In the last two decades, artificial intelligence (AI) has transformed the way in which we consume and analyse sports. 

 The role of AI in improving decision-making and forecasting in sports, amongst many other advantages, is rapidly expanding and gaining more attention in both the academic sector and the industry. Nonetheless, for many sports audiences, professionals and policy makers, who are not particularly au courant or experts in AI, the connexion between artificial intelligence and sports remains fuzzy.

using in-game play statistics came under focus as a means to assemble an exceptional team.

who gather the in-game data and analyse it to answer questions that will lead to improving team performance.

 The ongoing and exponential increase of computer processing power has further accelerated the ability to analyse “big data,” and indeed, computers increasingly are taking charge of the deeper analysis of data sets, through means of artificial intelligence (AI).

The wide applicability of machine learning algorithms, combined with increasing computing processing power as well as access to more and new sources of data in recent years, has made sports organisations hungry for new applications and strategies. The overriding aim is still to make them more competitive on and off the field–in athletic and business performance. The benefits of leveraging the power of AI can, in that regard, take different forms from optimising business or technical decision making to enhancing athlete/team performance but also increasing demand for attendance at sporting events, as well as promoting alternative entertainment formats of the sport.

Artificial Intelligence refers to technology that emulates human tasks, often using machine learning as the method to learn from data how to emulate these tasks.


Perform AI Innovation Centre have worked on a wide range of different types of data to make predictions on a number of different sports, from football to field hockey, volleyball to swimming using different types of data. There are 3 main types of sports data available: box scores, event data and tracking data. All these types of data facilitate the reconstruction of the story of a match or a particular performance. However, the more granular the temporal and spacial data of a game is, the better the story an analyst can tell.


Box-Score Statistics
The use of high-level box-score statistics (half-time match score, full-time match score, goal scorers, time of goals, yellow cards, etc.) can summarise a 90-minute match of football to provide an idea on how the game was played in just a few seconds. Basic box-score statistics can tell you who won the match, which team took the lead first, when were the goals scored and how close together to each other. Box-score statistics provide a fairly good snapshot of a game and a decent level of match reconstruction.

Box-score statistics also offer a more detailed level of information. For example, they can illustrate which team had more shots and the quality of those shots by showing the number of shots and shots on goal. They can also explain the distribution of possession between the teams in the match, which team had more corners, committed more fouls, made more saves and so on. Within a few second they can capture the story of the match, which team dominated or how close was that game.


Tracking Data
Tracking data is currently the most detailed level of data being captured in sports. It enables the projection of the location of all players and the ball into a diagram of the pitch that best reconstructs a match from the raw video footage of that match. Having a digital representation through tracking data of all players on the entire pitch enables analysts to perform better querying than simply using a video feed that only displays a subsection of the pitch.