Probing the Quality of Football Leagues Through Player’s Foot Laterality: A Data Analytics Approach

Abstract

The ability to use both sides of feet in association football, or so-called soccer in some countries, is highly valued, and therefore professional athletes rigorously train their non-dominant foot to perform effectively on the pitch. However, a subject that relates that players’ laterality aspect to the overall quality of the league of a given country has not yet been widely explored. This paper discusses the subject through a data analytics approach, leveraging the abundance of publicly available football datasets to describe interesting phenomena through data visualizations. In the present study, the foot laterality measure is represented by the percentage of left-footed scored goals and league’s overall quality by the market values of players. Results show that in general, leagues with higher market values have higher rates of left-footed goals. The findings in this work agree with those in past research conducted with other methods, hence confirming the validity of data analytics based on the crowdsourced football data. This research intends to emphasize the potential of data science in sport sector and motivate the football professionals to use big data to fine-tune their physical training methods and sport psychology approaches.