I strongly believe that 2025 will mark a huge increase in the number of teams using AI to spot trends, analyse their players and utilise the transfer market to the best of their ability.
We are already seeing this in part being used as a training mechanism; Liverpool partnered with DeepMind AI and started using TacticAI. It was a system that offered new suggestions and ideas for tactics when corners were taken and had analysed more than 7,000 corners since 2020. It gives advice to players on their positioning and tries to accentuate corner opportunities. Zone7 is another example, where they use AI to help players maximise their performance levels while avoiding injury. Los Angeles FC were crowned MLS champions in 2022 having used this platform as it maintained the team’s conditioning and reduced injuries per game 17%. It used the data to predict patterns with injuries and data that reveal when players are getting injured and what is causing these injuries. Napoli in Serie A now use this.
We have seen AI be in operation to help with VAR and offsides, its enhanced ball tracking methods proving vital to its existence.
However, the true benefits of AI are being utilised now in player recruitment and monitoring, which is what the crux of this article will focus on.
Evidence of these trends can be demonstrated with the hiring of AI scientist Laurie Shaw at the City Football Group, (parent to Man City), Shaw specialised in data analysis at Harvard. Previous footballers such as Jens Melvang are moving into data analytics, he is now at Stats Perform (parent to Opta) who translate the more technical aspects of the data they produce for coaches at football clubs. The goal here is to harness AI to identify talent. The data collecting insights that are made about decision making and performance of footballers are proving to be incredible essential – Real Analytics and Stats Perform are at the forefront of providing a holistic evaluation of footballers including their off the ball conduct.
How specifically is the AI being used?
Event data records on the ball actions, whereas tracking data is recording the positions of the players and out of possession work, and AI can now capture this from a TV broadcast, where computer vision identifies and tracks the players, even predicting their location when out of the picture. This generates complex data to give coaches true insight.
A Premier League club could use this to evaluate incoming transfer targets to see how they would fit into their team. They will analyse how players respond to different situations and make decisions in game. Now the development of chatbots such as Open AI's ChatGPT and Google Bard might have potential application in the field of data analysis in football transfers.
Soccerment, a data analytic company, is beginning to integrate these language models into their product, making it accessible enough for someone without expert knowledge, to search their database. The platform enables the user to search for the most like-for-like replacement for another footballer. For instance, if Arsenal were to lose Bukayo Saka, this could help them identify which targets are most similar; the algorithm generated that Raphinha is most similar – thus streamlining transfer issues and player monitoring.
It is plainly evident that AI will continue to gain momentum in its operation within football, and what I have highlighted above is only a short insight into the way that AI is taking a hold of some key aspects of the game.