  One of the main shortcomings of event data in football, which has been
extensively used for analytics in the recent years, is that it still requires
manual collection, thus limiting its availability to a reduced number of
tournaments. In this work, we propose a deterministic decision tree-based
algorithm to automatically extract football events using tracking data, which
consists of two steps: (1) a possession step that evaluates which player was in
possession of the ball at each frame in the tracking data, as well as the
distinct player configurations during the time intervals where the ball is not
in play to inform set piece detection; (2) an event detection step that
combines the changes in ball possession computed in the first step with the
laws of football to determine in-game events and set pieces. The automatically
generated events are benchmarked against manually annotated events and we show
that in most event categories the proposed methodology achieves $+90\%$
detection rate across different tournaments and tracking data providers.
Finally, we demonstrate how the contextual information offered by tracking data
can be leveraged to increase the granularity of auto-detected events, and
exhibit how the proposed framework may be used to conduct a myriad of data
analyses in football.
