  It is challenging to get access to datasets related to the physical
performance of soccer players. The teams consider such information highly
confidential, especially if it covers in-game performance.Hence, most of the
analysis and evaluation of the players' performance do not contain much
information on the physical aspect of the game, creating a blindspot in
performance analysis. We propose a novel method to solve this issue by deriving
movement characteristics of soccer players. We use event-based datasets from
data provider companies covering 50+ soccer leagues allowing us to analyze the
movement profiles of potentially tens of thousands of players without any major
investment. Our methodology does not require expensive, dedicated player
tracking system deployed in the stadium. We also compute the similarity of the
players based on their movement characteristics and as such identify potential
candidates who may be able to replace a given player. Finally, we quantify the
uniqueness and consistency of players in terms of their in-game movements. Our
study is the first of its kind that focuses on the movements of soccer players
at scale, while it derives novel, actionable insights for the soccer industry
from event-based datasets.
