Coach2vec: autoencoding the playing style of soccer coaches
  Capturing the playing style of professional soccer coaches is a complex, and
yet barely explored, task in sports analytics. Nowadays, the availability of
digital data describing every relevant spatio-temporal aspect of soccer
matches, allows for capturing and analyzing the playing style of players,
teams, and coaches in an automatic way. In this paper, we present coach2vec, a
workflow to capture the playing style of professional coaches using match event
streams and artificial intelligence. Coach2vec extracts ball possessions from
each match, clusters them based on their similarity, and reconstructs the
typical ball possessions of coaches. Then, it uses an autoencoder, a type of
artificial neural network, to obtain a concise representation (encoding) of the
playing style of each coach. Our experiments, conducted on soccer-logs
describing the last four seasons of the Italian first division, reveal
interesting similarities between prominent coaches, paving the road to the
simulation of playing styles and the quantitative comparison of professional
coaches.
