Automatic discovery of tactics in spatio-temporal soccer match data
Automatic discovery of tactics in spatio-temporal soccer match data Decroos et al., KDD’18
Here’s a fun paper to end the week. Data collection from sporting events is now widespread. This fuels an endless thirst for team and player statistics. In terms of football (which shall refer to the game of soccer throughout this write-up) that leads to metrics such as completed passes, distance covered, intercepts, shots-on-goal, and so on. Decroos et al. want to go one level deeper though, and use the data to uncover team tactics. The state of the art today for tactical analysis still involves watching hours of video footage.
This paper focuses on the problem of detecting tactics from professional soccer matches based on spatio-temporal data.
Now when I think of tactics, a key component in my mind is the team shape and movement of players off the ball. Unfortunately Decroos et al., don’t have the data available to analyse that. So they have to do what they can based on more limited information.
Our dataset consists of event data for the English Premier League for the 2015/2016 season. This event data was manually collected by humans who watch video feeds of the matches Continue reading
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