In this episode host, Jason Foster talks to Max Metral, the senior analytics manager over at Formula 1. Find out how they operate their data, dial up the use of insights to deliver value, the difference between being data-informed and data-driven – and which one is better to use in decision making.
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Apply critical thinking and use it as an insight in decision-making; which doesn’t mean to never pull the data, but make sure to have a second initiative in everything you do in terms of data.
[00:38] Max shares his role in Formula 1.
[03:21] What does the Formula 1 organisation look after?
[07:27] Where is the sports industry in terms of its use of data?
[14:58] Is there a difference between customers and fans? How are they defined and treated?
[22:25] Data challenges faced by Formula 1.
[25:11] What are some of Max’s favorite types of insights, or use cases, that added value to Formula 1?
[28:31] How Formula 1 sets up a team that delivers.
In sports, the level of someone’s understanding is whoever crosses the lines first wins. But it can get a bit more complex as it gets deeper. Then, you may start learning specific terminology. For example, Formula 1 sport is a mix between a car and technology race and a drivers’ race. It’s not just about who’s the fastest, but also who has the best car – among other things.
There are different types of fandom and maturity, especially if you’re international. You may find that some countries tend to be more mature in the fandom e.g. a Brazilian fan has had a race for ages. Whereas some Asian countries are more like grain, and fans have only had a race for a couple of years. So, there are different maturity cycles in different places.
Collecting and collating data is not that easy. You have to provide a valuable service to be able to get data from customers. The expectation level of free content is quite high, and the return for giving away the data is also quite high. One must be high up the value chain to interact in that way.
Everyone within the sports environment is doing a lot of research to try to better understand what makes someone give their data (like an email address), and to willingly subscribe to their newsletter which is socially important. They have to understand what they are going to get in return. The content must be likable for them to engage with it. Across the board, the content is the key piece of information.
The breadth and depth in solving a problem, as a team, is about asking these questions:
So for instance, when it comes to a project that is a bit more advanced than what you’re used to, you need to develop a support machine to make it work. Spending three or four months working on it exclusively, then having to update it and keeping it in shape, will not work well. That’s the time to look for the best partner to work with and trust.
Being data-informed (or data-guided) is better than being data-driven. Every piece of insight is a piece of information that you use when making decisions. But it doesn’t mean that you should disregard your gut instinct, which is also another piece of information that needs to be used as a weapon – a breadth of experience. Following the data strictly can lead to making mistakes. Right now with the pandemic, every historically driven database everyone’s looking at, is having issues.
That’s one of the reasons why your sample size might be big enough, or you might not be representative. There may be noises that you’re interpreting for signals. Also, every piece of information is put together by a human who can make mistakes; somewhere or other.