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Five Data Analytics Lessons You Can Learn From The Premier League

 

In this blog – originally published on the Insights blog of our technology alliance partner, Exasol – we hear from Matthieu Lille-Palette, Senior Vice President of Opta. Over the past decade he’s been a leading innovator of data analytics in sport – working with clubs, sports betting and media organizations across the Premier League and other global sports federations.

By: Matthieu Lille-Palette

If someone told you a few years ago you could learn a lot about data strategies from football, you would most likely have wanted to relegate them to the subs bench. But as official statistics partner of the English Premier League, at Stats Perform we’ve seen that data and sports truly belong together. And it’s true that there are surprising parallels between the data challenges of a football club and those that you may be facing in your own organization.

So, here are my five key lessons from football which will give you the freedom to get the most long-term value from your data strategy.

1. Data is just numbers without human interpretation

Data alone, in the context of both football and business, is not a silver bullet. It helps you do your job better and make better decisions. But you will always need people to work with the data – to interpret meaning, communicate findings and relate them to overarching strategies.

In football, the wealth of GPS tracking data, player performance stats, injury susceptibility data, etc., are essentially useless without data scientists to translate them all into digestible information. That insight then gets relayed to the manager, coaches and medical team to inform the team selection for the following match.

The same is true for your organization. The key is how you use data to achieve your unique goals, because unless you work to interpret data, ultimately, it’s just numbers. This applies whether you crave better decision-making, want to use data to gain a competitive advantage, improve customer engagement – or simply want a greater return on investment.

2. Using data effectively requires a team effort 

In sports or business, using data effectively requires you to build a team of the right people. Being a team that’s always pushing to seek out new information and ideas can ultimately give you the edge.

Take current Premier League champions Liverpool’s success over the last few seasons. Not only do they have a world-class manager in Jurgen Klopp and a squad of top-level players. But in the back-end, there’s an entire team of world-class researchers and statisticians crunching the numbers helping Liverpool to perform their best. And crucially there is a synergy between Klopp and that data team. Not only is he on board with incorporating data science in his team’s decisions — he embraces it.

Likewise, with the right investment in data scientists and professionals in your team, you can decide what areas of your data to focus on and how to interpret it best. You can prioritize and make smart decisions that help you stay competitive.

This has been particularly important during the pandemic, as organizations have needed to be agile and shorten decisionmaking cycles – requiring not only access to data but the right people who can help to quickly visualize it all as usable and actionable insights.

3. The importance of matching great data science with great leaders 

Neither football nor business have always been historically data-driven. The strengths of some of the great managers of the past, like Sir Bobby Robson or Sir Alex Ferguson, lay in their intuition and man-management skills. But data gives us an opportunity to be even smarter and more objective. It doesn’t mean we should ignore human intuition. They can complement one another.

For all the data at their disposal, a manager will almost certainly rely heavily on their intuition in the heat of a match, when it’s neck and neck and they’re looking to the subs bench and deciding who to bring on to give the team the decisive edge. But when it comes to team selection before the match, the data might inform them that certain players are showing signs of burnout or at risk of a potential injury, giving them insight that their intuition could never provide.

With the right strategy and leadership, you can get the best from intuition and great data science. This rings true in business where you need to treat data as an asset and define a strategy around how your entire organization uses it – with repeatable and common methods, practices and processes. And that’s why innovative data-driven companies are increasingly investing not just in data scientists but in the role of a CDO (Chief Data Officer) to bring all this together and put data at the heart of organizations.

Visualizing data makes it easier to build narratives and explain complex scenarios. For example, it can capture the wonderful creativity of Kevin de Bruyne’s PL season in 2019-20.

4. Simple messages and the role of data storytelling

We need to speak the language of those who need the data the most. In football, the clearer the message, the more likely it is to have an impact on the tactical decisions a manager makes; the player investments a club makes; or the on-field actions of the players.

Visualizing data, or ‘data storytelling’ is a common theme in business too, especially within organizations that understand that the more people who understand the data they use, the easier it is to have a positive impact. Data literacy is, therefore, more important than ever. If more employees in your club or business can understand and interpret data on the fly, they’ll be able to make faster, more informed decisions.

Data storytelling uses our innate ability to learn through narrative to uncover insights. This helps spark conversations and curiosity among data communities. But beyond simply communicating insights more effectively, data storytellers help evangelize the importance of data across an organization, by actively demonstrating how to find and communicate insights in effective ways.

5. Real-time predictive metrics are no longer just for performance

Football clubs now have well-established models which use the predictive power of data and AI to analyse the performance of both players and the club as a whole. But this is set to evolve into the realm of mental health and wellbeing. For instance, micro-surveys can be used to check on the energy levels and even happiness of staff.

The question is, could our organizations start to use the same data analytics to reduce office stress in an always-on age of high-pressure deadlines, job insecurity and remote working? It definitely seems likely, but this will have to be acutely balanced with increasing concerns over data security.

What next?

Whether you’re a football club or a business from any other sector, the same principles apply. Data alone isn’t enough. You need people – good people – to decide what to focus on and how to interpret it. That focus, and the goals you set, come down to what you want to achieve in the short-is term. And that should tie into your larger organizational strategies for the long-term.

Get this right and you’ll be top of the league, no matter your industry. If you’d like to hear more from me about data analytics in the game of football – to help you draw parallels with your own organization’s data challenges – check out my appearance on the DataXpresso podcast.


This piece originally appeared on Exasol’s blog.