Abstract
In recent years, excessive monetization of football and professionalism among the players have been argued to have affected the quality of the match in different ways. On the one hand, playing football has become a high-income profession and the players are highly motivated; on the other hand, stronger teams have higher incomes and therefore afford better players leading to an even stronger appearance in tournaments that can make the game more imbalanced and hence predictable. To quantify and document this observation, in this work, we take a minimalist network science approach to measure the predictability of football over 26 years in major European leagues. We show that over time, the games in major leagues have indeed become more predictable. We provide further support for this observation by showing that inequality between teams has increased and the home-field advantage has been vanishing ubiquitously. We do not include any direct analysis on the effects of monetization on football’s predictability or therefore, lack of excitement; however, we propose several hypotheses which could be tested in future analyses.
Highlights
We use a network science approach to quantify the predictability of football in a simple and robust way without the need for an extensive dataset and by calculating the measures in 26 years of 11 major European leagues we examine if predictability of football has changed over time
After having assessed the models’ validity and robustness, here we address if the predictability of football has been changing over time
We see the area under the curve (AUC) scores and smoothed fits to them per league over time, for the last 26 years
Summary
Most of the past research in this area, either focuses on inter-team interactions and modelling player behaviour rather than league tournament’s results prediction, or are limited in scope—they rarely take an historical approach in order to study the game as an evolving phenomenon [24,25,26]. This is understandable in light of the fact that most of these methods are data-thirsty and not easy to use in an historical context where extensive datasets are unavailable for games played in the past. We use a network science approach to quantify the predictability of football in a simple and robust way without the need for an extensive dataset and by calculating the measures in 26 years of 11 major European leagues we examine if predictability of football has changed over time
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