Complex network study of Brazilian soccer players

TL;DR

This study applies complex network analysis to Brazilian soccer, revealing statistical properties of players and clubs, and characterizing the network structure and its evolution.

Contribution

It introduces a bipartite network model of players and clubs, analyzes player career statistics, and examines the properties of the derived player network over time.

Findings

Player career length and goals follow specific statistical distributions.

The player network exhibits exponential degree decay.

Network metrics evolve over time and differ from random models.

Abstract

Although being a very popular sport in many countries, soccer has not received much attention from the scientific community. In this paper, we study soccer from a complex network point of view. First, we consider a bipartite network with two kinds of vertices or nodes: the soccer players and the clubs. Real data were gathered from the 32 editions of the Brazilian soccer championship, in a total of $13,411$ soccer players and 127 clubs. We find a lot of interesting and perhaps unsuspected results. The probability that a Brazilian soccer player has worked at $N$ clubs or played $M$ games shows an exponential decay while the probability that he has scored $G$ goals is power law. Now, if two soccer players who have worked at the same club at the same time are connected by an edge, then a new type of network arises (composed exclusively by soccer players nodes). Our analysis shows that for this network the degree distribution decays exponentially. We determine the exact values of the clustering coefficient, the assortativity coefficient and the average shortest path length and compare them with those of the Erd\"os-R\'enyi and configuration model. The time evolution of these quantities are calculated and the corresponding results discussed.