Max serves as Senior Analytics Manager for Formula 1®. He is responsible for leading F1’s Business analytics department which aims at unlocking commercial value and informing decision-making for both B2B (sponsorship and TV rights pricing strategies and contract negotiations, unlocking value for our promoters through behavioural economics, etc.) and B2C departments (OTT churn management, increasing fan engagement through Digital and CRM activities, etc.). Prior to joining Formula 1®, he has been working for City Football Group across their various soccer clubs such as Manchester City FC, New York FC, and Melbourne City FC, managing business analytics and data science projects across diverse fields such as fan relationship management, sales, marketing, ticketing, and sponsorship. Max also has had his share of hands-on experience working on data analytics projects for worldwide companies such as Accenture, Michelin, and Adidas.
1. In your opinion, how has the Data Analytics landscape evolved over the years?
Within F1, there is no denying that teams are at the forefront of the Data Analytics revolution. They are always looking for the next marginal gains to give them an edge over the competition. This is what we called “Sport Analytics”: using data in order to improve the performance on the track, or on the field for any other sport. For most organisations within sports, this is their key area of interest. How can we be more proficient? When looking outside of just F1, this equates to a lot of progress in the fields of training, scouting, tactical analysis, and even medical analytics. Not every sport is the best in each of these four areas; for instance, while some other sports like football have become very efficient at medical analytics, F1 is very much focused on the tactical aspect: “When should we do our pit-stop? How does it impact our rivals’ strategies?” Preventing injuries can save those millions of euros. The US Sports are—on their end—outstanding at scouting analytics. They were collecting a massive amount of data points on high school and college sports to fuel their predictive models of who will be the next superstar. They use these during their draft process, allowing the worst-ranked teams to select the best young talents in college. This Sports Analytics revolution started with the publication of the book Moneyball, The Art of Winning an Unfair Game in 2003, counting the tell of how Billy Beane—the general manager of the Oakland A’s—built one of the most powerful MLB team after losing three key free agents during the 2001-2002 offseason. The following season the A’s won 103 games, having the best record in league with the New York Yankees. The most impressive fact is that not only Billy Beane had impressive results after such a difficult offseason, but also he managed to accomplish these only with the 28th payroll in the league (!) with $39.6 million that year—4 times less than the New York Yankees with $126 million. The A’s managed to build a winning team out of underestimated and affordable players, thanks to their revolutionary use of data analytics and statistics. Nowadays, this model has been replicated in numerous sports, with the most recent example being, how FC Midtjylland reached the Champions’ League group stage thanks to their data-driven approach.
I believe than being data-driven is not always the best option. The difference between being data-driven and being data-informed might be subtle, but it can help you avoid catastrophes
The second aspect of Data Analytics within the Sports industry is what we call “Business Analytics”. Using data to improve the business performance of a sports organisation. This is very much so the focus of organisations whose revenues are not being impacted directly by sports results. For instance, leagues such as the NBA, Premier League or F1 are not directly incentivised by who is winning their championship. Therefore, they have little interest in investing in Sports Analytics. Their focus is on how to best use Data Analytics to improve decision-making within the organisation. From Ticketing analytics, Digital Analytics, CRM, Sponsorship, TV Rights, etc. Not every entity is specialised in all these areas, though. For instance, MLB franchises having 81 games at home per season are more incentivised to get better at ticketing pricing than NFL teams who only have eight games at home per season.
2. What are some of the advantages of the current technological evolution?
Technologies are now cheaper than ever with the likes of AWS and their fast and cheap computational power. Moreover, it is easier now to find some external agencies and talents being able to work efficiently and timely than it was in the past. What might have been too difficult in the past–training a custom convolutional neural network trained to identify and label sponsors in video footage for instance–is now more accessible than it ever was.
3. What according to you are some of the challenges plaguing the Data Analytics landscape, and how can they be effectively mitigated?
I believe that there are lots of misconceptions in the general opinion around Data Analytics. These can be very detrimental. For instance, I don’t believe data to be the “new oil”. Yes, data needs to be transformed to be usable—just like oil—but unlike oil which is fossil energy, data is truly unlimited. Unlike oil, data doesn’t have a global standard price. And finally, data doesn’t have any viable substitute, unless you want to run your business on gut instinct (hint: you don’t).
Nevertheless, what’s for sure is that data is at the core of its own industrial revolution, just like oil or electricity a few centuries ago. And in the same way, we had Chief Electricity Officers at the time to “figure out what this electricity stuff was about”, Chief Data Officers are now all the rage, but it’s still unclear as to what exactly their role is about until we finally master this new resource which seems so hard to grasp, as we did with oil and electricity through the last century. One of the other common misconceptions is that hiring data scientists will solve all your problems, and will ensure your company a bright future. But what’s more important than having data scientists is to have analytics translators within your company. Translators are people who understand analytics without having the deep technical expertise in programming or modelling that data scientists have. They are very close to the core business; they understand and can translate the impact of analytics to key decision-makers in the company. Finally, I believe than being data-driven is not always the best option. The difference between being data-driven and being data-informed might be subtle, but it can help you avoid catastrophes. With the former, data is at the centre of the decision-making process, while in the latter, data is treated as an independent piece of information like any other. Being data-driven can be a losing strategy in a few cases when the data quality may be questionable, when your data may not be representative of what you’re trying to forecast, or merely because of human error—which might be the most dangerous case scenario.
4. What are some of the best practices businesses should adopt today to steer ahead of competitors?
I really like Dan Ariely’s comment about big data: “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, and everyone thinks everyone else is doing it, so everyone claims they are doing it…” Despite being seven years old now, Dan Ariely’s comment couldn’t be more on point. In fact, Gartner released a compelling number recently: 80 percent of analytics insights will not deliver business outcomes through 2022. What’s more surprising is that the gap between understanding the importance of analytics and effectively applying it has never been more important, with a Harvard Business Review survey finding that “86 percent of organisations find that the ability to extract new value and insights from existing data/analytics applications is very important, but with only 30 percent being very effective at doing so.” The moral being, if you want to implement a successful analytics strategy, focus on what you’re doing, step by step, without paying too much attention to what others say because chances are they are not as successful as they’re claiming to be.