I’ll share with the sports scientists and that’s a greater level of detail; and then there’s information you share with the player.” Everyone involved with the Black Ferns is aware of the team’s ‘gold standards’. “We have gold standards around what that looks like in a game performance and how we can better use that in the training environment and, realistically, that’s what we capture these numbers for.”

At Manchester City, for example, Nick Chadd explains that the club has adopted a scaled approach in its academy. “They may want to come in and put their own slant on things and so the spine of alignment stays the same but they’re able to divert off and explore something else at the same time in more depth and detail. They’re absolutely allowed to do that and put their own individual flair on that.”

8. The value of real-time data

Real-time data collection can be useful tool for making training more intentional. Nick Chadd says: “The reason we use real-time data isn’t necessarily always from a load management perspective, it’s actually to generate an output or affect the training environment. We make it competitive and, therefore, can make our training more intentional.”

3 things to consider:

  1. Get to know the needs, hopes and expectations of your athletes, coaches and performance staff.
  2. Ensure that every dataset collected works towards the collective goal of maximising performance.
  3. The human aspect cannot be discounted at any cost.


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