Coaches, managers and scouts have had their day. A supercomputer spits out the names of the perfect starting XI, the most promising substitutions and the best transfers. Artificial intelligence (AI) makes it possible.
Is this what professional football looks like in the not too distant future? “I’m sure it won’t come to that – and as a football fan, I hope so too,” says Tim Schröder. As the main product developer responsible for the online platform Plaier, which wants to revolutionize player scouting using AI, Schröder also emphasizes: “The future cannot be stopped.” And the future will also be increasingly determined by AI in sports.
“This will also come in Europe, in Germany – and the early users will have an advantage,” said Schröder of the German Press Agency. There is no need to worry that high technology will have a negative impact on the core of sports competitions and make people in responsible positions superfluous. “People need other people as reference partners,” says Schröder, “technology is just a tool.” And used as such, AI could “make the sport better in terms of quality”.
The Institute for Applied Training Science (IAT) in Leipzig sees it similarly, where AI is now used primarily in biomechanics. “In our area, AI is not a risk, but an opportunity to generate more data in a shorter time,” explains Björn Mäurer, scientific IAT sports informatics employee. In the IAT, the movement of the athlete on the ski jump or in the discus ring is filmed, the recordings are then evaluated with software and the athlete is accompanied by AI-supported recording systems. In an international comparison, Mäurer said they are “in a good position in this regard, but China should be further along”.
AI in a smaller form has long been part of everyday sports
But that’s just a guess, nobody in the scene likes to share their level of knowledge. That is why the question of what AI can already do in sports is not easy to answer. “Artificial intelligence can help to structure data, show abnormalities and reduce the amount of data so that people can deal with it better,” answers sports scientist Carlo Dindorf from the Technical University of Kaiserslautern-Landau.
In a smaller form, something like this has long been part of everyday sport, “fitness trackers” with information on stress and fatigue reactions are standard. At the German cycling team Bora-hansgrohe, six people are already working on the daily processing of the vast amounts of data on wattage, heart rate and much more. In Formula 1, data analysis has been influencing the racing strategy for many years, and the use of AI via software companies is also being intensified here.
In professional football, the Plaier company wants to take player scouting to a new level using specially developed AI. A real-time analysis of the game system and squad is used. The results are combined with data on over 100,000 players registered in the system and weighted according to their skills in relation to the searching club. With this systematic approach, the AI learns from historical data and forecasts. Co-founder Jan Wendt promises customers a 90 percent probability of success in transfers: “We’re not saying: That could be. We’re saying: That’s the way it is and will look like this in the next six years.”
Coaches are still very important
The German Ski Association (DSV) is still in the early stages of using AI, but initial tests, for example when selecting waxes, were “very promising,” said Karlheinz Waibel, DSV national science and technology trainer, of the Rheinische Post. At the 2022 Olympics in Beijing, it was found out faster than with conventional ski tests which condition of the skis best suited the cross-country ski trail.
Waibel’s conclusion is: “The added value is great.” But you have to want to afford it. “If we want to work more intensively with AI, we also need state funds that have not yet been approved,” said the DSV man. Saving money for trainers instead is not a solution: “The trainer is still very important. After all, data doesn’t explain everything. Knowledge, experience and, last but not least, soft skills are just as important as AI in order to advance the athlete.”
Mäurer from the IAT also warns of an “increased risk” if decisions are currently transferred to the AI. If, for example, the computer were to be given the complete evaluation of the technique in sports such as apparatus gymnastics, “the software could certainly be manipulated – and not in a sporting sense, but technologically”.
In sports, the data sets are still relatively small, but that will change with the increasing use of AI. It is data hungry – and the more it is fed, the greater the output. Austria’s national soccer coach Ralf Rangnick, who has always seen the bigger picture, therefore believes in “undreamt-of possibilities” when used in sports: “The more AI develops, the more data volume, the more information there is, the more you can do get out of there.”
