Interview with Artus Rupalla, product management team lead and AI expert at TeamViewer
Hey Artus, thanks for talking to us about the exciting applications of artificial intelligence (AI) in the world of sports!
1. You’re quite the athlete yourself. You told us you love to cycle and play tennis.
What are your favorite AI applications in cycling and tennis?
In tennis, the first thing that comes to mind is the Hawk-Eye system. This system uses multiple cameras to track the ball and collect data to calculate the most likely ball trajectory. It then visualizes the path of the ball to help referees make decisions in close calls. The Hawk-Eye system is also being used to convert traditional tennis courts into interactive, AI-enhanced environments. There are advanced systems that employ high-speed cameras and AI algorithms to analyze stroke speed data and match statistics. In some cases, systems like this even function as automated referees.
It’s exciting to see how consumer apps have made AI technology more accessible to amateur athletes. In cycling, for instance, AI has revolutionized training for both professionals and amateurs. AI-based training programs can assess and adapt to the unique needs of each cyclist, enabing a high level of personalization. This has made pro-level coaching accessible to a wider range of cyclists.
2. As sports become increasingly data-driven, coaching teams have enlisted AI tools to enhance performance analytics. For instance, AI can assess player movements, shot success rates, and other key statistics.
How can AI improve the effectiveness of analyzing performance data?
AI is already widely used in performance analysis. We’re just not always aware of it. What stands out here are the predictive capabilities of AI. They allow teams to make forecasts based on historical and real-time data. This is particularly evident in sports like cycling, where AI is used to predict race outcomes by analyzing rider statistics against race conditions. In tennis and basketball, AI can track player movements and shot success rates as the game unfolds. This helps coaches make informed, in-the-moment tactical decisions. In team sports, AI-generated visualizations of player movements help coaches define the tactical formation of the team and develop strategic plays. Pattern recognition is also transforming sports like tennis, where understanding an opponent’s playing style and adapting strategies accordingly can make the difference between winning and losing.
Also, personalized training and recovery plans created by AI are revolutionizing the way athletes prepare for a match. AI can monitor and analyze athletes’ health to optimize training schedules, improve performance, and even reduce their risk of being injured. This enables them to train smarter, not just harder, which leads to sustained high performance.
3. In sports like football and basketball, AI algorithms can assist coaches in recruiting new players, deciding which players to substitute on the field, and determining the best tactical formation for the team.
What are your thoughts on AI taking over decision-making in sports?
The use of AI for sports decision-making is a complex topic. The strength of AI lies in its ability to analyze data quickly and impartially. AI can evaluate performance without the biases that might influence human scouts, which results in more efficient and objective player scouting. AI also contributes to fairer decision-making, enhancing the integrity of sports, especially when it comes to adjudicating rules and analyzing gameplay.
That being said, sports are more than just numbers and data. They involve human elements like morale, team chemistry, and leadership, which AI can’t fully comprehend. While AI can suggest strategies based on data, it lacks the capacity to understand human emotions and the dynamics that play out on the field.
Therefore, I believe that while AI brings valuable insights and efficiency to sports strategy, it cannot replace the nuanced judgment and emotional intelligence of human coaches and managers. So, there needs to be an approach where human skill and intuition are complemented by the analytical strengths of AI.