This book explores the application of data mining and machine learning techniques in studying
the activity pattern decision-making skills misconducts and actions resulting in the
intervention of VAR in European soccer leagues referees. The game of soccer at the elite level
is characterised by intense competitions a high level of intensity technical and tactical
skills coupled with a long duration of play. Referees are required to officiate the game and
deliver correct and indisputable decisions throughout the duration of play. The increase in the
spatial and temporal task demands of the game necessitates that the referees must respond and
cope with the physiological and psychological loads inherent in the game. The referees are also
required to deliver an accurate decision and uphold the rules and regulations of the game
during a match. These demands and attributes make the work of referees highly complex. The
increasing pace and complexity of the game resulted in the introduction of the Video Assistant
Referee (VAR) to assist and improve the decision-making of on-field referees. Despite the
integration of VAR into the current refereeing system the performances of the referees are yet
to be error-free. Machine learning coupled with data mining techniques has shown to be vital in
providing insights from a large dataset which could be used to draw important inferences that
can aid decision-making for diagnostics purposes and overall performance improvement.A total of
6232 matches from 5 consecutive seasons officiated across the English Premier League Spanish
LaLiga Italian Serie A as well as the German Bundesliga was studied. It is envisioned that the
findings in this book could be useful in recognising the activity pattern of top-class referees
that is non-trivial for the stakeholders in devising strategies to further enhance the
performances of referees as well as empower talent identification experts with pertinent
information for mapping out future high-performance referees.
