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.
