Dr Baihua Li, the project lead, says the technology could lead to major changes in the sport as it will enable clubs to effectively identify and quickly recruit talented players.
Current player performance analysis is a labour-intensive process and involves someone watching video recordings of matches and manually logging individual player’s actions – this involves recording how many passes and shots were taken by a player, where the action took place, and whether it had a successful result.
Not only is this method incredibly time-consuming, it also presents issues of accuracy, consistency, and comparability as it relies on human judgment and a lack of bias.
Some automated technologies are on the market already, but they are only able to track players on the pitch – to determine distance covered and speed – but they cannot provide detailed information on the actions taken by players.
To tackle this problem, Dr Li and her team aimed to develop a hybrid system where human data entry can be accelerated and supplemented by camera-based automated methods to meet the high demand for low-cost timely performance data generated from large amounts of football videos.
Funded by Innovate UK and in collaboration Statmetrix (a company that specialises in football performance data insights), the researchers have used the latest advances in computer vision, deep learning, and AI to achieve three main outcomes. They are: