Data in Sports: Past, Present, and Future

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As data analytics is becoming a more prominent part of the sports industry, we wanted to get an experts perspective of the development. Luckily our entrepreneur in residence, Paulo Oppermann, was more than happy to oblige.

Paulo has extensive experience from the world of data analysis as well as its application in the world of sports. Read all about him in our introduction post: Paulo Oppermann: QST Entrepreneur in Residence and Data Analysis Superhero.

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Paulo Oppermann, QST Entrepreneur in Residence

In your time working in this field, how have you experienced data analysis changing the training of athletes?

Data analysis is almost like the stock market. It comes and flows. Sometimes it’s very strong. Everybody wants to do data analysis and jump in with all their hearts and all their efforts and sometimes this phases out and shifts to something different. My biggest remark here would be that data analysis is really depending on the quality of the data analyst. It depends on the person who is behind crunching the numbers.

If they are amazing statisticians with numbers and with projections, that doesn’t mean that they can translate it into sports. Teams sometimes learn how to track heart rates and heart rate reliability, sweat, like how much an athlete is losing body fluids, and mineral acids. It really fluctuates of how much people are interested. If you don’t have an understanding of sports and the reality that the team faces, sometimes a complete data analysis could mean nothing for your performance assessment, performance development and reassessment.

Sometimes the numbers are there. You see in GPS tracking how much the athlete is running and performing. This could mean very little in performance. When you look at the numbers you can see amazing players don’t have the best numbers and then you see poor players who have tremendous numbers. Then you start asking yourself; “what have I done wrong here?”

So in data analysis, you need to start asking the right questions and having a good understanding of what you want to know. Then you need to break it down and really understand what you need to deliver. If you produce graphics for coaches that is amazing looking with a 3D projection of their running performance, it’s something that they can look at and that looks amazing but really doesn’t produce tangible results. It can mean your entire work is downgraded.

Data analysis in sports for performance includes asking the right questions and making the right coordinations. Also, data visualization is of paramount importance. You don’t produce graphics to every problem the same way. You produce graphics with the same results to coaches, different graphics to athletes, and different graphics to directors. Good sports analytics are done in a little bit of artistic way to deliver the best results to the different publics.

The same also applies to marketing plans, how startups can really make it or not make it in sports, and how technology can or can not have an impact on sports. A lot of people look at numbers and see startups that didn’t have a lot of success. This doesn’t mean that their technology was horrible or not applicable to sports. Maybe the marketing strategy was poor. Maybe the sports that they started working with was not good or that the target that they had was far from ideal. It is really a complexe world. If you have a simplistic approach you are definitely gonna fail. There is no question about it.

Have you seen any resistance in integrating data analysis in football training?

For sure, skepticism is something that comes with sports and comes with human nature. Whenever you try to implement something new it’s not only a task of delivering the performance enhancement or the performance analysis. It’s also a task of figuring out how to do so in the most informative way according to the team dynamic, to the team schedule, and to the team culture.

So it’s actually the opposite. I believe that I have never been involved with a team or with an organization where I didn’t find some sort of skepticism or some sort of resistance. When applying sports sciences or new sports technologies there is always resistance. There is always a question mark and those, for good professionals, for good sports scientists, and for good technology are good question marks because it pushes you to perform your best and to deliver the best result possible within the least amount of time. So those are the things that make you work at your best. It is also very satisfying when you have the very strong resistance and you able to get to the point where the team integrates very nicely the science that the professionals involved and everything that you are trying to implement. It is definitely an interesting path to get technology into sports and to get it done in the right way.

What is the holy grail of data analysis?

As the interest fluctuates so does statistical analysis and statistical packages. How you do one thing and how you do another thing, it really varies a lot. What I see as a holy grail in sports analysis is the quality of your sports analysts or your sports statistician. That has been something that has gained the focus for a lot of teams. You see in baseball in the US, for instance, a lot of statisticians are gaining a lot of ground because they are producing amazing results. Some other teams have gigantic sports analytics teams they are not doing so well. So they start losing a little bit of the focus in that area. So when you look at what is the holy grail of data analysis is sports right now it is the human being and a good statistician and a good sports analyst rather than a package, or a graphic delivery system, or a software or anything like that.

But, of course, if you jump in some specific sports you have trends that are coming up or coming down a lot of people talking about machine learning in sports analysis that can produce tremendous projections in data that you don’t have and you would like to understand better. It is incredibly powerful and incredibly good. However, if you don’t use it in the right way you will end up delivering amazing graphics, amazing looking projections while being really off in regard to reflecting reality and that is something that really frustrates a lot of clubs, teams, and people.

In the US there is a big push in the draft. In understanding the young athletes, which athletes to select. Gaining a good understanding of who those can be and how they can perform in professional sports, rather than college sports, is a huge area of focus right now. However, the quality of delivering projections is really something that is lacking. Few teams are doing it in a tremendous way and those professionals become the reference and the real holy grail in sports analysis and performance analysis and realization of sports analysis in different sports.

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