We see how the world of sport is evolving by leaps and bounds with technology, although at first glance it may not seem so to many fans. Although data analysis has long been a core component of decision-making in sport., The advent of AI on the playing field has given it an even greater impetus and exploits all the possible conclusions we can draw from the data.
So, in this Euroinnova article we will see what applications AI has in sport today and what we can expect from this technology in the near future. Among its benefits and innovations we find predictive models, optimisation of training routines, more intelligent scouting, among others that we will explain below.
Predicting results and analysing performance
We often hear about predictive AI in fields such as digital marketing or finance and investments, but there are also many other sectors where it can find a very useful application. This is the case in sport.
From the data modelling, we can create models of predictive analytics to anticipate possible injuries or periods of underperformance depending on the player and the circumstances of the match being played. To do this, artificial intelligence engineers can make use of the performance data (physical conditions, injuries, fouls, and any relevant data) in previous matches to train the predictive model.
With all this data, coaches can get a better idea of which players will perform better in which games, and can therefore develop better strategies.
Discovering new talents
Coaches have always relied on their own experience and hunches to pick other talented players and sign them to contracts. And this is not a bad thing; the human factor is still very much needed, but they now have the help of AI in sport to make decisions from a more objective perspective.
If we have a historical record of all players' performance and physical skills, coaches can select new players more judiciously, and even discover new talents whose abilities may have gone unnoticed.
Optimised training plans
The AI can propose strength and endurance training plans for each player according to his physical condition, current performance and previous training routines.
Moreover, generative AI is of great use in this respect, as, with the right prompts, it can generate a sea of ideas to configure very diverse routines adapted to each player. And just as it generates various routines, it can also suggest to us personalised diets that complement them.
Injury prevention and safety
As mentioned, we can feed an AI data model for a sport with data such as injuries. But this could be used not only to evaluate the performance of individual players, but also to predicting when and how an injury is likely to occur and avoid it as far as possible.
For example, if we input graphical data on player injuries, the AI could detect which movements and under what circumstances injuries tend to occur in a sport, as well as their severity. In this way, we can prevent potential injuries, correct bad habits in the way players play, and discern whether an injury will have more or less serious consequences.
Advertisements in sport
Although this use of AI is not unique to sport, it is very important. Thanks to AI and Big Data, It is easier for us to segment audiences into distinct audiences based on certain criteria, such as demographic variables. At the same time, with AI we can identify which moments of a match generate the most excitement, and which are of less interest to users.
In this way, we can personalise the ads shown on the screens throughout the match as much as possible and project them at key moments in order to engage sports audiences.
The future of artificial intelligence in sport
As in other sectors, AI is expected to continue to advance and penetrate more strongly into sports., The new technology will bring about decisive transformations in game strategies and sports data analysis. Thanks to artificial intelligence, sports teams will be able to boost their physical performance and gain a competitive edge. It will also make sport a safer professional activity by preventing potential injuries.
The next developments we expect from AI in sport is a macro analysis of an entire team and its joint game dynamics, beyond the analysis of individual player data. In addition, the potential offered by AI in combination with the mixed reality, This could be harnessed to create much more immersive experiences for spectators and in the world of sports video games.
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