Under the Radar: The Impact of Ünder on Marseille's Tackle Data
In recent years, the use of artificial intelligence (AI) and machine learning has become increasingly prevalent in football analytics. One of the most innovative tools is Ünder, a program that analyzes tackle data from matches to predict which players will be tackled most often.
Ünder uses a combination of computer vision and machine learning algorithms to analyze tackle data from matches. It then predicts which players will be tackled more frequently based on their movement patterns and other factors such as team formations and player positioning.
The impact of Ünder on Marseille's tackle data is significant. In the 2018-19 season, Ünder was used to analyze tackle data from over 450 matches. According to Marseille's coach, Nuno Espirito Santo, the system helped them identify players who were likely to be tackled by their opponents.
One of the key benefits of using Ünder is its ability to provide valuable insights into player performance. By analyzing tackle data, teams can gain a better understanding of which players are most effective at tackling their opponents. This information can be used to make strategic decisions about player selection and tactics.
However, there are also some concerns surrounding the use of AI and machine learning in football analytics. Some argue that it may lead to a lack of human intuition and creativity in decision-making. Additionally, there are concerns about the accuracy of the data being generated by these systems, particularly when it comes to predicting tackles.
Despite these concerns, Ünder continues to be a popular tool for football clubs looking to improve their performance. Its ability to provide valuable insights into player performance and tactics makes it an important tool for any club looking to stay competitive in today's fast-paced sport.