  In the last years, scientific and industrial research has experienced a
growing interest in acquiring large annotated data sets to train artificial
intelligence algorithms for tackling problems in different domains. In this
context, we have observed that even the market for football data has
substantially grown. The analysis of football matches relies on the annotation
of both individual players' and team actions, as well as the athletic
performance of players. Consequently, annotating football events at a
fine-grained level is a very expensive and error-prone task. Most existing
semi-automatic tools for football match annotation rely on cameras and computer
vision. However, those tools fall short in capturing team dynamics, and in
extracting data of players who are not visible in the camera frame. To address
these issues, in this manuscript we present FootApp, an AI-based system for
football match annotation. First, our system relies on an advanced and mixed
user interface that exploits both vocal and touch interaction. Second, the
motor performance of players is captured and processed by applying machine
learning algorithms to data collected from inertial sensors worn by players.
Artificial intelligence techniques are then used to check the consistency of
generated labels, including those regarding the physical activity of players,
to automatically recognize annotation errors. Notably, we implemented a full
prototype of the proposed system, performing experiments to show its
effectiveness in a real-world adoption scenario.
