Behara et al. Social Network Analysis of NFL Coaches DECISION SCIENCES INSTITUTE Exploring Leadership in Services: A Social Network Analysis of NFL Coaches (Full Paper Submission) Ravi S. Behara Florida Atlantic University [email protected] Preston J. Huang Spanish River Community High School [email protected] C. Derrick Huang Florida Atlantic University [email protected] ABSTRACT This study examines coaching leadership in the NFL from a social network perspective. Specifically, the role of coaches is explored based on team coaching staff networks across all NFL teams. Utilizing coaching staff data from all NFL teams for the period of 2000-2013, we find the eigenvector centrality as a useful measure to identify influential coaches. In addition, contrary to the general belief of the importance of degree centrality, we find that this network measure is more of an indicator of “journeyman” than “winning” coach. We further explore the networks of successful coaches and identify different “schools” of coaching. KEYWORDS: Social network analysis, Centrality measures, Leadership, National Football League, Coaching INTRODUCTION The National Football League (NFL) is the largest professional sports league in the U.S., generating annual revenues of more than $9 billion. The value of a NFL team ranges from just below $1 billion to over $2.3 billion (http://www.forbes.com/nfl-valuations/list/). Its total operating profits and franchise valuation of the NFL with 111 million fans are twice as much as those of the MLB, the next largest North American sports league (Quinn, 2012). Based on revenues, the NFL would make the Fortune 300 list: it is half the size of the pharmaceutical giant Bristol-Myers Squibb and about a tenth the size of Citi Group, Microsoft, or Boeing. Its profits are about the same as New York Life Insurance. - 1 - Behara et al. Social Network Analysis of NFL Coaches Criteria Value Year/Season Source Estimated Size of the Entire Sports Industry, U.S. $ 470 B 2013 PRE NFL League Revenue $ 8.8 B 2011/12 Forbes Overall Operating Profit $ 1.3 B 2011/12 Forbes Number of NFL Teams 32 Teams 2011/12 NFL Average NFL Game Attendance (16 Game 67,579 2011/12 ESPN Season) Spectators Average NFL Team Value $1.11 B 2012 Forbes Table 1: Overview of the National Football League (source: hwww.plunkettresearch.com) In addition to players, coaches play a critical role in the performance of an NFL team. In this study, we analyze the social networks of NFL coaches, based on a data set containing all coaching staff of all NFL teams from 2000-2013, focusing on the following three aspects: The characteristics of the coach network: Based on the overall network measures, can we identify the coach network with any of the theoretical networks? Centrality of coaches: Does any of the centrality measures prove useful in predicting the “influential” coaches? Comparison of long-tenured coaches: When we compared the networks of individual long-tenured coaches, can we identify characteristics that make them successful? We begin by reviewing the pertinent literature of social network analysis and its applications. We then briefly discuss the data set used in this study and develop the characteristics of the NFL coach network. Then various centrality measures are calculated and their implications discussed, followed by the development and comparison of the networks of three long serving coaches. Finally, we conclude with the implications of this study and directions for future research. LITERATURE REVIEW Network analysis A graphical representation of even a moderately sized social network can quickly become incomprehensible with many lines in the network diagram. Few, if any, useful conclusions can be drawn from scanning this type of dense network diagram showing relationships between coaching staff and NFL teams (as in this study). Such networks are better understood using a matrix and suitable network measures. The coach-team data can be represented by a rectangular i x j matrix, with i coaches and j teams. In this matrix a 1 represents an association of a specific coaching staff with a specific team, and a 0 represents no relationship. From this, we can derive a square i x i matrix of coaches, wherein a 1 represents a working relationship among coaches. The most important characteristic of social network data is the existence of relations among the entities (Wasserman and Faust, 1994). This has implications for the unit of modeling and analysis. For example, the network modeling unit can be constructed as the individual entity or node (e.g., a coach), pair of nodes or dyads, or the entire network of nodes. Social network analysis measures can be used to identify “important” or “prominent” nodes in the network. The importance or prominence of nodes is usually defined as a measure of their location in the - 2 - Behara et al. Social Network Analysis of NFL Coaches network. Measures used for this study include total degree (also referred to as degree), closeness, betweenness, and eigenvector centrality. Empirical research shows network centrality as affecting the node’s influence (Fombrun, 1983; Brass, 1984; Ronchetto et al., 1989). Measures of total degree centrality and betweenness centrality are extensively used in assessing the prominence of nodes in networks (Freeman, 1979; Faust, 1997). Individuals or organizations with high betweenness centrality are potentially influential, positioned to broker connections between groups, bear the influence of one group on another, and/or serve as a gatekeeper between groups to control access and information flow. Career success and influence of individuals in organizations have been shown to tie to the structure of that individual’s social networks (Brass, 1995; Ibarra, 1993; Lin and Huang, 2005). Specifically, Burt (1992; 1997) finds that individuals who have social networks with more structural holes are promoted faster and obtain higher levels of pay. In a complete network, such as the one we have in the NFL coach network, Brass (1984) finds that network centrality is associated with power. Wade et al. (2011) suggest that central players in inter-organizational networks will be more likely to be promoted across organizations. There have been a few attempts made to analyze the NFL coaching network. Fast and Jensen (2006) investigate the NFL coaching network by considering it as a complex adaptive system, and show mentoring as being impactful on the success of a coach. Wade et al. (2011) find that coaches with higher centrality are much less likely to leave the NFL and more likely to be promoted internally or externally. Vaz de Melo et al. (2012) have attempted to predict the success of teams based on team network dynamics, but apply it to the NBA and MLB. But overall, while these are some early efforts at investigating social networks in professional sports leagues, there are significant areas that have been left unaddressed. We address this gap by systematically investigating the NFL coach social network in this study, both at an entire network level as well as from the perspective of a specific node (coach). This is discussed in the remainder of this paper. MODEL DEVELOPMENT Data Collection and Analysis Methodology The first step in data collection for network analysis is to define the boundary and sampling. Laumann et al. (1989) define two distinct approaches to boundary specification in social network studies: the realist approach and the nominalist approach. The realist approach is one in which the boundary and membership is as perceived by the network agents themselves, while the nominalist approach is based on the theoretical concerns of the researchers. We adopt the realist approach, since the NFL is a well-defined professional league, and the coaching staff of the 32 teams makes up the group of interest. However, we also partially adopt a nominalist approach in the sampling of the NFL seasons of interest. We selected 2000-2013 as the years of interest to include the new entrants to the league. We collected the coaching data of all NFL teams from 2000 to 2013. During this period, one team—the Houston Texans—was added in the 2002 season. All key coaching positions—head coach, assistant coach, offensive coordinator, defensive coordinator, etc.—are included according to teams’ reports; however, only head coach position is identified. The affiliation of the coaches with the corresponding teams cover the playing season; any coaching staff changes (hiring and firing) during the season are not reflected in the data. This is not - 3 - Behara et al. Social Network Analysis of NFL Coaches considered a significant issue, as such changes are infrequent. The data are obtained via publically available sources such as teams’ websites and http://coachingroots.com/. After the data set is downloaded, we cleaned and organized the data using Excel to produce a .csv file of bi-nodal relationships of coach and team-year. The resulting sample size is 6,172. To analyze the data, we use Gephi, an open-source graph visualization tool, and ORA, a dynamic meta-network assessment and analysis tool from the Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon University, USA (Carley et al., 2013), to conduct network analysis. The results from Gephi and ORA are presented below. Characteristics of NFL Coach Network We first examine the overall network measures. The coach network from 2000 to 2013 contains 877 nodes, each representing an individual coach. An edge is formed when two coaches worked on the same team in the same year, and there are a total of 2,161 edges in the network. This node-edge combination gives the network density of 0.006 and average degree of 2.5. In addition to these simple statistics, other key measures help characterize a network. The average path length between any two nodes is 3.7, while the network diameter—the longest of all the calculated shortest paths in a network—is 6.