![](bg2.png)
mands statistical methods.
In this article, we investigate the properties of the
NFL social network, a network with players and te-
ams as nodes and labor relationships among them as
edges. Additionally, we propose to predict the success
of teams by modeling the behavior of players and te-
ams in the NFL social network. Experiments show
that the number of quarterbacks with significant im-
pact in the NFL history and in their teams is negligi-
ble if we exclusively rely on game statistics, such as
passer rating. In addition, we show that the NFL so-
cial network is scale-free, i.e., a very small number
of quarterbacks present extraordinary performance,
while a large number of quarterbacks perform poorly.
Moreover, we show that the NFL social network fol-
lows a small-world behavior, where the distance bet-
ween any two nodes are very small. Particularly, the
key contributions of this article are:
• We investigate the properties on the NFL social
network, showing that it can be characterized as a
scale-free and small-world network.
• We propose a method to predict the success of
NFL teams based on the network properties and
metrics.
• We thoroughly evaluate the network metrics used
by our method by contrasting them with usual
passer rating statistic. We show that our metrics
outperform this quarterback performance statistic
to predict the success of NFL teams.
The remainder of this article is organized as fol-
lows: Section 2 reviews the related literature on
complex networks. Section 3 presents related work.
Section 4 show that the usual passer rating statistic
plays a significant role in only a small fraction of the
NFL players. Section 5 presents the properties of the
NFL social network, including the method we pro-
pose to predict team success. Section 6 shows ex-
perimental results, attesting the effectiveness of our
method and metrics to predict team success, when
compared with passer rating. Finally, Section 7 pro-
vides a summary of the contributions and the conclu-
sions made throughout the other sections, presenting
directions for future research.
2 BACKGROUND
Complex networks are huge sets of interconnected
items with a structure that do not follow a regular pat-
tern. For instance, the Internet is a complex network
composed by millions of interconnected routers, fol-
lowing a pattern in which a small number of items are
extremely highly-connected, and the great majority
of items have very few connections (Faloutsos et al.,
1999). They usually are represented as graphs, with
items as nodes (or vertices), and the connections bet-
ween the nodes as edges (or links).
Particularly, a complex network models a real-
world problem with nodes and edges storing infor-
mation on the problem (Wasserman and Faust, 1994).
In multi-modal networks, the information are in the
nodes, while in multidimensional or multi-relational
networks, the information are in the edges. We can
also classify complex networks by their application
in real-world problems (Newman, 2010). Biologi-
cal networks represent biological systems, e.g., neu-
ral, protein, vascular and metabolic pathways net-
works. Information networks represent information
and knowledge systems where nodes are informa-
tion items, such as research articles, documents, and
Web pages. Citation networks and the Web are ex-
amples of information networks. Social networks re-
present relationships between people or groups, such
as friendships, family and professional relationships.
Usually, social networks present a small-world beha-
vior, where no one is far from anyone (Watts and Stro-
gatz, 1998). Technological networks represent man-
made systems, usually built for efficiently distribution
of resources, e.g., electrical grid, telephony, water dis-
tribution and the Internet (Newman, 2003).
Figures 1, 2, and 3, present three different kind of
complex networks. Particularly, they differ according
to how the connections between nodes are built (Costa
et al., 2007): randomly or non-randomly. Random
networks are built from a graph with n nodes, where e
edges are randomly drawn between the nodes (Erd
¨
os
and R
´
enyi, 1959), so that all nodes have the same pro-
bability of receiving new connections. In random net-
works, the more connections one add to the graph, the
greater the chance of a cluster to occur.
Figure 1: Example of a random network.
Scale-free networks are built from a graph with
n nodes, where e edges are not randomly drawn bet-
ween the nodes (Albert and Barab
´
asi, 2002), so that
the more connections a node has, the greater the
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
136