Glocal Explanations of Expected Goal Models in Soccer

In soccer, it is not uncommon for one team to dominate a match, creating many chances to score but failing to do so, while the opposing team manages to convert one of their few chances into a goal and win the match. Thus, the use of traditional end-of-match statistics is often argued against, because the number of shots, ball possession percentage, and shots inside the opponent's penalty area do not always accurately reflect the outcome of the match. The rapid pace of technological advancements in data collection, storage, and analysis have had a revolutionary impact on soccer analytics over the last decade. Thanks to these advancements, soccer data is collected in two main forms: event data consists of ball-related events and where on the field they occurred such as shots, passes, tackles, and dribbles while tracking data consists of the position of players and the ball throughout play on the pitch. The technological revolution has made it possible to propose a large number of key performance indicators to measure different aspects of the game, such as pass evaluation, quantification of controlled space, shot evaluation, and goal-scoring opportunities using possession values.